Abstract

In recent years, with the increasing demand for intelligent society, intelligent photonics has developed rapidly. Machine learning (ML), as a subset of artificial intelligence (AI), has played an important role in the intelligent evolution of optical fiber sensors. Its impact extends beyond enhancing sensor performance by introducing innovative problem-solving approaches. Specifically, ML algorithms have become instrumental in signal demodulation and elevating the efficacy of discrete and distributed sensors, and have also greatly promoted the development of optical fiber speckle pattern processing. This paper presents the latest advancements in ML-based optical fiber sensors, outlines the problems faced by conventional demodulation methods and the common ML algorithms applied in optical fiber sensors, and emphasizes key applications. Additionally, this paper delves into the challenges and future development of this emerging research direction.

Introduction

Due to advancements in powerful computing tools, hardware, widespread use of cloud data, and the evolution of the Internet of Things, the accessibility of large datasets has significantly improved. This has promoted the emergence of many efficient machine learning (ML) algorithms. These algorithms, aided by continuous advancements in data analysis technology, have demonstrated effectiveness in training models and solving specific problems. If ML algorithms are complex and deep enough in terms of layers, then ML can become so-called deep learning (DL) [1]. Regardless of the name, ML seems to have a wide range of applications in modern society, because the achievements of artificial intelligence (AI) often exceed those achieved by manual control design.

In 2021, Nature Photonics further advanced its exploration of the hot topic of optics/photonics through a focused issue called “Machine learning of Light”. As an important type of microphysics, optics seems to have similarities in complexity with some of the “black box” processes in ML. In the Q&A session, David Pille and Aydogan Ozcan discussed the two primary drivers behind the current application of ML in photonics [2]. One direction is to use AI methods on hardware to design optical structures and devices with specific performance. Another direction is to use optical systems as fully optical/hybrid statistical inference models to achieve AI calculations.

Optics and photonics applied to ML computing offer unique advantages, allowing for ultrafast calculations at extreme frame rates and low energy consumption [3], [4], [5], [6]. For instance, Feldmann et al. [7] proposed a fully optical neural synaptic system. They use wavelength division multiplexing technology to implement a scalable circuit architecture for photonic neural networks (NNs) and use optical pulse signals to regulate the phase change materials that encapsulate neurons. This phase change material can regulate transmittance and reflectivity through a controllable laser. This system can perform both supervised and unsupervised learning. The work successfully demonstrates direct pattern recognition in the full optical system. This photonic NN has the potential to leverage the inherent high-speed and high bandwidth of optical systems, thereby enabling the direct processing of full optical communication and visual data. However, logic gates, computation, and NNs are all linear (passive) systems that cannot address nonlinear (active) issues. The lack of effective nonlinear optical processes as activation functions for many nodes poses challenges for all-optical implementation [8].

Conclusion and future perspectives

This paper reviews the application of the ML algorithm in optical fiber sensors. In recent years, the advent of ML has greatly impacted optical development. Compared with static demodulation algorithms and complex spatial optical paths, the ML algorithm offers better signal processing mechanisms for optics. ML algorithms enable automated processing, analysis, and recognition of spectrums, thereby enhancing the efficiency and accuracy of spectrum analysis. Using ML algorithms to classify and identify the spectrum of different substances, enabling qualitative and quantitative analysis of substances. By learning from known corresponding spectrums and building models, ML algorithms can predict and analyze unknown spectrums. ML algorithms can extract and recognize features from the spectrum, analyze the content and proportion of various components, and reveal the intrinsic nature of substances. With the continuous development of spectroscopy and ML algorithms, the application prospects of ML in spectroscopy will become increasingly widespread. This advancement will contribute to more efficient and accurate analytical methods in chemical analysis, environmental monitoring, food safety, and other fields, promoting progress in scientific research and expanding application fields.

Abstract

Most of the companies’ sustainability and growth depend on how well its employees perform. However, the measurement of employees’ performance until now is inconclusive and inexhaustive. To accurately assess and predict an employee’s performance, numerous external factors (physical/environmental, social, and economic) related to an employee’s life have been taken into account in this work. The purpose of this research is to explore an unbiased AI algorithmic solution to predict future employee performance considering physical, social, and economic environmental factors that affect employee performance. We collected data of 1109 employees from the ‘For-Profit Organization’ in Bangladesh from both employers and employees to cover all the factors that justified the unbiased outcome. We utilized a few machine learning tools in this study including the Logistic Regression classifier, the Gaussian Naive Bayes, the Decision Tree classifier, the K-Nearest Neighbors (K-NN), the SVM classification, etc., in order to predict the employee performance evaluation. Then, we compared the effectiveness of those machine learning models by analyzing their precisions, recall, F1-score, and accuracy. This work can be utilized to obtain bias-free employee performance reviews. This fair employee performance assessment can aid decision-makers in making moral choices regarding employee promotions, career advancement, and training needs, among other things. The study also describes notes for future researchers.

Introduction

Performance evaluation, which includes assessing current performance, identifying good and poor performers, and providing feedback to staff, is one of the most challenging components of Human Resource Management (HRM) (Cherian et al., 2021, Fogoroș et al., 2020, Stone et al., 2015). Employee performance evaluations are not practiced systematically by numerous organizations. As a result, the evaluation method becomes erratic and ineffective. A systematic approach should be adopted in order to evaluate employees at the planning stage on a regular basis (Ahmed et al., 2013). Employees with these attributes—skills, dedication, attitudes, and knowledge—are valued as assets by the company (Al-Tit et al., 2022, Li et al., 2008, Yang and Lin, 2009). By creating new knowledge at firms’ level, an organization’s human resources can the firms to innovate (Terán-Bustamante et al., 2021). The accurate assessment of the employees’ performance contributes to the mission of the company with the maximum satisfaction of the employees (Pap et al., 2022). Since company progress depends on employee advancement, numerous executives search for efficient ways to improve performance drastically (Abbas and Yaqoob, 2009, Salam, 2021). To boost performance, employers first need to know the performance condition of the employee in any organization. An article in the Harvard Business Review (Antonio, 2018) claims that essential functions of the organizations, such as prediction, upselling, cross-selling, and performance management can be remarkably influenced by AI technologies (Ledro et al., 2023). In the future, businesses, communities, and nations will be significantly impacted by big data, automation, and machine learning (Lada et al., 2023, Tao et al., 2023). In this regard, AI has started to play a role in business, particularly in HRM, concerning the prediction and decision-making (Nilashi et al., 2023, Qureshi et al., 2023).

Conclusion

The performance of the company’s personnel determines a large portion of its sustainability and growth. Many external aspects (physical/environmental, social, and economic) relevant to an employee’s life have been included in this work in order to measure and anticipate an employee’s performance effectively. The goal of this project is to create an AI algorithmic-based ethical decision-making framework that takes into account the various environmental factors—physical, social, and economic – that have an impact on worker performance. Our results were impartial since we gathered information from a few “For-Profit Organizations” in Bangladesh, both objectively and subjectively. In this study, we used a variety of machine learning methods, and we were able to acquire a reasonable accuracy score. The Random Forest model obtained the highest accuracy score, and the Gaussian naïve Bayes model had the lowest. An impartial employee performance review can be obtained by using this work. Decision-makers can use this equitable employee performance evaluation to help them make moral decisions about training requirements, career advancement, and employee promotions, among other things.

Abstract

Although artificial intelligence can contribute to decision-making processes, many industry players lag behind pioneering companies in utilizing artificial intelligence-driven technologies, which is a significant problem. Explainable artificial intelligence can be a viable solution to mitigate this problem. This paper proposes a research model to address how explainable artificial intelligence can impact decision-making processes. Using an experimental design, empirical data is collected to test the research model. This paper is one of the pioneer papers providing empirical evidence about the impact of explainable artificial intelligence on supply chain decision-making processes. We propose a serial mediation path, which includes transparency and agile decision-making. Findings reveal that explainable artificial intelligence enhances transparency, thereby significantly contributing to agile decision-making for improving cyber resilience during supply chain cyberattacks. Moreover, we conduct a post hoc analysis using text analysis to explore the themes present in tweets discussing explainable artificial intelligence in decision support systems. The results indicate a predominantly positive attitude towards explainable artificial intelligence within these systems. Furthermore, the text analysis reveals two main themes that emphasize the importance of transparency, explainability, and interpretability in explainable artificial intelligence.

Introduction

Automotive industry leaders, such as Tesla, have made substantial investments in artificial intelligence (AI) to expedite the introduction of self-driving vehicles to the market, enhancing their competitive capabilities. The integration of AI in supply chain operations has played a crucial role in enabling Tesla to optimize its operational costs [64] while simultaneously facilitating the establishment of a Gigafactory in China [78]. We are witnessing a rapid digital transformation driven by the integration of AI in supply chain management. The COVID-19 pandemic forced companies and organizations to expedite the digitalization of their operations [6]. To enhance their competitive edge, prominent companies, including Amazon, Walmart, Alibaba, Siemens, and Toyota have embraced AI-based technologies to automate and digitalize their operations and supply chain activities [1,32]. Digital transformation also introduces new possibilities for potential cyberattacks. However, using AI-based technologies for decision-making during cyberattacks (e.g., American Express monitoring; [75]) offers significant advantages that outweigh the potential losses incurred.

Although AI-based technologies can contribute to decision-making processes in operations and supply chain management, many industry players are lagging behind pioneering companies in utilizing AI-driven technologies, which is a main problem. Therefore, the adoption of AI-based technologies in decision-making processes, particularly during sensitive situations such as cyberattacks, may encounter potential barriers that delay their usage. Due to its vital advantages, AI has received much attention from decision-makers to address resilient case studies [45] and other sensitive problems such as healthcare [69]. The lack of explanations of the underlying AI processes leads to the rejection of AI in decision support systems [60]. Leveraging AI-powered decision-making platforms can significantly facilitate and expedite the decision-making process, which results in improved overall performance. For instance, the Colonial Pipeline, a U.S. oil supplier, faced a cyberattack and, after a week of deliberation, opted to pay around $4.4 million to solve the issue [21]. A quick decision on the first day through agile decision-making could have saved them money and enabled the uninterrupted continuation of their operations with stockholders.

Results

We employed regression analysis to test the hypothesized relationship between research model variables. We conducted required tests relating to the experimental design along with method assumptions and bias checks.

4.1. Design check
To check the design, the scenario test, the realism test, the validity test, and the manipulation test were performed. We conducted the scenario check to measure the participants’ learning process regarding the provided information. The results showed that participants’ learning was strong enough to support the scenario design (in the first scenario, the Mean = 5.3 out of 7, with a Standard Deviation = 0.99, and in the second scenario, the Mean = 5.5 out of 7, with a Standard Deviation = 0.85). The realism check was conducted through a face validity process and tested using the quantitative approach proposed by Thomas et al. [65]. Participants supported the realism check by agreeing that the scenarios were close enough to reality (Mean = 4.08 out of 5; Standard Deviation = 0.44). Similarly, the validity check supported the scenario design (Mean = 4.07 out of 5; Standard Deviation = 0.43). The manipulation test supported the manipulation by showing a significant difference between the two manipulated levels (p-value <0.001, the first level, the low level of XAI: Mean = 2.68 out of 7; Standard Deviation = 0.12; and the second level, the high level of XAI: Mean = 5.9 out of 7; Standard Deviation = 0.10).

Abstract

This study aims to investigate the influence of blockchain and artificial intelligence on the audit quality of firms from Turkey. Primary data from 300 respondents are collected through random sampling to attain the study’s objectives. PLS-SEM is used to investigate the relationship between exogenous and endogenous variables. Our findings show that blockchain technologies and artificial intelligence (AI) utilization in their financial system positively impact audit quality by assisting in the audit process and the detection of fraud, which also improves financial reporting. Blockchain and Artificial Intelligence in the financial system create confidence for investors, stakeholders, and legislators. Moreover, this study advocated significant implications for investors, government, firms, and policymakers. Investors can make investment decisions based on the accuracy of the financial accounts; the government and policymakers can improve the governance mechanism by using the study’s findings.

Introduction

Audits play a vital role in ensuring compliance with laws, regulations, and accounting standards. Companies are subject to various regulatory requirements and must adhere to accounting principles and reporting frameworks [1]. High-quality audits provide independent assurance that these compliance requirements are met, reducing the risk of penalties, legal consequences, or reputational damage [2]. Quality audits contribute to effective corporate governance by holding management accountable and independently assessing the company’s financial reporting [3]. By examining internal controls and risk management processes, audits help identify weaknesses and areas for improvement, enhancing organizational transparency and accountability. Audits protect the interests of various stakeholders, including employees, customers, suppliers, and lenders [4]. By assuring the accuracy and reliability of financial statements, audits help safeguard the investments, jobs, and contractual relationships associated with a company. This promotes trust and stability in the business environment.

Auditing quality is an interesting research problem and is influenced by many factors such as the blockchain, Artificial Intelligence, professional skepticism, auditor independence, audit firm size, regulatory environment, technological advancements, and the adoption of innovative audit tools. Professional skepticism plays a pivotal role in audit quality [5]. Auditor independence, ensuring auditors maintain an unbiased stance, is fundamental for objective and reliable audit outcomes [6]. The audit firm size has been identified as a factor, with larger firms often having more resources and expertise to deliver higher-quality audits [7]. The regulatory environment and oversight mechanisms significantly impact audit quality, as stringent regulations and effective oversight contribute to a more robust auditing process [8]. Technological advancements and the adoption of innovative audit tools have emerged as crucial factors shaping audit quality in the contemporary business landscape [9]. Understanding the intricate interplay of these factors is essential for advancing research and enhancing the overall quality and effectiveness of auditing practices. Therefore, these factors are widely discussed in the previous literature in different demographic contexts but lack the investigation on Artificial Intelligence (AI) and blockchain technology in relation to audit quality in the modern world.

Conclusion and recommendations

Our findings show that AI improves audit quality by providing assistance and timely fraud detection. AI’s data analysis capabilities, ability to identify complex fraud patterns, and real-time monitoring contribute to more effective and efficient audits. As AI advances, it holds tremendous potential to enhance auditing practices’ overall quality and effectiveness. This study provides specific insights into the implications of these technologies within Turkey and also other varieties of implications for investors, management, stakeholders, and policymakers. The study assessed the level of adoption and readiness of Turkish auditing firms to embrace blockchain and AI technologies. It identified the factors influencing the adoption process, including organizational culture, infrastructure, resources, and regulatory environment. Understanding the current state of technology adoption in Turkey sheds light on the potential implications for audit quality.

The implications of blockchain and AI on fraud detection and risk assessment practices in Turkish audits can be explored. The study assessed how these technologies improve the ability to detect and prevent fraud, including identifying unusual patterns, real-time monitoring of transactions, and using predictive analytics. It can also examine the impact of risk assessment methodologies and the ability to assess risks accurately. The investor can invest by seeing the audit quality of the firms within Turkey to protect their investment, and the government can make the legislation in the weakness and delayed audit without enjoying the blockchain technologies and the other factors. Future research should be conducted on the efficiency of blockchain technology that is effective in the accounting system and preparation of the financial system that makes it easier for the technologies to improve the audit quality. Some more Blockchain units should be included in further research. Due to the limitation, we included one economy in our investigation; future research should include more than one and compare each economy, the use, and the efficiency of the artificial intelligence system.

Abstract

The research on emotional intelligence in social robots is growing. This paper provides a thematic analysis of the studies on robots and emotional intelligence, synthesising and evaluating current knowledge and research topics. In addition, based on the thematic analysis of the studies, it also provides a conceptual framework explaining the emotional intelligence of robots that includes both actors (human and robot) in a human-robot interaction setting. The findings are based on the analysis of 252 studies published until the end of 2022 and indexed in the Scopus database. The results unveiled two main themes (robot design-technical developments and characteristics and human-robot interaction), including sub-themes and topics that emerged in the literature. Finally, the themes and sub-themes were evaluated through a critical discussion to develop a conceptual framework for robots and emotional intelligence.

Introduction

1.1. Rationale
A robot is a “programmed actuated mechanism with a degree of autonomy to perform locomotion, manipulation or positioning” [1]. While robots are widely used in manufacturing [2,3] for producing goods, they find their way into agriculture [4] and the delivery of services [[5], [6], [7]] as well. Social robots help children with autism [8], assist the elderly [9], and redefine the meaning we put in sexual relationships [10].

One of the main directions of robotics research is on human-robot interaction (HRI) [11]. The topic has been researched in the context of industrial robots [12], service robots [13], and social robots [14]. A key component of HRI in the context of social robots are the emotions of humans and the emotional intelligence of both parties in the relationship [11,15] because emotions trigger specific affective, cognitive, and behavioural responses [16]. For example, when humans experience positive emotions in their interactions with robots, they are more likely to use robots and less likely to object to them [17]. Additionally, humans’ perceptions of the emotional skills of robots are positively related to the perceived appropriateness of robot use in service contexts, e.g. museums in galleries, as found by Webster and Ivanov [18].

To elicit respective emotional reactions in humans, robots need to have some degree of emotional intelligence. Emotional intelligence (EI) is defined as “the capacity to reason about emotions, and of emotions to enhance thinking. It includes the abilities to accurately perceive emotions, to access and generate emotions so as to assist thought, to understand emotions and emotional knowledge, and to reflectively regulate emotions so as to promote emotional and intellectual growth” ([19]:197). EI has four aspects measured through the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT) ([19]:200, [20], [21]:281–282).

Conclusion

This paper provided a thematic analysis of the studies on robots and emotional intelligence. The results uncovered two main: (i) robot design-technical developments and characteristics, and (ii) human-robot interaction. The robot design-technical developments and characteristics include sensory aspects (facial expression and voice-related), cognitive aspects and behavioural aspects, visual aspects, and other aspects. The “human-robot interaction” category comprises two main sub-themes, namely settings (health, education, and other settings) and attitudes (perceptions toward robots).

Abstract

Abstract
The rapid expansion of mobile broadband networks and the proliferation of Internet of Things (IoT) applications have substantially increased data transmission and processing demands. However, the application domains of IoT-enabled models often face resource limitations, requiring rapid responses, low latency, and large bandwidth, surpassing their inherent capabilities. To address these challenges, we propose a fishnet approach-based packet scheduling and resource allocation system, termed Fishnet-6G, to optimize network resource allocation in the proposed 6G networks. Initially, we constructed a Sierpinski Triangle-based network in a 6G-IoT environment, enhancing device connectivity. We utilize the Quantum Density Peak Clustering (QDPC) algorithm to perform clustering for IoT devices, establishing Cluster Head (CH) and Substitute CH (SUB CH) based on actual metrics. Furthermore, traffic prediction is achieved through two processes, grouping, and fair queue status, using the Improved Deep Deterministic Policy Gradient (IMPDDPG) algorithm with a variable sampling rate, resulting in well-organized packet scheduling. Subsequently, we perform optimal packet scheduling by employing the Willow Catkin Optimization (WCO) algorithm, and the scheduled packets are managed within a Fishing Net Topology to reduce energy consumption and system complexity. Finally, we allocate the scheduled packets to the desired resource blocks using the Bayesian Game-Theoretic Approach (BGTA). The proposed approach is implemented using Network Simulator-3.26, and the performance of the Fishnet-6G model is evaluated based on time, transmission rate, energy efficiency, average throughput, latency, and Packet loss rate. Numerical analysis demonstrates that Fishnet-6G outperforms existing approaches across these metrics, showcasing its effectiveness in addressing the challenges of 6G-IoT networks.

Introduction

The rapid expansion of IoT devices has led to notable progress in data generation, contributing significantly to the growth of linked objects inside the IoT systems. It is essential to highlight that this development is experiencing a sustained increase as many objects and devices are interconnected in IoT applications [1,2]. As 5G networks continue to improve and expand, concerns arise about their capacity to support the constantly evolving needs of emerging IoT applications fully [3]. The impending era of sixth-generation (6G) mobile communication is marked by the widespread adoption of IoT devices and cellular networks, contributing to a substantial increase in energy consumption and network traffic [4]. The ever-growing demands of intelligent and self-sufficient systems challenge their capabilities and spark the evolution towards the 6G-IoT vision [5]. By 2025, it is projected that there will be over 25 billion IoT devices, putting immense pressure on current multiple access techniques and necessitating the development of B5G wireless systems. In light of this uncertainty, attention has turned towards the potential of 6G wireless technologies to overcome these limitations and propel the transformation of existing networks [6]. Envisioned as a vital component in the development of sustainable smart cities, the 6G-IoT vision incorporates cutting-edge elements such as intelligent edge computing, health analytics, and multidimensional design technology, with a firm emphasis on critical features such as scalability, wireless multi-access, personalized AI, machine learning, cyber security, blockchain, and augmented sensing [3,[6], [7], [8], [9]]. The strategic placement of small cells in a 6G system optimizes coverage and data transmission efficiency, while sensor nodes collect essential information communicated to IoT devices, enabling diverse applications like home automation and healthcare, showcasing the network’s sophistication [10,11]. Network architecture and administration must mitigate complexities in the 6G network to provide exemplary performance. The construction of a 6G network presents numerous advantages, encompassing enhanced capacity, extensive coverage, cost-effectiveness, load balancing, and energy efficiency [12].

Conclusion

This study proposes a fishnet technique to overcome the significant issues related to packet scheduling and resource allocation in the context of the 6G-IoT environment. The primary objective of this study is to implement the Sierpinski triangle-based network construction in a 6G-IoT environment to mitigate the issues of excessive energy consumption and communication overhead. The edge server utilizes QDPC to provide clustering for IoT devices. The suggested study aims to group IoT devices based on relevant metrics. This clustering process involves the identification of cluster heads and sub-cluster heads, which are executed by an edge server. Subsequently, the process of traffic prediction is conducted through two distinct stages, namely grouping and fair queue status assessment, employing the IMPDDPG algorithm with variable sampling rate. Packet scheduling is executed by considering the relevant metrics and employing the WCO algorithm. Following the establishment of a schedule, the management of packets is conducted inside the fishing net architecture, resulting in a reduction in energy consumption, complexity, and overall process weight. The allocation of scheduled packets to the requested resource blocks is accomplished using BGTA, while taking into consideration the operational indicators. The proposed approach was tested using Network Simulator-3.26, and its effectiveness was assessed by comparing its performance to existing methods taking into account various permanence metrics, including time, transmission rate, energy efficiency, average throughput, latency, and packet loss rate. The efficacy of our methodology is examined by quantitative analysis, which substantiates that our technique surpasses existing methodologies across all metrics. The future work will consider cases high availability in cases of Base Station failures and massive IoT V2X environment to support the next generation of wireless networks.

Abstract

The popularity of cloud computing, with its incredible scalability and accessibility, has already welcomed a new era of innovation. Consumers who subscribe to a cloud-based service and use the associated pay-as-you-go features have unlimited access to the applications mentioned above and technologies. In addition to lowering prices, this notion also increased the reliability and accessibility of the offerings. One of the most crucial aspects of cloud technology is the on-demand viewing of personal services, which is also one of its most significant advantages. Apps that are cloud-based are available on demand from anywhere in the world at a reduced cost. Although it causes its users pain with safety concerns, cloud computing can thrive because of its fantastic instantaneous services. There are various violations, but they all accomplish something similar, taking the systems offline. Distributed denial of service attacks are among the most harmful forms of online assault. For fast and accurate DDoS (Distributed Denial of Service, distributed denial of service) attack detection. This research introduced the DDOS attack and a method to defend against it, making the system more resistant to such attacks. In this scenario, numerous hosts are used to carrying out a distributed denial of service assault against cloud-based web pages, sending possibly millions or even trillions of packets. It uses an OS like ParrotSec to pave the way for the attack and make it possible. In the last phase, the most effective algorithms, such as Naive Bayes and Random Forest, are used for detection and mitigation. Another major topic was studying the many cyber attacks that can be launched against cloud computing.

Introduction

DDos attack is a distributed type of attack mode in which an attacker controls a large number of attack machines and sends out DoS attack instructions to the machine. In the latest Internet security report, DDoS attacks remain one of the major cybersecurity threats. The inexpensive pricing and “pay-as-you-go” focused accessibility to computational features and amenities on demand make cloud-based services a formidable competitor to the conventional IT solutions available in prior eras. The use of cloud computing is gaining popularity rapidly. Whether entirely or largely governments and companies have moved their IT infrastructures onto the cloud. Cloud-based Infrastructure offers various advantages compared to traditional, on-site conventional infrastructures. The removal of expenses associated with operation and impairment, as well as the accessibility of materials on request, are only a few of the advantages. However, there are many concerns that cloud consumers have, and the research addresses these issues. The majority of these inquiries centre on safeguarding operational concepts and information. Many security-related attacks can be prevented in conventional IT systems that do not use cloud computing. Focused cloud-based crimes are already using their innovations. Many security vulnerabilities in cloud computing are unique compared to their predecessors in non-cloud computing environments because data and business logic are stored on an external cloud server that lacks accessible oversight. The denial-of-service (DoS) assault is one technique that has been in the spotlight recently. Denial-of-service incidents are directed at the server rather than the people it supports. DoS attackers attempt to flood live servers by masquerading genuine users to overload the service’s capacity to handle incoming inquiries [1]. Cloud computing is an Internet-based service that enables users to access configurable computing resource sharing pools (including server, storage, application software, services, networks, etc.) to achieve online access to computing resources on demand. As a mixture of emerging technologies and business models, cloud computing has developed rapidly in recent years due to its advantages of super-large scale, virtualization, high reliability, good scalability and on-demand services. To overcome this issue, multiple inquiries are sent to the server simultaneously. The term “distributed denial of service,” or DDoS, refers to a variation on the classic “denial of service” that uses numerous computers to attack and impair one service at a time simultaneously.

Conclusion

The key goals of this study are to learn how to recognize and prevent attacks involving distributed denial-of-service. The first and most crucial step is determining which ports can be exploited. Nevertheless, this approach is not risk-free because susceptible ports are more likely to be exploited. Given ParrotSec’s track record for stability and performance, we decided it would be the ideal choice for our company’s computer system. Since a DDoS attack involves sending one million separate packets toward the target, starting with an on-the-internet website would be best. The targeted website was taken offline after it became clear that an assault had happened. Machine learning is constructive in this detecting process as well. Using this data, the most popular and accessible tool, “weka,” is being trained. Employing pre-processing techniques and the “discretize” filter to achieve the desired effect. Therefore, the following phase is not only quite intriguing but also rather useful for both forecasting and detecting. We employed both methods and compared the findings on the same platform, and we found that the naive Bayes method provides the most trustworthy conclusions. PCA selected 21 features from the possible 42 features, while LVQ selected only 20 features. The results suggest that LVQ based feature selection in the DT model may be more accurate than other methods in identifying attacks. As mentioned earlier, the model also outperformed the previous models in terms of accuracy, recall, specificity, and f-score. It was shown that the naive Bayes model had significantly better predictive power than the random forest model. There is a chance that a false positive rate warning will be triggered for packet transmissions within a network. Moreover, when compared to the random forest, naive Bayes produces considerably more accurate forecasts. It was demonstrated that the Naive Bayes algorithm outperformed the random forest technique to identify the false and actual rate of transmissions. The result detection is not carried out in real time. Although attacks can be detected, real-time alarm cannot be realized in the environment of high cluster security, so the feasibility of real-time monitoring under Hadoop platform should be studied continuously.

Abstract

Deep learning techniques have shown significant impact in enhancing security across various domains by leveraging artificial neural networks models. When applied to cloud computing security, deep learning offers cost-effective solutions by automating threat detection, reducing manual monitoring, and improving overall security effectiveness. Deep learning models using neural networks play crucial role in tasks like intrusion detection, malware detection, anomaly detection, and log analysis. Integration of deep learning into cloud security requires careful evaluation of existing systems, defining objectives, dataset selection and preparation, model tuning, and eventual modifications for compatibility. Furthermore, implementing deep learning techniques in cloud security entails considering factors such as computational resources, data collection and preparation costs, model development, integration efforts, and ongoing monitoring and maintenance. This paper proposes a feed-forward propagation Artificial Neural Network (ANN) model in cloud security and investigates the key steps for integrating such models into cloud security strategies. Considering that the effectiveness of the ANN model depends on factors such as training data quality, network architecture, and weight adjustment algorithms, the study utilizes a dataset from Kaggle.com for validation and demonstrates steps involved in training and evaluation of the ANN model.

Introduction

Deep learning (DL) techniques have emerged to become effective tools for enhancing security in various domains. These techniques leverage the capabilities of artificial neural networks to learn and identify patterns from vast amounts of data, enabling more robust and efficient security solutions [1][2]. When applied to cloud computing security, deep learning techniques offer several benefits having direct implications in the cost-effectiveness of the products. A great benefit of implementation of DL in security of cloud is seen in automated threat detection. DL algorithms can analyze large volumes of data, such as network traffic logs, system logs, and user behavior, to detect anomalies and potential security threats automatically [2], [3]. This together with other utilizations as a result of DL, reduces the need for manual monitoring and analysis, what leads to faster identification of security incidents, timely response, mitigation, and reduced costs in time and resources. Considering this, through automation of various security tasks, DL minimizes the risk of human error, which can lead to security breaches and associated costs.

Automated systems powered by deep learning can perform tasks consistently and accurately, enhancing overall security effectiveness [4], [5]. DL models learn patterns and relationships in data, allowing for more accurate threat detection and classification. Through detecting patterns and anomalies which could go unnoticed by traditional rulebased systems, DL implementation makes it possible for organizations to optimize resource allocation and minimize unnecessary costs.

Conclusion

The integration of deep learning techniques into cloud computing security offers numerous benefits. By analyzing large volumes of data, deep learning algorithms can detect security threats in real-time and minimize various risks. However, the implementation of deep learning in cloud security requires careful evaluation of costs and resources. Organizations struggle when having to adjust the solution to the computational resources, data acquisition and preparation costs, personnel expertise, and ongoing monitoring and maintenance.

The integration of deep learning techniques in cloud security involves few steps for a successful outcome. These include assessing the strengths and weaknesses of the existing system, identifying specific tasks that deep learning can address, selecting suitable models and algorithms, training the models with labeled data, and ensuring compatibility and integration with existing components. Continuous monitoring and feedback are also crucial stages for maintaining the efficiency and effectiveness of the integrated system.

Abstract

Face recognition systems that do not implement liveness detection are susceptible to face spoofing attacks. This vulnerability implies that an attacker could disguise themselves as another individual and the system would falsely take the attendance of that other individual. To prevent these attacks, a liveness detection step can be implemented before recognizing subjects. Face recognition-based attendance system devices are typically installed at the entrance to an event or space, so having a portable device that can be easily relocated is practical and efficient. Hence, face recognition systems should be lightweight enough to be able to run on portable devices with limited computational power. Implementing liveness detection will increase the system’s processing time. Therefore, this study aims to develop a lightweight liveness detection method that can be run on a Raspberry Pi. To achieve this, several pre-trained models were evaluated and MobileNetV2 was chosen based on the results. The MobileNetV2 model was then trained using transfer learning method. The proposed attendance system achieved an average processing time below 0.6 s and 96 % accuracy for live subjects, 79 % accuracy for level A spoof attacks, 83.7 % accuracy for level B spoof attacks, and 70 % accuracy for level C spoof attacks.

Introduction

There are two types of attendance systems, manual attendance systems, and automated attendance systems [1]. With manual attendance system, the attendance of each individual present in an event is recorded manually by someone in charge. In an automated attendance system, the tedious process of manually recording everyone’s attendance is replaced with an automated system. One way to achieve this is by using an authentication method to verify and record individuals’ attendance. Fingerprint, palm veins, face, and iris recognition are often used due to their false rejection rate and higher acceptance rate [2]. Face recognition is preferred over other biometric authentication methods because of its inherent benefits such as non-intrusive interaction and accessibility [1].

Biometric authentication methods are robust and convenient because it verifies the identity of the subject by using their physiological and/or behavioral characteristics and not the subject’s knowledge or possession. Password-based authentication is based on knowledge, which implies that if someone knows the knowledge, they can authenticate as someone else. Ownership-based authentication is based on item possession, which means that someone who owns the object can authenticate as someone else. While password-based authentication and ownership-based authentication have their own security concerns, biometric authentication such as face recognition also has its own security concerns.

Face recognition, one of the biometric identification techniques, uses an analysis of an individual’s distinctive facial features to confirm their identity. In most cases, this involves capturing an image or a video of the person’s face, after which algorithms are used to extract and evaluate particular facial features, such the distance between the eyes, the curves of the nose, and the jawline. To see if there is a match, these features are then put in comparison with a database of recognized faces. Face recognition systems are vulnerable to face spoofing attacks, where the attacker would try to gain illegitimate access by presenting a fake face of an authorized individual [2].

Conclusions

Face recognition systems, especially those that do not implement liveness detection, are vulnerable to face spoofing attacks where attackers would try to gain access by presenting a fake face of another individual. On face recognition-based attendance systems, liveness detection is used to prevent individual attendance that is not really present to be taken. Attendance systems typically run on a portable device as it is more practical and efficient because it could be relocated to any appropriate location as needed. Having a limitation of computing power on portable devices, implementing liveness detection will be a challenge as the liveness detection method that will be implemented must be lightweight so the attendance system could still be run on portable devices. By adding a liveness detection step, the processing time of each face presented to the system will be impacted.

In this study, several pre-trained CNN models were used to perform experiments. The pre-trained models used were pre-trained models trained for face recognition and object recognition. Transfer learning with several variations of new layers was performed on MobileNetV2, FaceNet, and MobileFaceNet. From the results of the experiment, variation C obtained the best result. While MobileFaceNet has a lower average processing time, MobileNetV2 was chosen because the average processing time difference is small, has better accuracy on CelebA-Spoof dataset which has a larger range of variations and has decent accuracy on NUAA dataset.

Abstract

The Internet of Things (IoT) connects devices, enabling real-time data acquisition, automation, and collaboration. Wireless sensor networks are one of the important components of the Internet of Things, consisting of many wireless sensor nodes distributed in space. These nodes can perceive environmental information and transmit it to other nodes through wireless communication. In wireless sensor network routing optimization methods, improved ant colony algorithm can be used to find the optimal routing scheme. Ant colony algorithm simulates the behavior of ants in the process of searching for food, and optimizes factors such as transfer probability and pheromone concentration to enable ants to find the shortest path. In wireless sensor networks, node positions can be used as reference nodes and anchor nodes, combined with the objective function of wireless sensor network routing optimization, and improved ant colony algorithm can be used to solve the optimal path, thus obtaining the optimal wireless sensor network routing optimization scheme. Through experimental results, it can be found that the proposed method performs well in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. These optimization results have positive implications for the sustainable development and practical application of the Internet of Things, which can improve the development of the digital economy and enhance the construction of smart cities.

Introduction

At present, wireless sensor networks have been widely applied in various fields such as environmental monitoring, agriculture, and the Internet of Things. In these applications [1,2], wireless communication between nodes in wireless sensor networks plays a crucial role. The Internet of Things has characteristics such as connectivity, intelligence, and real-time. It connects devices through wireless communication technology and endows them with perception, computing, and decision-making capabilities. This enables the Internet of Things to obtain and transmit data in real-time, automate collaboration and data sharing, improve production and operational efficiency, and reduce costs. At the same time, the Internet of Things utilizes big data analysis and predictive maintenance to provide insight into market trends and user needs, helping enterprises make more accurate decisions. In addition, the application of the Internet of Things in fields such as smart homes and health monitoring is improving people’s quality of life and promoting the process of sustainable development. Therefore, the Internet of Things has significant advantages in improving efficiency, reducing costs, enhancing insight, improving quality of life, and promoting sustainable development. The routing optimization of wireless sensor networks has become an important and challenging issue due to the limited energy of sensor nodes, wide and random distribution of nodes, and other characteristics. The goal of wireless sensor network routing optimization is to improve network performance and energy utilization efficiency by selecting appropriate paths, minimizing energy consumption, maintaining low latency, and maximizing network lifespan [3]. It involves techniques and mechanisms such as path selection and transmission scheduling to ensure efficient and accurate data transmission between nodes. The research on routing optimization in wireless sensor networks is of great significance for improving network performance, extending network lifespan, providing reliable data transmission, and supporting real-time applications. By adopting effective routing optimization algorithms and mechanisms, more reliable, intelligent, and efficient wireless sensor network systems can be achieved. Wireless sensor network routing optimization plays an important role in the Internet of Things, as it can improve network performance and energy utilization efficiency, extend network lifespan, provide reliable data transmission and support real-time applications for various application fields. Therefore, routing optimization in wireless sensor networks is of great significance in promoting the development and application of animal networking [4,5].

Conclusion

Wireless sensor network routing optimization is one of the key issues in the Internet of Things, which is of great significance for improving network performance, extending network lifespan, providing reliable data transmission, and supporting real-time applications. The current routing optimization methods have problems such as high energy consumption, high transmission delay, high number of dead nodes, and low network throughput. To address these issues, this article proposes a wireless sensor network routing optimization method based on improved ant colony algorithm in the Internet of Things. Through experimental testing, it has been proven that the proposed method has absolute advantages over the current method in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. This research achievement can not only improve the energy efficiency of wireless sensor networks, but also improve the quality of data transmission and provide better support for real-time applications. At the same time, this method can also optimize network capacity and meet the network performance requirements of different application scenarios. These contributions provide important support for the reliability, intelligence, and efficiency of wireless sensor networks, and promote the further development of wireless sensor network technology in various application fields. However, there are still some challenges in the research of routing optimization in wireless sensor networks. For example, when optimizing routing in a dynamic environment, it is necessary to consider issues such as changes in network topology and the balance between energy consumption and data transmission quality. These challenges require further in-depth research to propose more comprehensive and adaptable routing optimization methods for wireless sensor networks, and to promote the development and application of animal networking technology.

Abstract

Decision-making in marketing has changed dramatically in the past decade. Companies increasingly use algorithms to generate predictions for marketing decisions, such as which consumers to target with which offers. Such algorithmic decision-making promises to make marketing more intelligent, efficient, consumer-friendly, and, ultimately, more effective. Not surprisingly, machine learning is a trending topic for marketing researchers and practitioners. However, machine learning also introduces important challenges to the marketing landscape. We discuss this development by outlining recent progress and future research directions of machine learning in marketing. Specifically, we provide an overview of typical machine learning applications in marketing and present a guiding framework. We position the articles in the Journal of Business Research’s Special Issue on “Machine Learning in Marketing” within this framework and conclude by putting forward a research agenda to further guide future research in this area.

Introduction

Marketing decision-makers today often struggle to adequately capture and transform (big) customer data into meaningful insights (e.g., De Luca et al., 2021, Sheth and Kellstadt, 2021). Recent research indicates that machine learning (ML)—a field of computer science dedicated to developing learning algorithms, often using big data, to generate predictions needed to make decisions (Agrawal, Gans, & Goldfarb, 2018)—can help companies manage the flood of data (e.g., Davenport et al., 2020, Hagen et al., 2020, Ma and Sun, 2020, Vermeer et al., 2019). ML has been a trending topic in many industries for quite a while now, the marketing industry will be no exception, and it is being used in various industries in the context of both B2C and B2B (e.g., Herhausen et al., 2020, Kumar et al., 2020, Luo et al., 2019, Rust, 2020). ML promises to make marketing more intelligent, efficient, consumer-friendly, and, ultimately, more effective (Huang & Rust, 2021). To put it more directly, proficiency in ML could become an essential skill for numerous marketing researchers and practitioners rather than just a desirable one.

Faced with this development, we aim to highlight recent progress and future research directions on ML in marketing. Such an endeavor is both important and timely, given the dramatic increase in publications on ML in marketing in recent years, as summarized in Fig. 1. Indeed, from 2012 to 2022, the number of publications has increased by almost 600 percent to more than 50,000 yearly publications. Rather than provide an exhaustive literature review, our purpose here is to discuss the most fundamental concepts and topics from past and present research that will drive future research on ML in marketing.1 In doing so, we position the articles of this special issue as an overarching framework that touches upon three key themes, namely employing ML in marketing, benchmarking ML in marketing, and managing ML in marketing.

Conclusion and acknowledgments

We hope this overview article and the papers in this Special Issue serve as an impetus for further research on the important topic of ML in marketing. In addition to summarizing the insights from the published papers, we also discussed important future research directions related to the themes of (1) automated machine learning, (2) data privacy and security, (3) model interpretability, (4) algorithm fairness, and (5) causal machine learning. Selected research questions for ML in marketing derived from these discussions are summarized in Table 2. In addition, broader ML themes might also benefit from a marketing perspective. For example, how can we ensure that ML models are producing ethical and socially responsible models and predictions (De Cremer, 2020; De Cremer & Kasparov, 2022)? Deep learning seems to change how prediction models are developed (Kaur & Sharma, 2023) – what will these techniques’ impact on marketing analytics be in the near future? And, importantly, how will ChatGPT and similar advanced AI technologies change marketing research (van Dis, Bollen, Zuidema, van Rooij, & Bockting, 2023)?

Abstract

Despite providing unparalleled connectivity and convenience, the exponential growth of the Internet of Things (IoT) ecosystem has triggered significant cybersecurity concerns. These concerns stem from various factors, including the heterogeneity of IoT devices, widespread deployment, and inherent computational limitations. Integrating emerging technologies to address these concerns becomes imperative as the dynamic IoT landscape evolves. Machine Learning (ML), a rapidly advancing technology, has shown considerable promise in addressing IoT security issues. It has significantly influenced and advanced research in cyber threat detection. This survey provides a comprehensive overview of current trends, methodologies, and challenges in applying machine learning for cyber threat detection in IoT environments. Specifically, we further perform a comparative analysis of state-of-the-art ML-based Intrusion Detection Systems (IDSs) in the landscape of IoT security. In addition, we shed light on the pressing unresolved issues and challenges within this dynamic field. We provide a future vision with Generative AI and large language models to enhance IoT security. The discussions present an in-depth understanding of different cyber threat detection methods, enhancing the knowledge base of researchers and practitioners alike. This paper is a valuable resource for those keen to delve into the evolving world of cyber threat detection leveraging ML and IoT security.

Introduction

Brendan O’Brien astutely observed, ”If you think the Internet has changed your life, think again. The Internet of Things is about to change it all over again!” [1]. This is indeed the case, as the Internet of Things (IoT) has heralded unprecedented connectivity. The advancements in sensor technology, wireless communication, and data analytics have spurred an exponential increase in connected devices. This influx of connectivity, brought about by integrating IoT into various industries, cities, and households, promotes unmatched efficiency and convenience. As the backbone of IoT, sensors and actuators acquire and convert data from the physical world into digital signals. These compact devices amass a diverse range of data, thereby enabling real-time monitoring and control of numerous systems and processes.

However, the rapid proliferation and extensive integration of IoT devices into everyday life have ushered in various security challenges. These issues must be robustly addressed to ensure the safety and reliability of this expanding ecosystem. The sheer volume and variety of IoT devices and their often inconsistent security features and protocols engender a fragmented environment teeming with potential attack vectors. IoT devices frequently prioritize low cost and user simplicity over security, making them susceptible to breaches and exploitation. As a result, these devices are at risk of various cyber threats, including data breaches, Distributed Denial-of-Service (DDoS) attacks, and malware infections. Any security breach in these devices could significantly compromise privacy and crucial infrastructure systems, given the sensitive nature of the data they handle. Moreover, IoT devices, potentially serving as entry points, might allow attackers to infiltrate broader networks, amplifying the potential impact of security breaches. Another primary concern is the security of communication routes between IoT devices and networks, as many IoT devices utilize wireless communication protocols susceptible to interception or manipulation. These vulnerabilities can be exacerbated by the resource constraints of specific IoT devices, which prevent them from adopting contemporary encryption and authentication techniques. Furthermore, the long lifespan and widespread deployment of IoT devices compound the difficulty of managing security upgrades and patches, as many devices may not receive regular updates or may be difficult to access for maintenance. This could lead to an increased number of outdated or vulnerable devices, further exacerbating security concerns [2].

Conclusion

IoT devices have become fundamental to everyday life, offering increased connectivity and convenience. However, this accelerated development in IoT devices also brought several security challenges that need to be addressed to guarantee the safety and reliability of these interconnected systems. The survey was organized into five sections detailing the role of machine learning in enhancing IoT security. We have provided an overview of the current trends in ML for cyber threat detection in IoT environments. Furthermore, we survey recent cyber detection methods, define them, highlight the approach, and detail the attack surface utilized along with their evaluations. The utilized ML techniques are also discussed, defined, and compared regarding advantages and drawbacks in the relevant use cases. Moreover, open issues were discussed briefly, concluding that further research and development is required as no current solution can address the issues related to IoT cyber threat detection. A holistic strategy combining diverse techniques and strategies is required as a final recommendation. We present this survey as a reference for the current advancements in the field and point out the direction being taken. Our goal is for the survey to create a more secure and resilient IoT environment by identifying the current issues, understanding their root causes and possible implications, and developing innovative solutions.

Abstract

Cloud computing has revolutionized the way businesses and individuals manage and utilize computing resources, providing significant benefits such as scalability, flexibility, and cost-effectiveness. However, the increasing use of cloud computing to store and transmit sensitive data has raised concerns about data security. Ensuring confidentiality and data integrity while enabling effective data transmission is a significant challenge. To overcome this issue, the proposed approach integrates encryption and compression methods to enhance transmission performance in the cloud and prevent unauthorized access to confidential information. This approach applies multiple layers of robust encryption algorithms, followed by LZMA, which aims to compress data size while ensuring data security. The proposed approach is well-suited for real-time implementation and delivers high encryption quality and compression capabilities. Various performance metrics evaluate its performance, including space-saving percentage, processing time, and the NIST randomness test. It provides a substantial improvement in space-saving percentage from 58.63% to 81.8%, ensuring efficiency. The security analysis confirms that ciphertexts generated by this approach pass all NIST tests, generating a 99% confidence level regarding the randomness of the ciphertext. The proposed hybrid approach offers an effective solution for addressing the challenges of securing sensitive data while taking advantage of the benefits of cloud computing.

introduction

Cloud computing has become an increasingly popular way in the networking and computer science field. It has given small and medium-scale businesses with a cost-saving opportunity to avoid the expense of purchasing hardware resources. It gives us an equal opportunity to shine as it enables users with access to a powerful computing resource without having to invest in expensive hardware or software. Cloud computing operates on the principle of virtualization, wherein a single large machine is utilized by multiple clients with a view that they have their dedicated resources. Due to the significant benefits of managing resources in unlimited storage in the most inexpensive way, business continuity, and scalability, cloud computing has had rapid growth in the IT industry, therefore, this technology has emerged as one of the most powerful innovations that has attracted the interest of technologists globally (Sajay, 2019). Cloud computing services can be classified into three primary categories: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). SaaS provides software packages, system software, and application-related server storage networks to end users such as Gmail. PaaS is used by application developers to create applications that are hosted in the cloud such as Google App Engine. IaaS deals with hardware services, virtual machines, and network architecture infrastructure such as Microsoft Azure (Mahesh et al., 2023). The deployment models include private cloud, public cloud, community cloud and hybrid cloud. Public clouds are available to everyone while private clouds are used by organizations. Community clouds are shared by multiple organizations and hybrid clouds combine both private and public clouds (El-Booz and Attiya, 2017, Thabit et al., 2021).

Conclusion and futurework

This research utilized a multi-layered security approach with compression to secure and compress text data streams while preserving the original data sequence without any changes. This research focused on developing a secure and efficient way of storing and transferring data streams in the cloud computing environment by incorporating existing encryption and data compression techniques, including symmetric key encryption techniques Rc4, AES, and the substitution cipher Vigenère, while AES is a more advanced encryption standard. Combining these techniques provided a more robust and comprehensive solution for data security while maintaining low storage of text data through LZMA compression. The proposed approach consists of seven stages in the encoding phase (sender Side) Initial Encryption, Decomposition, Substitution, Data size treatment, Compression, Encryption2, and Uploading Encrypted-compressed data in the cloud and the inverse on the (receiver side). Experimental results proved that the proposed approach achieved faster processing time compared to performing the encryption and compression techniques as separate steps. Security analysis demonstrated that the proposed approach for generating ciphertexts passed all NIST tests with a high level of confidence (99 %) in terms of ciphertext randomness. Additionally, compression efficiency analysis demonstrates that the proposed approach produced a significantly higher range of space-saving percentages (58.63 % to 81.8 %) compared to standard techniques while also providing adequate security.