Reviewerly/exHarmony huggingface dataset:
Design and Analysis of New Version of Cryptographic Hash Function Based on Improved Chaotic Maps With Induced DNA Sequences

One of the first and most used hashing algorithms in blockchains is SHA-256 so the main aim of this paper is how to increase the security level of the blockchain based on the increasing hashing algorithm security. This paper proposes to present a modified SHA-256 like hash algorithm by exploring the design principles of the two hash schemes (SHA-256 and RIPEMD-160). To retrieve the increased security in the proposed algorithm, we modify the SHA-256 after the public key generation. The proposed modification is based on four different chosen types of chaotic maps and DNA sequences which eventually complicate the association between the original message and hash digest i.e., maximizing the security level, and minimizing its vulnerabilities. The proposed hash function efficiency is practically assessed, analyzed, and compared with the well-known SHA-256 with respect to the main properties of the confusion, diffusion and distribution. The security performance is also analyzed using the analysis of collision, which reveals that the new constructed hash function improves SHA-256 with respect to the security and robustness. From the experimental analyses results, the proposed modified hash scheme found to exhibit the better security performance than many state-of-the art existing hash function schemes.
ER-ERT:A Method of Ensemble Representation Learning of Encrypted RAT Traffic

Remote Access Trojan (RAT) is one of the major threats to today's network environment. It is a class of malware frequently used by hacking collectives to monitor victims' actions and steal personal information in targeted computers. Traditional machine learning algorithms have been widely used to detect malicious encrypted RAT traffic. Traditional machine learning algorithms rely deeply on expert experience, and it is difficult for current traffic classification models to design effective handcraft features. Deep learning methods have been introduced in recent years to generate representations from raw network traffic data automatically. Previous deep learning-based malicious traffic detection methods generate representations from flow sequences or packet payload bytes. None of these methods simultaneously learn embeddings from flow sequence and packet payload bytes. Thus, we propose a novel ensemble model to draw fine-grained and multi-angle traffic representations for RAT traffic. The model extract (1) temporal features with convolution neural network (CNN) and the Reproducing Kernel Hilbert Space (RKHS) embedding method to model network flow sequence, (2) spatial features with autoencoder and bidirectional gated recurrent unit (Bi-GRU) network to model packet payload bytes, and (3) some stage-based attributes to enhance the identification ability of RAT traffic behaviors. According to the experimental result, our approach achieves better performance than previous works with a precision rate of 97.0% and a recall rate of 96.5%.
Federated Learning-Based In-Network Traffic Analysis on IoT Edge

The rise of IoT-connected devices has led to an increase in collected data for service and traffic analysis, but also to emerging threats and attacks. In-network machine learning-based attack detection has proven effective in fast response, but scaling to distributed IoT edge devices risks increasing communication overheads and raising data privacy concerns. To address these concerns, we present FLIP4, a distributed in-network attack detection framework based on federated tree models. FLIP4 maintains data privacy by enabling distributed machine learning training while keeping data local on IoT edge, and provides in-network inference within the programmable data plane on edge gateway for timely attack labeling and mitigation. Evaluation results show that FLIP4 can accurately detect attacks while maintaining source data privacy and enabling lightweight deployment on IoT edge.
Towards a Framework to Assess the Impact of Design Patterns on Software Metrics

Design patterns are reusable solutions to common design problems. They speed up the development process and help to document software systems. This paper presents a framework to assess the impact of design patterns on software metrics. Our proposed framework applies refactoring techniques to generate non-pattern version of a subject system. Then, software metrics have been calculated and compared for both versions; the pattern version and non-pattern version. The proposed framework has been applied to two subject systems and one design pattern. The evaluation results show that there is no consistent behavior of the software metrics between the non-pattern version and the pattern version.
Artificial Intelligence in Supply Chain Management in Industry 4.0

Industry 4.0 has recently undergone major improvements owing to artificial intelligence (AI). Depending on their intention to incorporate IoT devices and linked machinery within their device, enterprises may utilize AI to interpret information transferred from those sources. Organizations can thoroughly monitor all of their end-to-end processes and operations due to AI. The advances in technology and revolutionary corporate practices made it essential for the need to modify the operating system of warehouses (WH) because of increased orders and complexity within it. The rise of smarter WH has been facilitated by the lack of managing expertise. Furthermore, since storage has become more important to logistics and supply chains, intelligent WH is extremely necessary to improve organizational management and profitability. The use of AI in WH activities improves the logistics, managerial, and coordinating capabilities of WH activities. In the current study, it is suggested that AI can be used in WH to transform it into smarter surroundings for automation logistics. The focus of the experiment is on a robotic automation system of storing and retrieving through employing AI, IoT, and cloud computing to provide unlimited access to the warehouse's supply.
Analysis of Mathematical Modelling Deterministic and Stochastic Problems in Federated Learning

Federated Learning is a distributed ma- chine learning method that empowers numerous gatherings to cooperate to get familiar with a model without sharing their information This paper presents a numerical examination of deterministic and stochastic issues in Federated Learning. In deterministic Fed- erated Learning, all the clients have the same data distribution, furthermore, the objective is to limit a worldwide goal capability. In stochastic Federated Learning, the clients have different data distributions, what’s more, the objective is to limit a worldwide goal capability. We talk about the difficulties of Federated Learning, like client heterogeneity and non-IID information appropriations, and the techniques proposed to address these challenges. We also highlight open research questions, such as how to handle non-stationarity in Federated Learning and how to handle adversarial clients in Federated Learning. Finally, we suggest future research directions, including investigating the impact of Federated Learning on model interpretability, fairness, and robustness, and exploring the potential of Federated Learning for federated reinforcement learning, federated meta- learning, and federated unsupervised learning.
Identification and Mitigation of Phishing Email Attacks using Deep Learning

Internet security is seriously threatened by email-based internet phishing. The process of completely hiding the sender information of a phishing mails is far less flexible than the ability to manipulate the body and its overall structure of phishing mails. It is a type of cyber-attack when the attacker sends an email seeming to be a reliable source, tricking the recipient into clicking a harmful link or giving sensitive data like passwords or credit card numbers. The main aim of email phishing is often to get access to sensitive information/data, spread malware, or scam the victim out of money. Detection of email phishing using machine learning involves training a model on a large dataset of both phishing and legitimate emails, and then using that model to automatically classify new incoming emails as either phishing or legitimate.
Convolutional Neural Network Model for Traffic Sign Recognition

Despite the fact that traffic sign segmentation has been publicized for a long time, the majority of past research has centered on traffic signs using graphics. This study aims to propose method for evaluating all sorts of traffic signals based on observations, including in surveillance video streams captured by a vehicle camera, both icon and word signs. Extraction of traffic sign areas of interest (ROIs), exploration and sentiment analysis of ROIs, and post-processing are the three steps of the system. To identify the Regions of interest from each shot of road markings, the most stable fractal dimension regions on grey and calibrated RGB channels are employed. The researchers suggest a multi-convolutional neural network that was trained using a large quantity of data, including fake road markings and pictures extracted from real-time scenarios, Then it refines and emphasizes them based on the most recent their descriptions. Finally, post-processing gathers all of the investors when making an isolating pronouncement. The utility of the suggested technology has been shown through experimental data. This effort aims to improve highest level of quality by flashing drivers about hazards and risks.
The Emergence and Challenges of Blockchain Technology in Business and IoT Applications

As we can see nowadays Blockchain is gaining popularity among us via media and press in various domains, especially in Banking and payment-related stuff. At the business level, this blockchain concept is about to set a new standard as it is going to be used as a foundation of a very new technology emerging that actually deals with security and internet immutability. Here we will also discuss the challenges being faced when implementing blockchain and we will also cover new areas where it is being implemented. In the end, we really hope that our research will help others to make any sort of decision if they are willing to work on the blockchain and want to invest in IoT.
Framework for the Deployment of Intelligent Smart Cities (ISC) using Artificial Intelligence and Software Networking Technologies

This article’s goals include providing a framework for interpersonal and communication to safeguard welfare services and assess how well the innovation ecosystem is now understood. An essay starts with an overview or description of "urban area" This viewpoint includes neighborhood services, modern amenities, or smart infrastructure. Information and technology, digital operations use, monitoring, and interconnection, amongst other things. The section that follows examines various urban planning concerns, either administration or expansion. Utilizing diverse technologies effectively is made possible by innovative information about towns or an answer to a question concerning high-quality public infrastructure. An innovative method of managing networks is the Software Defined Network (SDN) architecture. Unlike switches in a conventional network computing environment, changes in SDN do not process inbound sachets in the similar means. If no inbound packets are identified after searching the linking tables, they are passed to the control system, the SDN’s operating system, for processing. A Spread Denial of Service (DoS) outbreak poses the biggest risk to the cyber security of an SDN network.The system or application layers of the disease-ridden devices connected to the system will be the target of the attack. The approach for determining whether incoming packets are contaminated or not is clever machine learning-based, and it is proposed in this paper. In the context of smart cities, the deployment of artificial intelligence (AI) has newly drawn more and more attention. The purpose of this study is to perform a works assessment to examine the role of AI in the various parts of smart towns given the relevance of the topic. This study’s technique combines a thorough literature evaluation to find the most pertinent papers with reference system investigation to reveal the subtleties of the subject. The findings of this study highlight the significance of this technology, which is essential to many applications in a smart city, as well as the difficulties that it poses for both cities and citizens.
A Theoretical analysis of Recent development in Statistical kind of Machine Learning

The field of Statistical Machine Learning (SML) has seen a significant increase in interest in recent years due to the abundance of data and the growing need for automated decision-making in various management applications. SML techniques, including supervised and unsupervised learning, have been successfully applied to a wide range of management applications such as predictive modeling, customer segmentation, inventory management, fraud detection, marketing optimization and human resource management. This review paper aims to summarize recent advances in SML for management applications, highlighting key trends such as the use of deep learning for predictive modeling, unsupervised learning for customer segmentation, reinforcement learning for inventory management, and the use of Explainable AI (XAI) for transparent and interpretable decision-making. Additionally, this paper also highlights some of the challenges faced in the application of SML in management, including the need for high-quality and labeled data, domain expertise, robust evaluation metrics, and transparency. In conclusion, this review paper provides an overview of the recent advances and challenges in SML for management applications, and it could be useful for researchers and practitioners in the field.
Decoding the Black Box: A Comprehensive Review of Explainable Artificial Intelligence

This review explores the current state of Explainable Artificial Intelligence (XAI). This study looks at current advances in XAI research, as well as challenges and the future. To accomplish this, the review will explore XAI literature on a wide range of topics, including current techniques, strategies, and applications. The review begins with an overview of XAI, including its definition, history, and motivations. It will then investigate XAI's current situation as well as explainability approaches. This will entail a look at model-based, post-hoc, and interactive explainability, as well as the methods used to get there, such as feature attribution, rule extraction, and counterfactual analysis. The application of XAI in healthcare, finance, and autonomous systems will be examined. The review will look at the ethical and social consequences of XAI, such as bias, accountability, and transparency. Among the challenges that XAI faces include a lack of uniform measurements and evaluation procedures, interpretable data, and domain-specific expertise. The review will include important trends, patterns, and avenues for improvement. The review will also recommend XAI research and development. This review will assist XAI researchers, practitioners, and policymakers in understanding the field's current state, limitations and challenges, and future direction.
Digital Transformation Through Blockchain Usgae Towards Data Security

Digital transformation in government-provided services emerges as a vibrant element towards governance resilience. The integration of technological advancement in each government sector establishes the Government as a Platform (). The interacts with citizens, communities, institutions, and industries. It does not merely work to facilitate the citizens, but it establishes the level of trust and engagement towards and between the state-run institutions. Being the advanced form of e-governance, research towards understanding the level of trust in Government through is absent in the literature. Living in the digital economy makes it imperative to indulge in assessing such phenomena. A research framework was established to investigate the Trust in with Service Quality of government services and Word of Mouth impression towards such technological services through the level of Satisfaction as a mediator. An online survey was conducted to collect the data. Partial Least Structural Equation Modelling (PLS-SEM), Sporadic, and Importance Performance Map Analysis (IPMA) were used for data analysis of 236 collected responses. The study inferences provide novel contributions towards policy-making for enhancing the government's resilience and citizen engagement. The study will establish the platform towards the digital economy perspective in government-backed services.
Cloud Top Management Role in Reducing Mobile Broadband Transmission Hazards and Offering Safety

In both the academic and industrial worlds, cloud technology is becoming a widely used research area. Both providers of cloud services and consumers profit from cloud computation. The literature has mostly focused on the security issues related to the cloud. This thorough literature study's objective is to assess the state of knowledge on dangers, difficulties, and privacy for public cloud. This studies from different countries at the areas of study that some well rising institutions are focusing between on 2010. and 2020. We chose 81 papers after carefully examining several attempts to solve the stated research issues. The study's results revealed seven serious security threats to internet data centres. The results revealed that a few of the extensively covered subjects with in chosen works included data modification and leaking. Network discontinuities, storing, and information inside the realm of public cloud were also recognised potential threats. The findings of this Study also shown that CSPs and internet users alike still have difficulties when reassessing client data. The cryptocurrency was identified in our research report as an innovation activitythat would help to allay security worries. The SLR findings reveal a few things that should be done in efforts to provide categorization, legitimacy, and information availability in the present.
Utilizing Social Media Platforms for Artificial Intelligence-Based Detection and Developing Effective Communications in Disaster-Affected Areas

Disaster response organizations must have timely access to current and pertinent disaster information in order to formulate wise management decisions and decrease the impact of disasters. Social media has developed recently as a proactive avenue for disaster communications. It has made it easier for groups that deal with disasters to get a tonne of information in real time about them from the people that are affected. Researchers and professionals are now able to fully realise the significance of such a user-generated content for aiding sensible decision-making in catastrophe management thanks to advancements in artificial intelligence. This paper provides an overview of how artificial intelligence techniques are now being used to evaluate social media content connected to disasters in order to support disaster management at various stages. It gives a brief overview of a few increasingly digitally systems that use information from social media to manage disasters. This study also highlights a number of research opportunities and obstacles for handling disaster-related information on social media.
Treatment of Mental Health Disorders Advanced By Artificial Intelligence

Goal of the Evaluation Technique founded on artificial intelligence has the ability to drastically change the way mental health care is provided. This paragraph provides a brief history of artificial intelligence (AI) and its newer technologies in insurance, an analysis of notable actual studies on AI focused on personal wellbeing, and a consultation about how AI can bolster patient care while taking into account its present constraints, areas requiring further study, and wider dilemmas due to the advancements.remarkable discoveries. We looked at 28 studies on emotional stability which used medical records, mood self - report measures, neuroscience data, novel tracking systems (like smartphones and videos), and online communities to indicate, define, or clade mental health issues like ptsd, psychoses or other psychosocial problems, as well as attempts at suicide. [1] The majority of those investigations should then be viewed as early real evidence works illustrating the prospects that use machine learning algorithms to recognize psychiatric concerns and which types of algorithms yield the outstanding outcome, even though they collectively revealed high accuracies and deliver great case studies of possibilities in psychological therapies.
Exploring the Effectiveness of Natural Language Processing in Customer Service

Natural Language Processing (NLP) is a field of artificial intelligence that involves using technology to understand and generate human language. In customer service, NLP can be used to improve the efficiency and effectiveness of communication between customers and support teams. This can be done through the use of chatbots, which are computer programs that can understand and respond to natural language input. NLP can also be used to analyze customer feedback and sentiment, which can help companies identify and address common issues. Overall, the use of NLP in customer service can lead to faster resolution times, improved customer satisfaction, and increased efficiency for support teams.
Analysis of Cyber Security Threats in Payment Gateway Technology

There has been a meteoric rise in the use of online payment systems recently. Multiple e-payment systems exist, each designed to provide the highest degree of security while maintaining the greatest possible convenience for online shoppers. Cyber-attack techniques, on the other hand, are developing at the same rapid pace as security mechanisms. The authors of this paper examine the history of electronic payment systems, from the development of the corresponding language through the emergence of today's standard electronic payment systems. It also reveals a lack of security measures and approaches to fixing the problem. The current survey research makes a significant contribution by outlining the state of the electronic payment system framework and the possibilities it presents for the development of e-commerce in the future. The rates of suspicious purchases, which will serve as a yardstick in the creation of a trustworthy e-payment system, have been briefly discussed and analyzed.
Role of Artificial Intelligence in the Next Generation Wearable Devices

The famous category of gadgets now includes whole new and quickly developing devices thanks to wearable technology. Wearable technology has the ability to provide artificial intelligence (AI) techniques with a wealth of useful data, in addition to being attractive and outfitted with cutting-edge hardware technologies like communication modules and networking in next generation. Numerous projects have previously been completed using a variety of AI approaches, including monitored, unattended, semi-supervised, and reinforcement learning (RL). This article examines contemporary wearable apps that have used AI to accomplish their goals. Users cover specific examples of both supervised and unsupervised applications in medical diagnosis. Additionally, instances fusing RL, wearables, and the web of things are discussed. Wearables application samples for particular industries, including the healthcare, commercial, & sporting fields, are also provided. Implications in medicine range from personal exercise to mobility disorders and mental wellness. The use of wearables in the workplace includes improving productivity of employees. The main goal of sports programs is to improve the client satisfaction through exercise or competitive activity.The biggest issues with wearable device creation and creation as well as the computational cost of applying AI techniques are discussed. Finally, prospects and difficulties facing wearable technology in the next are discussed.