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The boundary layer problem for unsteady dusty Newtonian fluid flow through a sphere influenced by magnetic field has been investigated in this paper. The two fluid flow phases that will be discussed in this work are referred to as dusty. Including the fluid and dust phases in a two-phase flow can help us comprehend the interaction of dust particles in fluid flow better. Next, the impact of magnetohydrodynamics (MHD) on fluid flow will be important to researchers since it allows them to regulate systems like cooling systems by adjusting the magnetic field. Thus, by considering the influenced of magnetic field and the existence of dust particles will be examined in this paper. The mathematical model for fluid and dusty phases is developed using continuity and momentum equations. First order partial differential equations (PDEs) are obtained by applying suitable similarity transformations on higher order PDEs The Finite Difference Method (FDM), Newton's method, and the development of a block tridiagonal matrix are the main rules that are solved using the Keller Box method. The Keller Box procedure is programmed in MATLAB environment and analysed graphically. The results are discovered to be quite compatible with earlier research.

Estimating the threshold for extreme values is essential for anticipating and managing rare and impactful events. This paper discusses four different graphical methods of estimating thresholds using three different stock price datasets. The datasets have different levels of volatility (classified as low, medium, and high). For each of the datasets, thresholds are estimated, and a generalised Pareto distribution is then fitted to the exceedances above each threshold. Subsequently, the mean squared error is calculated for each fitted model, which is then used together with the number of exceedances for each respective threshold as criteria to analyse and make inferences on the most suitable threshold approach when using a dataset that has a specified degree of volatility. It was observed that when dealing with a dataset with low volatility, Pickand plot should be considered for threshold setting. When volatility is very moderate or high, using Hill plot to determine thresholds for extreme values is recommended. The motivation for this paper lies in the need to explore and identify the most effective threshold estimation methods when dealing with different levels of stock price volatility.

The Danish fire insurance data is one of the most recognised and well-known datasets in the empirical insurance claims literature. This dataset is used in many textbooks and articles to illustrate the analysis of fire insurance claims, more specifically in the application of heavy-tailed loss distributions and extreme value theory. In this paper, we provide a short review of publications that used the Danish fire insurance data and conduct an additional analysis. Our additional analysis on the Danish fire insurance data involves investigating the: (i) modality issue using appropriate statistical tests and software, (ii) k-means clustering pattern using different techniques, (iii) effect of using a splicing model on the data, and (iv) differences in results that we obtained as compared to what other cited researchers reported in their earlier publications. In short, the objective of this paper is to highlight the importance of the Danish fire claims dataset by showcasing different models where it has been used to verify certain hypotheses in the empirical actuarial field. Additional analyses are also conducted to illustrate its dense usefulness in the actuarial and extremes field, where real-life datasets are scarce because they are often subjected to a lot of proprietary and privacy laws.

Mobile phone device became one of the most important devices in our life, usually, we feel incomplete without it, this device has many multiple uses, mobile companies manufacturers give some specifications with their new phones, one of them is specific absorption rate (SAR), this paper research will be a useful guidance for the users of mobile phones carefully how to buy and use this device, according to the specifications given by the manufacturer and the results given in this paper to be safety from the electromagnetic (EM) wave radiation during his/her dialing and call as given in the results, this research is a comparative study between most of those mobile devices that are used nowadays, it focuses on the mobile devices that had less EM radiation effect on human, depending on some practical tests as shown in the results, it is a helpful attempt for the mobile users the lowest EM radiation effect mobile device, also, this work shows the techniques to calculate and estimate SAR for mobile devices

The drones or Unmanned Aerial Vehicles (UAVs), will be crucial for addressing issues in airspace and developing traffic management. This paper’s goal will provide a review of recent research, which focuses on the development of the system based on four requirements: accuracy of position, system quality, power consumption, and user interface. Additionally, upgrades in computer vision algorithms will be implemented to capture specific information from UAVs that have captured video and images, facilitating communication with other research endeavors. On enhancing traffic flow prediction and analysis methods, addressing the challenges posed by increased numbers of UAVs (multiUAVs) and how to overcome roundabouts and obstacles, in conjunction with their consequences. This paper will summarize all methods used in mining data and leveraging it to identify the most suitable way to reduce accidents and enhance monitoring. We focused on the YOLO (You Only Look Once) algorithm and compared all versions. It was observed that the eighth version is considered the best, and students can benefit from it in projects related to computer vision. Then, the YOLO output can be passed to the Queuing theory for time control, specifically for side applications.

Artificial intelligence is the fast growing field of science, technology, and engineering. Artificial intelligence tries to mimic human intelligence capabilities such as understanding, learning, inferring, logic, problem solving, decision making and install these capabilities into machines, software, computer applications and computer programs. Artificial intelligence is used and implemented in many science, engineering and medical fields. The aim of this research paper is to investigate and introduce the role of the artificial intelligence in the medical field. Artificial intelligence is used in different sections in the medical field such as offering more accurate medical diagnoses, discovering new drugs, better medical data analysis, better patient care, better healthcare systems, electronic health records, clinical trial medicine, and diagnostic imaging analysis. The methodology used in this research paper was to identify and select research and technical papers related to the used of artificial intelligence in the field of medicine. The selected papers then were systematically reviewed. The findings of the paper showed that artificial intelligence is increasingly used in many areas of the medical field.

With the emergence of Artificial Intelligence (AI), there are many applications for 3D computer vision, and different problems in diversified domains are being solved. Particularly deep learning and image processing techniques are widely used in computer vision applications, for example, medical imaging which commonly uses 2D images to see human organs can benefit tremendously from 3D reconstruction of a human organ or cancer lesions for diagnosis purposes. Cars nowadays use radar, lidar, and sensors to create an awareness of the surroundings of the car, having a 3D reconstruction of the car’s surroundings will provide a better situational awareness. In many computer vision applications, it is essential to use image conversion techniques. With AI in place, learning-based approaches became popular along with image processing techniques. Towards this end, this paper throws light on those methods with a systematic literature review. The insights presented in this paper can help in further research in computer vision applications.

The automotive business has changed as a result of connected vehicles, which have improved urban adaptability and livability. The influence of Vehicle Ad-hoc Networks (VANETs) and their development into Intelligent Transportation Systems (ITS) is examined in this paper. The Internet of Vehicles (IoV), which facilitates vehicle-to-vehicle (V2V) communication, is built on top of VANET. Even though VANETs have advantages like increased entertainment and road safety, managing a variety of services with different Quality of Service (QoS) needs can be difficult. Software-Defined Networking (SDN), which facilitates effective control of mobile nodes, inter-operations, and resource allocation, is essential in addressing these issues. The paper is reviewed that how VANET technologies have developed over time, showing how they are now used for more than just safety services. Examples of these uses include IoT-based advertising, surveillance, and vehicle cloud computing. However, the inherent difficulties of VANETs, including changing node densities and high mobility, need programmable networking designs like SDN .Splitting architecture of network to control plane with data planes. Strategically, and SDN-enabled Vehicular Networks (SDVN) offer a centralized view for efficient management.

Heart diseases are serious global health concerns that could result in many deaths. Detecting and classifying the heart diseases early is crucial for initiating treatment and improving patient outcomes. ECG signals contain valuable information to analyze cardiac functions. It can be argued that techniques of Deep learning (DL) are effective aid to classify ECG signals accurately through learning from large amount of ECG data, ability to extract hidden information, and achieving superior performance in detection heart abnormalities. ECG signals processing involves three phases, preprocessing, extraction features and classification. This paper intends to review several studies published from 2019 to 2024 in this field. It follows a method of comparative analysis, considering specific performance metrics, preprocessing techniques, and the DL model used. The aim is to determine the most accurate DL technique for classifying ECG signals. Eventually, the paper indicated that the debate on the most accurate technique for classification remains ongoing. However , the reviewed studies demonstrated that models based on CNN and RNN can achieve significant level of accuracy in classifying ECG signals. On other hand, according to the conducted comparative analysis, it is recommended to use VGG16 as a classifier for ECG signals. As a suggestion, the complexity of VGG16 can be reduced, allowing for the implementation of a real-time application.

This survey paper is motivated by the imperative need for advanced and precise diagnostic tools in the realm of prostate cancer, where Gleason grading plays a pivotal role in determining the severity and treatment strategy. The aim of this comprehensive review is to explore and assess the diverse spectrum of deep learning approaches applied to prostate cancer Gleason grading, with a specific focus on convolutional neural networks (CNNs), transfer learning, ensemble methods, and emerging techniques. The primary contribution lies in offering a consolidated understanding of the current state-of-the-art methodologies, their architectures, and training strategies, while also addressing challenges and advancements in the integration of deep learning into clinical workflows. Furthermore, the survey discusses recent developments such as the incorporation of multimodal data and explainable AI methods, shedding light on their potential to enhance the interpretability and adoption of deep learning models in the critical domain of prostate cancer diagnosis. Through this, the paper aims to provide a valuable resource for researchers, clinicians, and practitioners, guiding future endeavors toward more accurate and efficient Gleason grading using deep learning techniques.

This research paper explores the pivotal role of energy-efficient Heating, Ventilation, and Air Conditioning (HVAC) systems in propelling sustainable urban development within the framework of India’s ambitious Viksit Bharat 2047 mission. By examining the intersection of sustainable urbanization and HVAC technology, this study aims to elucidate how innovative HVAC solutions can contribute to the realization of Viksit Bharat’s vision for environmentally conscious and resilient urban environments by the year 2047. [12] Through a comprehensive analysis of best practices, technological advancements, and policy interventions, this paper offers insights into the transformative potential of energy-efficient HVAC systems in fostering sustainable growth, enhancing indoor comfort, and mitigating environmental impacts in India’s rapidly evolving urban landscape.

The rapid adoption of Internet of Things (IoT) devices in smart homes has led to growing security vulnerabilities, primarily due to the limitations of traditional, static access control mechanisms. This paper presents a novel, dynamic access control policy that leverages the immutable and transparent nature of Blockchain technology, specifically Ethereum, along with machine learning algorithms to enhance security measures. By integrating machine learning algorithms like Support Vector Machines (SVM) and Neural Networks, the proposed system can adapt and respond to changing behavioural patterns and potential threats in real time. Additionally, a caching mechanism implemented on the Ethereum Blockchain is introduced to optimize system performance and reduce latency. Experimental results demonstrate significant improvements in access control security, system efficiency, and adaptability. The findings of this paper not only contribute to the advancement of secure access control policies for IoT smart homes but pave the way for future research in integrating Blockchain and machine learning for robust and scalable IoT security solutions.

The main goal of an audio steganography method is to improve the capacity, im-perceptibility, and robustness. Several methods of audio steganography have been proposed to enhance its capabilities. To further optimize the efficiency of the audio steganography method, this paper proposes to select the appropriate audio cover for concealing the message. The selection of an appropriate cover can significantly improve the quality of the output and facilitate the development of innovative audio steganography techniques. This paper proposed a new audio cover selection method, which can further enhance the characteristics of the resulting output. This method ranked each cover according to the qualities that need to be boosted using various measurements. These measurements are the Maximum Available Space (MAS), Peak Signal to Noise Ratio (PSNR) and Bit Error Rate (BER) for capacity, imperceptibility, and robustness respectively. Based on the experiment conducted using five cover audios stored in the self-created database, each audio is ranked differently based on the measurement used to determine its characteristic level. In conclusion, the proposed cover selection method can be used to select the most proper cover, hence improving the characteristics of audio steganography.

Vehicular traffic flow anomaly detection is crucial for traffic management, public safety, and transportation efficiency. It assists experts in responding promptly to abnormal traffic conditions and making decisions to improve the traffic flow. This survey paper offers an overview of the application of machine learning to detect anomalies in the traffic flow. Through an extensive review of the literature from the Scopus database, this paper explores the technical aspects of traffic flow anomaly detection using machine learning, including data sources, data processing approaches, machine learning algorithms, and evaluation metrics. Additionally, the paper highlights the emerging research opportunities for researchers in enhancing traffic flow anomaly detection using machine learning.

In the realm of the Internet of Things (IoT), a smart mirror is akin to a regular mirror possessed with intelligence, incorporating technology to provide useful information. The need for an integrated multi-functional information display, efficiently consolidating global information, optimizing electricity consumption, and monitoring fire hazards, has become crucial for increasing awareness among organizational staff and visitors. However, many organizations still rely on traditional information displays, leaving them vulnerable to issues such as electricity wastage and hazard oversight. To tackle these issues, we developed a smart mirror enhanced with voice recognition to quickly retrieve global information, simultaneously reducing electricity costs and enabling early detection of fire hazards. This paper details the design and development of smart mirror, following a systematic approach based on the System Development Life Cycle (SDLC) methodology. As a result, evaluations of user acceptance measured through the System Usability Scale (SUS) tool, confirms the operational efficiency and positive reception of the system. This paper significantly contributes to advancing smart mirror research, providing a valuable resource for developers, researchers, and practitioners constructing their iterations and offering inspiration for future enhancements.

The paper describes the Data Envelopment Analysis method. It demonstrates its application to study the efficiency of traffic management. The paper solves the problem of increasing the efficiency of a T-junction by increasing exit capacity and optimizing the number of exit traffic lanes. An output-oriented DEA model is applied to solve this problem. The sample uses indicators from 56 similar T-junctions with three entry traffic lanes and a different number of exit traffic lanes.

The paper considers the problems of ergonomic thinking, its formation and evolution when designing information systems in the conditions of socio-technogenic development of the world. Special attention is paid to the analysis of the ergonomic thinking evolution in the design of human-technology-environment systems. The main purpose of this work is to study the process of formation and further development of ergonomic thinking since the very beginning of ergonomics as a science. The paper considers the main approaches to the definition of ergonomic thinking, on which basis definitions are proposed that take into account all the features and directions of the evolution of ergonomic thought. Special attention is paid to the development of the main directions and factors of ergonomics according to the periods of its formation.

Thefittest is a new Python library specializing in evolutionary optimization methods and machine learning methods that use evolutionary optimization methods. Thefittest provides both classical evolutionary algorithms and efficient modifications of these algorithms that do not have implementations in the open access. Among the advantages of the library are the performance of the implemented methods, accessibility and ease of use. The paper discusses the motivation for developing and leading the project, describes the structure of the library with examples of use, and provides a comparison with other projects with a similar development goal. Thefittest is an open-source project published on GitHub and PyPi, developed using modern methods of code analysis and testing. At the moment of writing the paper, the latest version of the library is 0.2.3.

For efficient operation of machine learning methods, it is necessary to set their parameters up properly. Both the computational complexity and the accuracy of the method for the problem being solved can depend on the selected parameter values. The paper discusses the method of logical analysis of data, which is used to solve classification problems and consists of a number of steps. At each step, it is necessary to set the method parameters up to suit the problem being solved. When setting parameters, one should be guided by a compromise between the accuracy of the method and the computational complexity. With comparable values for classification accuracy of several variants of the method implementation, preference will always be given to the simplest of them in terms of computational complexity. Since the method works only with binary characteristics, at the first stage it is necessary to binarize quantitative characteristics. The binarization procedure is associated with the choice of the “number of binarization thresholds” parameter. This paper proposes an experimental approach to determine the best value of the specified method parameter for the problem being solved.

The output distribution of statistical variables characterizes the level of uncertainty of a complex system, the state of which is given by probabilistic changes in internal relationships. Information entropy is an effective tool for analysing the distribution of data in complex systems. Subfamilies of generalized beta distributions of the first and second types includes many simple distributions as special cases. These subfamilies are often used to model the output state of the system by distributions of random variables. There are conditions in the paper, that are imposed on the parameters of the generalized beta distribution to obtain relationships with simpler types. The paper contains an expression for calculating the information entropy, the generalized beta distribution, which includes distributions of the first and second kind as special cases. Expressions of information entropies for the most famous subfamilies of generalized beta distribution also are given.

The paper presents the development of time series forecasting algorithms based on the Integrated Autoregressive Moving Average Model (ARIMA) and the Fourier Expansion model. These models were applied to non-stationary time series of stock quotes after bringing these series to a stationary form. In the paper, ARIMA and Fourier Expansion model were constructed, using Python development environment. The developed algorithms were tested on Russian and American stock indices using the Mean Absolute Percentage Error metric.

The paper is aimed at developing distributed artificial intelligence systems for relevant simulation of collective solving of practical problems by modeling reflection. The paper presents the basic design principles and typical functional structure of reflexive-active systems of artificial heterogeneous intelligent agents. Agents of such systems mutually model each other’s reflexive positions; dynamically develop tactics and strategies for their behavior that are relevant to the models of their counterparties; if it is necessary during problem solving, attract new agents from the pool of available agents built by various developers and exclude existing ones, rebuilding “on the fly” the composition of agents and the structure of connections between them.

This paper investigates the significance of context comprehension within the smart city domain by emphasizing the various perspectives of stakeholders such as ministries, municipalities, and citizens. Since the value of service provision depends on the interactions across different service systems within distinct domains and contexts, through an exploration of the value chain and formulation of value propositions, the paper aims to achieve the synergy among diverse service contexts. Using the smart city context of the Czech Republic as a case study, the paper systematically examines the implementation of context-driven methodology and provides insights for the broader development of smart cities. Also, this study highlights the importance of understanding service overlaps and identifies key issues for future service research, with potential applicability in smart city initiatives, particularly in middle and eastern EU countries.

Digital instruments, devices, and platforms are exerting a transformative influence on trends and dynamics within socio-economic assets. This evolution leads to the emergence of novel configurations, exemplified by the concept of Smart Cities. It is crucial to comprehend and effectively manage the associated challenges, risks, and opportunities to ensure the viability of socio-economic configurations. This necessitates a paradigm shift in managing and addressing the relationships that form the backbone of these configurations. Given the ongoing evolution in socio- economic landscapes, this paper zeroes in on the imperative to reassess established approaches to citizens’ engagement in Smart City within Digital Era. By centring attention on the domains of technology readiness and the digital gap, the paper introduces a conceptual model grounded in the interpretative framework provided by Viable Systems Approach. This model serves to delineate key pillars for reflection and action, guiding the exploration of new pathways and processes aimed at amplifying citizens’ engagement in Smart City within Digital Era.

This paper aims to investigate the role and function of digital and smart technologies, including AI applications, within organizations in making them much more inclusive for people with disability (PWD) at the workplace starting from the recruitment process. Specifically, this conceptual study provides an indepth analysis of smart recruitment process for creating work environments much more inclusive and sustainable for PWD. In the last three decades, also because of the COVID-19 pandemic, the digital transformation, largely adopting digital and smart technologies, has significantly, both positively and negatively, affected any field and industry in the private and professional life. Indeed, our ways of working and quality of life have been improved by digital and smart technologies which are able to overcome geographical, physical, and social barriers. Likewise, some negative effects are related to this phenomenon, such as digital divide especially for some categories of people, like those with disabilities or special needs. This conceptual paper provides a systematic literature review; indeed, the phenomenon of smart recruitment is investigated providing an overview regarding its insights, challenges, and future developments. A bibliometric analysis is conducted using WoS and Scopus databases with manual selection through the VOSviewer software.

In smart cities, community pharmacies offer a range of health services, including clinical data gathering, medication dispensing, and automated alerts for precautions. Our paper reviews smart pharmacies, highlighting a shift from prescription-based treatment to patient-centered care. This transformation involves promoting a culture of cooperation and shared decision-making among stakeholders for value co-creation through seamless integration of pharmaceutical services and technology. Through the lens of Service Dominant (SD) Logic, this conceptual paper sets out to examine the context of smart pharmacies in the contemporary healthcare landscape, revealing their potential in advancing patient-centered care and driving collaborative value creation within a complex healthcare ecosystem.

Recently, there has been increased awareness about the importance of data derived from actual customer journeys, including the subjective customer experience, in the analysis and evaluation of service quality. In this paper, we explore how customer journey analysis and process mining can be combined to advance the analysis and improvement of services. First, we demonstrate the strengths and weaknesses of both methodologies using a specific case study as an illustrative example. Subsequently, we delve into the synergies and challenges inherent in their combination, deriving practical guidelines. We then suggest avenues for further research questions in this cross-disciplinary approach. The paper underscores the potential of aligning these methodologies to provide a more accurate and complete understanding of service delivery, ultimately contributing to the enhancement of customer experience.

Incentive mechanism plays an important role in online education. In order to better play the role of the incentive mechanism, it must rely on contemporary information technology, such as blockchain technology, big data technology and so on. In this paper an incentive mechanism based on blockchain technology is proposed with which can establish a good trust relationship between multiple learning resource nodes and improve data security during the process of online education. A structure of online learning resource chain is proposed in this paper also. And a dynamic optimization framework of learning resource chain by analyzing a series of problems faced in the process of online education is formed. The private chain of learning resource is constructed on the basis of the design of incentive smart contract. The learners and the builders of learning resources can obtain the dynamic rewards by performing a smart method of contract. The experimental results show that the incentive method based on blockchain can well mobilize participants’ enthusiasm for resource construction and dynamic improvement of resource quality, and effectively solve a series of problems in the learning process.

The paper suggests fast hardware accelerators for discovering clusters of zeros and/or ones in binary vectors. Any cluster is composed of successive bits with the same value (either 1 or 0). Search for such segments is required in many practical problems, for example, coding, data, and image processing. The proposed solutions enable, for a given vector, answering such questions as how many one/zero clusters can be found; what is the largest number of consecutive ones/zeros; what is the number of clusters having k consecutive ones/zeros; is the vector only composed of segments with exactly k consecutive ones/zeros; and some others. The relevant practical applications, for which acceleration is required, are also discussed. The paper suggests two core architectural solutions that are based on combinational and iterative networks of gates. Each network is modeled in software (C++ language) and then specified in a hardware-description language (VHDL), synthesized, and implemented in FPGA. Finally, the results of the circuits’ evaluations and comparisons are presented.

This paper addresses the issue of underutilized software test data in current aerospace software quality management. Through analyzing the current third-party software evaluation process, the paper proposes to optimize software evaluation process and management to enhance software development quality and software test quality. The paper proposes an aerospace software quality improvement framework based on software test data analysis, which provides a reference scheme for aerospace software quality management. Overall, the paper emphasizes the importance of software test data analysis in improving aerospace software quality and provides a comprehensive solution for addressing problems identified in aerospace software quality management.

Variational estimation method is a deterministic approximation technique which involves Bayesian framework while giving a point estimate instead of the usual Bayesian interval estimation. The linear regression model, which has always been a popular model, can benefit from the implementation of variational estimation method. In this paper, the theoretical basis on why variational method can reduce overfitting in linear regression is reviewed. Based on the review, in theory, variational method is more robust to overfitting than MLE. This paper also performed a simulation study. The simulation is done in a manner such that the simulation represents the situation of predicting for new or hidden data. The simulation starts from generating random explanatory data and generates the appropriate response data based on linear regression equation. Then, the randomly generated data is used to estimate the linear regression parameters. The simulation is performed to compare the parameters estimation results from variational method with the method of MLE. The comparison is done using the estimation values and the squared differences between true parameters value and the estimates. Empirical findings show that both methods have relatively close estimate values. It can be seen as the simulation study concludes that both variational and ML yield rather close parameters estimates for simple linear regression case. The estimates closeness gets more obvious as the sample size grows. The study also found that Variational method has performs better in terms of parameters estimation in linear regression when the sample size is small or the data has large variance.

This paper proposes a framework for privacy-preserving credential issuance and verification over the public blockchain. The credential used in this framework is a soulbound token (SBT), a non-transferrable non-fungible token (NFT) verifiable on the blockchain. Once the issuing organization issues the credential, this framework gives the holder complete control of the credential. This privacy-preserving property allows the holder to selectively disclose the credential attributes in the verification process. The framework proposed suggests a decentralized credential recovery mechanism if the credential holder loses their private key. This paper compares this framework’s efficiency with different schemes based on privacy-preserving, selective disclosure, and decentralized credential recovery. This paper also compares the overhead for credential issuance and verification with Merkle trees. This paper also discusses the real-world use cases where this framework can be applied.

The paper presents the Sustainability compass, which aims at supporting small and medium-sized enterprises (SMEs) to improve the performance of their sustainable development; hereafter called the SME Sustainable compass (SME-SC). Firstly, the paper introduces the result of the first step of the project that proposes the Sustainability compass as an online diagnostic tool. Thus, the paper continues with the second step of the project, which uses the data captured in the previous step to develop a recommender system to suggest the models to follow, and specific recommendations of those role models. Accordingly, a general architecture for the SME-SC is presented. Finally, the paper ends with a discussion, conclusion, and further research directions.

Long-period maintenance of software often causes the original program modularization to decay, resulting in a degraded quality of the software. Placement of software artifacts, namely code files or classes in a suboptimal manner among software packages is one of the most common causes of this decaying modularization of software. Software remodularization (remodularization) is an old reverse engineering approach that helps in alleviating this issue by improving the quality of software modularization. However, in literature, a multitude of software remodularization approaches exists leaving researchers and developers in a dilemma of choosing appropriate remodularization criteria. Therefore, this paper carries out a Systematic Literature Review (SLR) of the last two decades and presents concise information by answering different relevant research questions important to both developers and researchers of this field. This paper considers 54 research articles as primary studies.

Everyday there is an increase in the number of malwares being created which presents a significant danger to the Android systems holding a large share in the operating systems market. This surge in malware creation also makes it challenging to analyse and detect these malicious applications. Machine learning techniques are commonly used for malware detection, but the development of an effective system requires a reliable dataset to train and test it. This paper provides an overview of the most commonly used datasets in malware detection research conducted between 2015-2020, based on their performance, usability, availability, and effectiveness. By analysing and comparing these datasets, this paper aims to provide insights into the selection of appropriate datasets for future research in this area.

A mobile ad hoc network (MANET) is a dynamic wireless network developed using wireless nodes without using any infrastructures. The significant features of MANET are low-cost infrastructure, self-organization, mobility, and rapid deployment which offer the opportunity to deploy it for various applications such as disaster relief, environmental monitoring and military communications. The wireless nature of mobile networks causes the susceptible to malicious attacks. Therefore, security is turned out an essential factor to ease the secured message transmission among mobile nodes in the wireless medium. In this paper, the significant definition of the secure routing protocol is designated with its merits. Since there are various types of secure routing developed for accomplishing the secure transmission over the MANET. This paper studies the different types of existing routing methods such as optimization-based routing, and key encryption-based routing used in the MANET. The packet delivery ratio, energy consumption and end-to-end delay are considered key parameters for defining the effectiveness of secure routing protocols. This comprehensive research supports the researchers to obtain the best solutions for the current issues in the secure routing of MANET.

This paper study of renewable energy sources in electrical output by the appearance of numerous and diverse issues and concerns. Finding smart technologies and algorithms capable of producing efficient solutions is one of the most critical issues. This paper also discusses how the algorithm improved to deal with difficulties linked to renewable energies to obtain the required results. The PSO algorithm was handle numerous issues in non-conventional power source, like optimum solar power, hybrid power, size, and total prices, etc. when the PSO method demonstrated its resolving issues versatility and several scholars continued to explore and refine the PSO algorithm. The proposed method efficiency and correctness are confirmed by develop model, its performance by developed model, its performance investigate the result by using MATLAB/Simulink. Overall, investigations and studies have demonstrated that the technique of PSO algorithm is one of the best renewable energy systems. This is because the technique is simple, efficient, and effective when compared with other optimization techniques and methods.

This paper presents a qualitative automatic feature selection framework. Feature selection plays a very important role in selecting those features which provides the best results in terms of accuracy. The research work is aimed for in depth analysis of non-linear parameters using EEG signals. This paper also provides a comprehensive study of the features and their interpretations in characterizing epileptic seizures. We examine the quality of each feature independently in terms of classification performance metrics to provide meaningful information about the features. Optimally setting the non-linear combination leads to high classification accuracy. The accuracy can further improve by combining some other qualitative features with the optimal non-linear combination. Experimental results on two data sets shows that Hjorth parameters (HjPm) + approximate entropy (ApEn), and HjPm + ApEn + Higuchi fractal dimension (HFrDm) give high and nearly the same accuracy. However, HjPm + ApEn combined with some statistical feature gives more than 99% accuracy for most of the cases. The optimal combination of the features is providing a computationally inexpensive solution and can be deployed on low-cost hardware.

The implementation of CNN FPGA is of increasing importance due to the growing demand for low-power and high-performance edge AI applications. This paper presents a comprehensive survey and research on the topic, with a focus on comparing and evaluating the performance of two main FPGA architectures, streaming and single unit computing. The study includes a detailed evaluation of the state-of-the-art CNNs, LeNet-5 and YOLOv2, on both FPGA architectures. The results provide useful insights into the trade-offs involved, limitations, challenges, and the complexity of implementing CNNs on FPGAs. The paper highlights the difficulties and intricacies involved in implementing CNNs on FPGAs and provides potential solutions for improving performance and efficiency.

In the Internet of Things and wireless sensor networks period, a large number of connected objects and seeing bias are devoted to collecting, transferring, and inducing a huge quantum of data for a wide variety of fields and operations. To effectively run these complex networks of connected objects, there are several challenges like topology changes, link failures, memory constraints, interoperability, network traffic, content, scalability, network operation, security, and sequestration to name many. therefore, overcoming these challenges and exploiting them to support this technological outbreak would be one of the most pivotal tasks of the ultramodern world. Recently, the development of Artificial Intelligence(AI) led to the emergence of Machine Learning (ML), which has become the crucial enabler to figure out results and literacy models in an attempt to enhance the quality of service parameters of Internet of Things and wireless sensor networks. By learning from one gest, ML ways aim to resolve issues in the Internet of Things and wireless sensor networks and fields by erecting algorithmic models. In this paper, we’re going to punctuate the most abecedarian generalities of ML orders and Algorithms. We start by furnishing a thorough overview of the Internet of Things and wireless sensor network technologies. We also bandy the vital part of ML ways in driving up the elaboration of these technologies. also, as the crucial donation of this paper, a new taxonomy of ML algorithms is handed. We also epitomize the major operations and exploration challenges that abused ML ways in the WSN and IoT. ultimately, we dissect the critical issues and list some unborn exploration directions

The key issue of smart city development usually lies in the understanding of the complexity of the services’ structure. Thus, smart city models are developed to improve current outlooks on Smart City Services’ structure by using service-dominant logic and service science. However, the contextual changes are usually not fully considered in the existing smart city models. It is in turn difficult to catch the changes among the layers in the smart city model. Therefore, in this paper, we propose a complex and innovative structure of smart city services. The main contribution of the proposed approach is to consolidate the interconnection of services that is affected by the changing context. It will also lead to a better understanding of the complexity in a smart city. The presented conceptual model has a direct influence on the practical development of a smart city. Since every country and city approaches the services’ structure differently, the smart city model is derived with the lack of a common understanding of various smart cities. In the case of accepting our model as a common solution, the sharing of knowledge and information among cities and countries would be easier and more valuable. The paper also shows how the current knowledge in service-dominant logic and service science helps to develop a new practical approach to understanding the smart city structure.

Smart Cities can be considered as one of the most challenging topics within the last decades. Overcoming the well analysed debate about the digital devices and infrastructures needed for supporting the emergence and viability of Smart Cities, the paper aims at investigating how citizens’ engagement in Smart Cities can be used for promoting positive attitudes and behaviours towards Cities Circular Economy (CCE). Building upon a managerial perspective the paper aims at depicting preliminary conceptual reflections about antecedents and conditions able to influence citizens’ engagement in Smart Cities functioning. The paper proposes a Knowledge Management based framework for supporting both policy makers and managers in better understanding on which levels to act for ensuring a participative approach to the definition of a Circular Economy Logic in Smart Cities ecosystems.

The quality of public services provided to citizens through information and communication technologies has emerged as one of the key points of public administration reform. In Italy, many legislative measures have triggered a virtuous circle of eGovernment that has achieved limited results in adopting new technologies by citizens and businesses [1]. This paper sets out to explore whether the design of an IT platform operating in a smart city context may increase the quality of services provided to citizens. Based on a case study methodology, the research examines the process of revitalization and smartification of a city, seen from the stakeholders’ perspective such as Government, Citizens and Tourists [2]. The paper is original in its examination of the Tely platform enabled by data obtained from the technology developer. It turned out that the platform has the potential to increase the quality of services provided to citizens and promote social integration in an area with a significant migratory population. However, the limitations of a single case study in a specific research context, such as a small town, are acknowledged and further research is needed to fully understand the implications of smart city initiatives and their effective implementation in different contexts.

The purpose of this paper is to review and critique the literature on the role of smart cities within the healthcare context, providing an overview of the state of research and outlining a future research agenda. Initially, six hundred and seventeen newspaper articles were extracted from Scopus, and their content was analysed for the article selection process by the two researchers in parallel. Finally, forty-six articles dealing with smart cities in healthcare and published in various academic journals have been analysed through content analysis and bibliometric analysis. The results show that the literature on this research topic is somewhat scarce and dominated by unrelated research. Content analysis provides the emergence of three main strands of research: 1) Smart cities as a tool for health security; 2) Smart cities as sources of opportunities for data communication in healthcare; 3) Smart cities for the creation of knowledge and skills in healthcare. The paper presents the first attempt to provide a comprehensive, structured literature review of the role of smart cities in the healthcare environment after the outbreak of the Covid-19 pandemic. Despite the growing literature on smart cities, this research area contextualised the healthcare context is still fragmented and under-theorised. More systematic and holistic studies are needed, considering the technological, economic, and social aspects of the importance of smart cities in the healthcare system.

This paper provides an overview of Nature-Based Solutions (NBS), a new emerging concept in climate change adaptation and mitigation strategies. It introduces a typology of NBS, from minimal intervention to the highest degree of engineering, and provides examples of each. It also provides an overview of the ‘Life Metro Adapt’ project, which focused on the climate problems of the Milan Metropolitan City and supported the development of climate change adaptation strategies through alternative, nature-based solutions. Finally, the paper stresses the need for increased awareness of environmental issues, in order to ensure that NBS become ‘commonplace’ today and can be perfected in the future.

This paper investigates the Italian football clubs adopting a double perspective: the point of view of sustainability and technology innovation. In the last decades, the increasing implementation and spread of digital technologies worldwide in any economic settings, including the sport industry, especially the football business, has been playing a crucial role in supporting managerial, work out and training processes and making them much more competitive and high performing. At the same time, sport organizations, including football clubs, must achieve the UN Agenda 2030 with its 17 SDGs trying to improve their performance in the sustainability perspective. This paper, through a multiple case study of the Italian football clubs in the Serie A for the year 2021/2022, provides a portrait of these sport organizations and their way to be sustainable and much more technologically advanced in one specific county, where football industry has high relevance for volume of affairs and impact on sustainability. Thus, we match the two perspectives especially considering the role and function of digital technologies in making football clubs much more sustainable. After a brief review of the literature on the topic, we investigate the Italian football clubs mapping them considering their degree of sustainability and technology orientation. Our qualitative research provides useful insights about specific technologies able to improve the overall performance of the sport organizations, as well as knowledge about best practices already adopted by organizations for being sustainable, creating a kind of guideline for operators in the football industry.

This paper presents a control algorithm for generation of reference voltage and current based on Observerbased QSG (Quadrature Signal Generator) for UPQC (Unified Power Quality Conditioner) in a Single-Phase Distribution System. The proposed UPQC is a combination of DSTATCOM and DVR used for load and source compensation such as voltage sag and swell, voltage and current harmonics etc. The proposed control algorithm is capable of generating reference signals from the distorted voltages and currents. In this paper, an improved OQSGbased PLL (Phase-Locked Loop) is utilized over OQSG to enable and operate with increased bandwidths that will improve the dynamic response, tracking accuracy and faster detection of reference signal under all varying load and grid conditions. The control algorithm is tested and evaluated using MATLAB / Simulink.

Chat applications nowadays have evolved into one of the most significant and widely used applications on smart devices. They are capable of sending and receiving messages, documents, and images with zero cost to any part of the world. All the messages should be protected. All the chat applications today are used to send messages quickly and securely. The actuality of the situation is that the messages sent are not as secure as they claim to be. So, to bridge this gap, in this paper Homomorphic Encryption is used to secure the messages further while also not reducing the speed of the transaction. This paper aims to implement a Chat application using Homomorphic Encryption which adds a layer of security over end-to-end encryption.

Cloud has grown significantly and has become a popular serviceoriented paradigm offering users a variety of services. The end-user submits requests to the cloud in the form of tasks with the expectation that they will be executed at the best possible lowest time, cost and without any errors. On the other hand, the cloud executes these tasks on the Virtual Machines (VM) by using resource scheduling algorithms. The cloud performance is directly dependent on how the resources are managed and allocated for executing the tasks. The main aim of this research paper is to compare the behaviour of cloud resource scheduling algorithms: First Come First Serve (FCFS) and Shortest Job First (SJF) by processing high-sized tasks. This research paper is broadly divided into four phases: the first phase includes an experiment conducted by processing approximately 80 thousand tasks from the Alibaba task event dataset using the resource scheduling algorithms: FCFS and SJF on the cloud VMs under different circumstances; the second phase includes the experimental results; the third phase includes a empirical analysis of the behaviour of resource scheduling algorithms; the last phase includes the proposed need of Reinforcement Learning (RL) to improve cloud resource scheduling and its overall performance.

Amidst Covid-19, young adults have experienced major symptoms of anxiety and/or depression disorder (56%). Mental health issues have been spiking all over the world rapidly. People have taken up to social media as a platform to vent about their mental breakdowns. Twitter has seen enormous rise in depressive and anxious tweets in these times, but the downside being that majority of the population has neglected the importance of mental health issues and there are not enough resources to liberate people about it. Also, people hesitate to talk about their mental issues and seek help. So, a machine learning model using distant supervision to detect depression on Twitter is curated. Use of Sentiment140 dataset with 1.6 million records of different tweets. Our training data makes use of Twitter tweets included with emojis, which are classified as noisy labels on a dataset. Further, this paper mentions about how to use models like Support Vector Machine (SVM), Logistic Regression, Naive Bayes, Random Forest, XGBoost to distinguishing tweets between depressive or nondepressive. The purpose behind using multiple models is to achieve highest accuracy when trained with emoticon dataset. The paper’s main contribution is the idea of using tweets with emoticons for distant supervised learning.