Search

Abstract In the digitization era, the battery consumption factor plays a vital role for the devices that operate Android software, expecting them to deliver high performance and good maintainability.The study aims to analyze the Android-specific code smells, their impact on battery consumption, and the formulation of a mathematical model concerning static code metrics hampered by the code smells. We studied the impact on battery consumption by three Android-specific code smells, namely: No Low Memory Resolver (NLMR), Slow Loop (SL) and Unclosed Closable, considering 4,165 classes of 16 Android applications. We used a rule-based classification method that aids the refactoring ideology. Subsequently, multi-linear regression (MLR) modeling is used to evaluate battery usage against the software metrics of smelly code instances. Moreover, it was possible to devise a correlation for the software metric influenced by battery consumption and rule-based classifiers. The outcome confirms that the refactoring of the considered code smells minimizes the battery consumption levels. The refactoring method accounts for an accuracy of 87.47% cumulatively. The applied MLR model has an R-square value of 0.76 for NLMR and 0.668 for SL, respectively. This study can guide the developers towards a complete package for the focused development life cycle of Android code, helping them minimize smartphone battery consumption and use the saved battery lives for other operations, contributing to the green energy revolution in mobile devices.

This study aims to protect software development by creating a Software Risk Assessment (SRA) model for each phase of the Software Development Life Cycle (SDLC) using an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. Software developers discovered and validated the risk variables affecting each SDLC phase, following which relevant data about risk factors and associated SRA for each SDLC phase were collected. To create the SRA model for SDLC phases, risk factors were used as inputs, and SRA was used as an output. The formulated model was simulated using 70 % and 80 % of the data for training, while 30 % and 20 % were used for testing the model. The performance of the SRA models using the test datasets was evaluated based on accuracy. According to the study findings, many risk variables were discovered and confirmed for the requirement, design, implementation, integration, and operation phases of SDLC 11, 8, 9, 4, and 6, respectively. The SRA model was formulated using the risk factors using 2048, 256, 512, 16, and 64 inference rules for the requirement, design, implementation, integration, and operation phases, respectively. The study concluded that using the SRA model to assess security risk at each SDLC phase provided a secured software development process.

Abstract Analyzing, identifying, and classifying nonfunctional requirements from requirement documents is time-consuming and challenging. Machine learning-based approaches have been proposed to minimize analysts’ efforts, labor, and stress. However, the traditional approach of supervised machine learning necessitates manual feature extraction, which is time-consuming. This study presents a novel deep-learning framework for NFR classification to overcome these limitations. The framework leverages a more profound architecture that naturally captures feature structures, possesses enhanced representational power, and efficiently captures a broader context than shallower structures. To evaluate the effectiveness of the proposed method, an experiment was conducted on two widely-used datasets, encompassing 914 NFR instances. Performance analysis was performed on the applied models, and the results were evaluated using various metrics. Notably, the DReqANN model outperforms the other models in classifying NFR, achieving precision between 81 and 99.8%, recall between 74 and 89%, and F1-score between 83 and 89%. These significant results highlight the exceptional efficacy of the proposed deep learning framework in addressing NFR classification tasks, showcasing its potential for advancing the field of NFR analysis and classification.

Abstract Background The importance of customer reviews in determining satisfaction has significantly increased in the digital marketplace. Using sentiment analysis in customer reviews has immense potential but encounters challenges owing to domain heterogeneity. The sentiment orientation of words varies by domain; however, comprehending domain-specific sentiment reviews remains a significant constraint. Aim This study proposes an Improved VADER (IVADER) lexicon-based classification model to evaluate customer sentiment in multiple domains. The model involves constructing a domain-specific dictionary based on the VADER lexicon and classifying doeviews using the constructed dictionary. Methodology The proposed IVADER model uses data preprocessing, Vectorizer transformation, WordnetLemmatizer-based feature selection, and enhanced VADER Lexicon classifier. Result Compared to existing studies, the IVVADER model accomplished outcomes of accuracy of 98.64%, precision of 97%, recall of 94%, f1-measure of 92%, and less training time of 44 s for classification. Outcome Product designers and business organizations can benefit from the IVADER model to evaluate multi-domain customer sentiment and introduce new products in the competitive online marketplace.

Tumor necrosis factor-α (TNFα) is a master cytokine which induces expression of chemokines and adhesion molecules, such as intercellular adhesion molecule 1 (ICAM-1) and vascular cell adhesion molecule 1 (VCAM-1), in endothelial cells to initiate the vascular inflammatory response. In this study, we identified neuropilin-1 (NRP1), a co-receptor of several structurally diverse ligands, as a modulator of TNFα-induced inflammatory response of endothelial cells. NRP1 shRNA expression suppressed TNFα-stimulated leukocyte adhesion and expression of ICAM-1 and VCAM-1 in human umbilical vein endothelial cells (HUVECs). Likewise, it reduced TNFα-induced phosphorylation of MAPK p38 but did not significantly affect other TNF-induced signaling pathways, such as the classical NFκB and the AKT pathway. Immunofluorescent staining demonstrated co-localization of NRP1 with the two receptors of TNF, TNFR1 and TNFR2. Co-immunoprecipitation further confirmed that NRP1 was in the same protein complex or membrane compartment as TNFR1 and TNFR2, respectively. Modulation of NRP1 expression, however, neither affected TNFR levels in the cell membrane nor the receptor binding affinities of TNFα. Although a direct interface between NRP1 and TNFα/TNFR1 appeared possible from a protein docking model, a direct interaction was not supported by binding assays in cell-free microplates and cultured cells. Furthermore, TNFα was shown to downregulate NRP1 in a time-dependent manner through TNFR1-NFκB pathway in HUVECs. Taken together, our study reveals a novel reciprocal crosstalk between NRP1 and TNFα in vascular endothelial cells.

The pervasive spread of fake news in online social media has emerged as a critical threat to societal integrity and democratic processes. To address this pressing issue, this research harnesses the power of supervised AI algorithms aimed at classifying fake news with selected algorithms. Algorithms such as Passive Aggressive Classifier, perceptron, and decision stump undergo meticulous refinement for text classification tasks, leveraging 29 models trained on diverse social media datasets. Sensors can be utilized for data collection. Data preprocessing involves rigorous cleansing and feature vector generation using TF-IDF and Count Vectorizers. The models’ efficacy in classifying genuine news from falsified or exaggerated content is evaluated using metrics like accuracy, precision, recall, and more. In order to obtain the best-performing algorithm from each of the datasets, a predictive model was developed, through which SG with 0.681190 performs best in Dataset 1, BernoulliRBM has 0.933789 in Dataset 2, LinearSVC has 0.689180 in Dataset 3, and BernoulliRBM has 0.026346 in Dataset 4. This research illuminates strategies for classifying fake news, offering potential solutions to ensure information integrity and democratic discourse, thus carrying profound implications for academia and real-world applications. This work also suggests the strength of sensors for data collection in IoT environments, big data analytics for smart cities, and sensor applications which contribute to maintaining the integrity of information within urban environments.

Abstract A genetic algorithm is a biologically inspired stochastic approach to finding solutions to optimization problems. However, unlike its deterministic counterpart, it cannot guarantee a globally optimal solution since it may be trapped within a local optimum of the search space. Most researchers have focused on proposing new techniques for various parameters of genetic algorithms. That is a mutation, crossover, or selection algorithm. This research proposes a modification to the standard genetic algorithm, which may serve as a framework that can integrate any of these parameters for their contribution to the final solution of the genetic algorithm. The multiple restart dynamic population genetic algorithm (MRDPGA) proposed in this research was used in training the parameters of the adaptive neuro-fuzzy inference system (ANFIS) for scheduling road vehicular traffic flows. The results of training the ANFIS models based on the different clustering methods showed that the MRDPGA-based ANFIS controller performed better with the mean square error (MSE) of 0.299 and root mean square error (RMSE) of 0.547 in the training phase; and MSE of 0.272 and RMSE of 0.521 in the testing phase. Using the controllers for traffic flow scheduling, the results showed that the MRDPGA-trained controllers performed better in terms of average waiting time (AWT) minimization and total arrived vehicles (TAV). The best-performing controller achieved 50.40% AWT minimization and 21.44% TAV improvement. Analyzing the results using a one-tailed t-test for paired two-sample means showed that the MRDPGA algorithm had a significant impact on the controllers. Particularly the FCM controller, where (p = 0.0038) and (p = 0.0003) for AWT and TAV at a 95% confidence level. Thus, MRDPGA algorithms are recommended for further assessment in other optimization problems to ascertain their performance in those problem domains.

In human resource management, training is considered one of the most effective ways to improve employees’ task performance. However, the effectiveness of training depends mostly on the resources and effort of learners, especially the operant resources. This study investigates the influence of operant resources on individual task performance within the framework of knowledge management. Building on existing research, a quantitative model was developed and tested using data from 296 Vietnamese managers and senior employees. Data analysis employed SPSS 21 and AMOS 24 software. The findings provide strong support for all nine proposed hypotheses, demonstrating a positive impact of operant resources on both learner behavior and subsequent task performance. The research highlights the significant role of individual operant resources in enhancing learning outcomes and employee effectiveness. Managerial implications are derived from these results, offering practical guidance for businesses to improve training activities and ultimately boost employee task performance.

Chitosan (CS) and two-dimensional nanomaterial (2D nanomaterials)-based scaffolds have received widespread attention in recent times in biomedical applications due to their excellent synergistic potential. CS has garnered much attention as a biomedical scaffold material either alone or in combination with some other material due to its favorable physiochemical properties. The emerging 2D nanomaterials, such as black phosphorus (BP), molybdenum disulfide (MoS2), etc., have taken huge steps towards varying biomedical applications. However, the implementation of a CS-2D nanomaterial-based scaffold for clinical applications remains challenging for different reasons such as toxicity, stability, etc. Here, we reviewed different types of CS scaffold materials and discussed their advantages in biomedical applications. In addition, a different CS nanostructure, instead of a scaffold, has been described. After that, the importance of 2D nanomaterials has been elaborated on in terms of physiochemical properties. In the next section, the biomedical applications of CS with different 2D nanomaterial scaffolds have been highlighted. Finally, we highlighted the existing challenges and future perspectives of using CS-2D nanomaterial scaffolds for biomedical applications. We hope that this review will encourage a more synergistic biomedical application of the CS-2D nanomaterial scaffolds and their utilization clinical applications.

The recent progress in blockchain and wireless communication infrastructures has paved the way for creating blockchain-based systems that protect data integrity and enable secure information sharing. Despite these advancements, concerns regarding security and privacy continue to impede the widespread adoption of blockchain technology, especially when sharing sensitive data. Specific security attacks against blockchains, such as data poisoning attacks, privacy leaks, and a single point of failure, must be addressed to develop efficient blockchain-supported IT infrastructures. This study proposes the use of deep learning methods, including Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural network LSTM (CNN-LSTM), to detect phishing attacks in a blockchain transaction network. These methods were evaluated on a dataset comprising malicious and benign addresses from the Ethereum blockchain dark list and whitelist dataset, and the results showed an accuracy of 99.72%.

Abstract Containers as a service (CaaS) are a kind of services that permits the organization to handle the containers more effectively. Containers are lightweight, require less computing resources, portable, and facilitate better support for microservices. In the CaaS model, containers are deployed in virtual machines, and the virtual machine runs on the physical machine. The objective of this paper is to estimate the resource required by a VM to execute a number of containers. VM sizing is a directorial process where the system administrators have to optimize the allocated resources within the permitted virtualized space. In this work, the VM sizing is carried out using the Deep Convolutional Long Short Term Memory Network (Deep-ConvLSTM), where the weights are updated by Fractional Pelican Optimization (FPO) Algorithm. Here, the FPO is configured by hybridizing the concept of Fractional Calculus (FC) within the updated location of the Pelican Optimization Algorithm (POA). Moreover, the task grouping is done with Deep Embedded Clustering (DEC), where the grouping is established with respect to the various task parameters, such as task length, submission rate, scheduling class, priority, resource usage, task latency, and Task Rejection Rate (TRR). In addition, the performance analysis of VM sizing is done by taking 100, 200, 300, and 400 tasks. We got the best resource utilization of 0.104 with 300 tasks, a response time of 262ms with 100 tasks, and a TRR of 0.156 with 100 tasks and makespan of 0.5788 with 100 tasks.

In recent years, the rapid and wide-ranging implementation of a cloud-based electronic healthcare record (EHR) storage system has shown significant advantages in effectively managing EHR for healthcare organizations and patients. However, in the cloud-based EHR storage model, the patients no longer have direct control of their EHR, whereas healthcare organizations may access the outsourced EHR whenever necessary. It may always cause severe security issues, specifically when healthcare organizations collude with the cloud service provider (CSP) to conceal any medical malpractice. Therefore, to deal with these significant concerns, we have introduced a novel blockchain based efficient tamper-proof model for EHR storage in decentralized InterPlanetary File System (IPFS) storage in the cloud - “TAC-EHR”. The key idea of the model is that every operation involves outsourcing EHRs and integrating these EHRs into a transaction on the public blockchain provides computationally unforgeability to the outsourced EHRs. Moreover, the proposed EHR storage model can also manage batch outsourcing, i.e., numerous EHR outsourcing for multiple patients by multiple doctors simultaneously, in an effective manner. The experimental and security analysis demonstrates that the proposed blockchain based cloud-assisted EHR storage model efficiently assures intractability computationally and outperforms the existing models in terms of computational and communication overhead.

The number of mobile apps is continuously growing but development organisations are not sure of the good alignment of the skills of application developers with what the industry needs. A precise definition of the professional profile of the Mobile Application Developer (MAD) can help to better understand the needs of software development teams. This cooperation of several European organisations for analysing professional profile of MAD has led to a study of skills and competences resulting in a conceptual model with relevant characteristics: a) based on literature review, b) framed within the European standard e-Competence Framework, e-CF (EN16234) and the ESCO official labour classification and c) empirically validated with qualitative and quantitative data from many stakeholders in the field. This analysis might help to add homogeneity to talent management overcoming possible barriers for international mobility within EU as the concepts are taken from the EU reference models.

The purpose of automated video object removal is to not only detect and remove the object of interest automatically, but also to utilize background context to inpaint the foreground area. Video inpainting requires to fill spatiotemporal gaps in a video with convincing material, necessitating both temporal and spatial consistency; the inpainted part must seamlessly integrate into the background in a variety of scenes, and it must maintain a consistent appearance in subsequent frames even if its surroundings change noticeably. We introduce deep learning-based methodology for removing unwanted human-like shapes in videos. The method uses Pareto-optimized Generative Adversarial Networks (GANs) technology, which is a novel contribution. The system automatically selects the Region of Interest (ROI) for each humanoid shape and uses a skeleton detection module to determine which humanoid shape to retain. The semantic masks of human like shapes are created using a semantic-aware occlusion-robust model that has four primary components: feature extraction, and local, global, and semantic branches. The global branch encodes occlusion-aware information to make the extracted features resistant to occlusion, while the local branch retrieves fine-grained local characteristics. A modified big mask inpainting approach is employed to eliminate a person from the image, leveraging Fast Fourier convolutions and utilizing polygonal chains and rectangles with unpredictable aspect ratios. The inpainter network takes the input image and the mask to create an output image excluding the background humanoid shapes. The generator uses an encoder-decoder structure with included skip connections to recover spatial information and dilated convolution and squeeze and excitation blocks to make the regions behind the humanoid shapes consistent with their surroundings. The discriminator avoids dissimilar structure at the patch scale, and the refiner network catches features around the boundaries of each background humanoid shape. The efficiency was assessed using the Structural Learned Perceptual Image Patch Similarity, Frechet Inception Distance, and Similarity Index Measure metrics and showed promising results in fully automated background person removal task. The method is evaluated on two video object segmentation datasets (DAVIS indicating respective values of 0.02, FID of 5.01 and SSIM of 0.79 and YouTube-VOS, resulting in 0.03, 6.22, 0.78 respectively) as well a database of 66 distinct video sequences of people behind a desk in an office environment (0.02, 4.01, and 0.78 respectively).

Abstract Background Information is essential for growth; without it, little can be accomplished. Data gathering has seen significant changes throughout the previous few centuries because of the certain transitory medium. The look and style of information transference are affected by the employment of new and emerging technologies, some of which are efficient, others are reliable, and many more are quick and effective, but a few were disappointing for various reasons. Aims This study aims at using TextBlob and VADER analyser with historical tweets, to analyse emotional responses to the coronavirus pandemic (COVID-19). It shows us how much of a sociological, environmental, and economic impact it has in Nigeria, among other things. This study would be a tremendous step forward for students, researchers, and scholars who want to advance in fields like data science, machine learning, and deep learning. Methodology The hashtag ‘COVID-19' was used to collect 1,048,575 tweets from Twitter. The tweets were pre-processed with a Twitter tokenizer, while TextBlob and Valence Aware Dictionary for Sentiment Reasoning (VADER) were used for text mining and sentiment analysis, respectively. Topic modelling was done with Latent Dirichlet Allocation and visualized with Multidimensional scaling. Results The result of the VADER sentiment returned 39.8%, 31.3%, and 28.9%, positive, neutral, and negative sentiment, respectively, while the result of the TextBlob sentiment returned 46.0%, 36.7%, and 17.3%, neutral, positive, and negative sentiment, respectively. Conclusion With all of this, information from social media may be used to help organizations, governments, and nations around the world make smart and effective decisions about how to restrict and limit the negative effects of COVID-19. Also, know the opinion and challenges of people, then deal with the problem of misinformation. It is concluded that with popular belief a significant number of the populace regards COVID-19 as a virus that has come to stay, some believe it will eventually be conquered.

With the environmental and societal changes, the achievement of sustainable development goals (SDGs) and the realization of sustainability in general is now more important than ever. Through a bibliometric analysis and scientific mapping analysis, this study aims to explore and provide a review regarding the role of artificial intelligence (AI), the Internet of Things (IoT), and artificial intelligence of things (AIoT) in realizing sustainable development and achieving SDGs. AIoT can be defined as the combination of AI with IoT to create more efficient and data-driven interconnected, intelligent, and autonomous IoT systems and infrastructure that use AI methods and algorithms. The analysis involved 9182 documents from Scopus and Web of Science (WoS) from 1989 to 2022. Descriptive statistics of the related documents and the annual scientific production were explored. The most relevant and impactful authors, articles, outlets, affiliations, countries, and keywords were identified. The most popular topics and research directions throughout the years and the advancement of the field and the research focus were also examined. The study examines the results, discusses the main findings, presents open issues, and suggests new research directions. Based on the results of this study, AIoT emerged as an important contributor in ensuring sustainability and in achieving SDGs.

Background Recent guidelines have emphasized the use of medical management, early diagnosis, and a multidisciplinary team to effectively treat patients with critical limb ischemia (CLI). Previous literature briefly highlighted the current racial disparities in its intervention. Herein, we analyze the trend over a 14‐year time period to investigate whether the disparities gap in CLI management is closing. Methods and Results The National Inpatient Sample was queried between 2005 and 2018 for hospitalizations involving CLI. Nontraumatic amputations and revascularization were identified. Utilization trends of these procedures were compared between races (White, Black, Hispanic, Asian and Pacific Islander, Native American, and Other). Multivariable regression assessed differences in race regarding procedure usage. There were 6 904 562 admissions involving CLI in the 14‐year study period. The rate of admissions in White patients who received any revascularization decreased by 0.23% (P

The Internet of Things (IoT) refers to the collection of all those devices that could connect to the Internet to collect and share data. The introduction of varied devices continues to grow tremendously, posing new privacy and security risks—the proliferation of Internet connections and the advent of new technologies such as the IoT. Various and sophisticated intrusions are driving the IoT paradigm into computer networks. Companies are increasing their investment in research to improve the detection of these attacks. By comparing the highest rates of accuracy, institutions are picking intelligent procedures for testing and verification. The adoption of IoT in the different sectors, including health, has also continued to increase in recent times. Where the IoT applications became well known for technology researchers and developers. Unfortunately, the striking challenge of IoT is the privacy and security issues resulting from the energy limitations and scalability of IoT devices. Therefore, how to improve the security and privacy challenges of IoT remains an important problem in the computer security field. This paper proposes a machine learning-based intrusion detection system (ML-IDS) for detecting IoT network attacks. The primary objective of this research focuses on applying ML-supervised algorithm-based IDS for IoT. In the first stage of this research methodology, feature scaling was done using the Minimum-maximum (min–max) concept of normalization on the UNSW-NB15 dataset to limit information leakage on the test data. This dataset is a mixture of contemporary attacks and normal activities of network traffic grouped into nine different attack types. In the next stage, dimensionality reduction was performed with Principal Component Analysis (PCA). Lastly, six proposed machine learning models were used for the analysis. The experimental results of our findings were evaluated in terms of validation dataset, accuracy, the area under the curve, recall, F1, precision, kappa, and Mathew correlation coefficient (MCC). The findings were also benchmarked with the existing works, and our results were competitive with an accuracy of 99.9% and MCC of 99.97%.

The recent innovations in network application and the internet have made data and network security the major role in data communication system development. Cryptography is one of the outstanding and powerful tools for ensuring data and network security. In cryptography, randomization of encrypted data increases the security level as well as the Computational Complexity of cryptographic algorithms involved. This research study provides encryption algorithms that bring confidentiality and integrity based on two algorithms. The encryption algorithms include a well-known RSA algorithm (1024 key length) with an enhanced bit insertion algorithm to enhance the security of RSA against different attacks. The security classical RSA has depreciated irrespective of the size of the key length due to the development in computing technology and hacking system. Due to these lapses, we have tried to improve on the contribution of the paper by enhancing the security of RSA against different attacks and also increasing diffusion degree without increasing the key length. The security analysis of the study was compared with classical RSA of 1024 key length using mathematical evaluation proofs, the experimental results generated were compared with classical RSA of 1024 key length using avalanche effect in (%) and computational complexity as performance evaluation metrics. The results show that RBMRSA is better than classical RSA in terms of security but at the cost of execution time.

Due to technological advancements and consumer demands, online shopping creates new features and adapts to new standards. A robust customer satisfaction prediction model concerning trust and privacy platforms can encourage an organization to make better decisions about its service and quality. This study presented an approach to predict consumer satisfaction using the blockchain-based framework combining the Multi-Dimensional Naive Bayes-K Nearest Neighbor (MDNB-KNN) and the Multi-Objective Logistic Particle Swarm Optimization Algorithm (MOL-PSOA). A regression model is employed to quantify the impact of various production factors on customer satisfaction. The proposed method yields better levels of measurement for customer satisfaction (98%), accuracy (95%), necessary time (60%), precision (95%), and recall (95%) compared to existing studies. Measuring consumer satisfaction with a trustworthy platform facilitates to development of the conceptual and practical distinctions influencing customers' purchasing decisions.

As the topic of sustainable development continues to prominence in global affairs, the case for renewable energy has never been stronger. To be regarded as a perfect alternative to conventional (non-renewable) energy sources in many climes, renewable energy, such as solar and wind, shows promise when considering concepts like grid parity. A significant number of studies have been devoted to understanding the concept. However, only a few studies have committed themselves to analysing the research activity carried out on it. This paper will present a bibliometric and empirical review of worldwide grid parity, energy transition, and electricity cost research. To situate the progress in this research area, a detailed search of Scopus was used to identify and situate research development in the field from 1965 until 2021. Using the data extracted from Scopus and VOSviewer for analysis, we explore different aspects of the publications, such as the volume, growth rate, and coverage of published documents, the most influential research papers and journals in this research area, and the most studied research themes in recent years. We also discuss Governmental policies in developed and developing economies that have accelerated the attainment of Grid parity in certain countries. Also, an empirical review of top-down, bottom-up, and artificial neural network approaches to evaluating grid parity was conducted. The study revealed a steady increase in the research articles focused on grid parity, energy transition, and electricity cost research from 2006. The geographic distribution of the publications shows that most of the publications on the subject originated from the USA, Germany, China, United Kingdom, and Spain, raking in 42.2% of the publications. Also, the top 7 authors with the highest document count from Scopus are from Finland, which coincidentally is one of the countries making significant progress in Grid parity attainment. Of the total document count from Scopus, only 0.02% are papers published from African Countries. Could this reluctance to publish research findings on energy transition be one of the reasons for the slow progression of sustainable energy for all in Africa? Therefore, it is imperative now more than ever for more research focusing on the attainment of grid parity, energy transition, and electricity costs for developing countries to be brought to the fore. This article provides a review of state-of-the-art research on the attainment of grid parity and energy transition with a focus on the Levelized Cost of Electricity (LCOE) models of renewable energy sources.

Social Media today has become the most relevant and affordable platform to express one's views in real-time. The #Endsars protest in Nigeria and the COVID-19 pandemic have proven how important and reliant both government agencies and individuals are on social media. This research uses tweets collected from Twitter API to identify and classify transportation disasters in Nigeria. Information such as the user, location, and time of the tweet makes identification and classification of transportation disasters available in real-time. Bidirectional Encoder Representations from Transformers (BERT) uses a transformer that includes two separate mechanisms, a decoder that produces a prediction for the task and an encoder that reads the text input. It learns contextual relations between words (sub-words) in a text. This research applied BERT with a combination of AdamW optimizers. AdamW is an improved version of stochastic gradient descent that computes an adaptive learning rate for each parameter. Our proposed model produces an accuracy of 82%. It was concluded that our approach outperformed the existing algorithm: BERT having an accuracy of 64%.

Despite its status as one of the most ancient sectors worldwide, agriculture continues to be a fundamental cornerstone of the global economy. Nevertheless, it faces obstacles such as a lack of trust, difficulties in tracking, and inefficiencies in managing the supply chain. This article examines the potential of blockchain technology (BCT) to alter the agricultural industry by providing a decentralized, transparent, and unchangeable solution to meet the difficulties it faces. The initial discussion provides an overview of the challenges encountered by the agricultural industry, followed by a thorough analysis of BCT, highlighting its potential advantages. Following that, the article explores other agricultural uses for blockchain technology, such as managing supply chains, verifying products, and processing payments. In addition, this paper examines the constraints and challenges related to the use of blockchain technology in agriculture, including issues such as scalability, legal frameworks, and interoperability. This paper highlights the potential of BCT to transform the agricultural industry by offering a transparent and secure platform for managing the supply chain. Nevertheless, it emphasizes the need for involving stakeholders, having clear legislation, and possessing technical skills in order to achieve effective implementation. This work utilizes a systematic literature review using the PRISMA technique and applies meta-analysis as the research methodology, enabling a thorough investigation of the present information available. The results emphasize the significant and positive effect of BCT on agriculture, emphasizing the need for cooperative endeavors among governments, industry pioneers, and technology specialists to encourage its extensive implementation and contribute to the advancement of a sustainable and resilient food system.

Regulatory authorities, consumers, and producers alike are alarmed by the issue of food safety, which is a matter of international concern. The conventional approaches utilized in food quality management demonstrate deficiencies in their capacity to sufficiently address issues related to traceability, transparency, and accountability. The emergence of blockchain technology (BCT) has provided a feasible approach to tackle the challenge of regulating food safety. This research paper presents a methodology for implementing blockchain technology to establish risk traceability in the context of monitoring, tracing, and authenticating agricultural products. The proposed system underwent a comprehensive evaluation, which placed significant emphasis on simulation parameters and assessment standards. The aim of the study was to demonstrate the effectiveness of the system through the assessment of various quantitative metrics, including throughput, latency, and resource utilization. The Hyperledger Fabric and Hyperledger Caliper were employed in the formulation and assessment of algorithms intended for agricultural supply chain management. The configuration comprising two entities and two peers achieved the highest write throughput (205.87 transactions per second; TPS), thereby demonstrating the network’s effective transaction processing capability. In a two-organization, two-peer system, the mean latency for read operations exhibited variability spanning from 0.037 to 0.061 s, contingent upon the transaction rates and accounting for the duration needed for network processing and validation. The results were visually depicted, offering a distinct demonstration of the system’s efficacy under various conditions. This study presents a quantitative analysis that illustrates the efficacy of the blockchain system in enhancing the traceability of agricultural products across the entire supply chain. The results of this research suggest that the implementation of blockchain technology could potentially enhance both the security and efficacy of food supply management.

In recent years, BCT has garnered significant attention from researchers worldwide. The technology in question is a distributed database system characterised by its decentralised nature and lack of reliability. BCT has been widely adopted by numerous governments and scholars across various sectors for a number of years. Blockchain technology also involves highly innovative and advanced concepts. Given the increasing interest among scholars in the academic community regarding the agrifood supply chain, the objective of this study was to investigate BCT and its potential for application in the fields of food and agriculture. This research paper presents a bibliometric analysis of articles on the utilisation of BCT in the fields of food and agriculture. This study discusses scholarly articles that have been published in esteemed academic journals and conferences. Through our bibliometric analysis, we aimed to discern the recurring trends and themes within the research on BCT in relation to agrifood systems. Furthermore, this study examines a diverse array of research domains, prominent scholarly publications, leading publishing platforms, prominent funding institutions, and the prospective trajectory of future research. This study also presents the prominent patterns and themes within this field through an analysis of the most influential scholarly articles, authors, countries, and keywords found in the existing literature. Hence, this research employed various analytical techniques, including analyzing the co-occurrence of author keywords, bibliographic coupling analysis, network view map analysis, and co-citation analysis. This study holds promise as a valuable learning resource for aspiring researchers seeking to acquire compelling and pertinent information about research outcomes from studies on the utilisation of BCT in the field of smart agriculture.

Efforts to raise the bar of higher education so as to respond to dynamic societal/industry needs have led to a number of initiatives, including artificial neural network (ANN) based educational data mining (EDM) inclusive. With ANN-based EDM, humongous amount of student data in higher institutions could be harnessed for informed academic advisory that promotes adaptive learning for purposes of student retention, student progression, and cost saving. Mining students’ data optimally requires predictive data mining tool and machine learning technique like ANN. However, despite acknowledging the capability of ANN-based EDM for efficiently classifying students’ learning behavior and accurately predicting students’ performance, the concept has received less than commensurate attention in the literature. This seems to suggest that there are gaps and challenges confronting ANN-based EDM in higher education. In this study, we used the systematic literature review technique to gauge the pulse of researchers from the viewpoint of modeling, learning procedure, and cost function optimization using research studies. We aim to unearth the gaps and challenges with a view to offering research direction to upcoming researchers that want to make invaluable contributions to this relatively new field. We analyzed 190 studies conducted in 2010–2018. Our findings reveal that hardware challenges, training challenges, theoretical challenges, and quality concerns are the bane of ANN-based EDM in higher education and offer windows of opportunities for further research. We are optimistic that advances in research along this direction will make ANN-based EDM in higher education more visible and relevant in the quest for higher education-driven sustainable development.

Cyberattacks are becoming more sophisticated and ubiquitous. Cybercriminals are inevitably adopting Artificial Intelligence (AI) techniques to evade the cyberspace and cause greater damages without being noticed. Researchers in cybersecurity domain have not researched the concept behind AI-powered cyberattacks enough to understand the level of sophistication this type of attack possesses. This paper aims to investigate the emerging threat of AI-powered cyberattacks and provide insights into malicious used of AI in cyberattacks. The study was performed through a three-step process by selecting only articles based on quality, exclusion, and inclusion criteria that focus on AI-driven cyberattacks. Searches in ACM, arXiv Blackhat, Scopus, Springer, MDPI, IEEE Xplore and other sources were executed to retrieve relevant articles. Out of the 936 papers that met our search criteria, a total of 46 articles were finally selected for this study. The result shows that 56% of the AI-Driven cyberattack technique identified was demonstrated in the access and penetration phase, 12% was demonstrated in exploitation, and command and control phase, respectively; 11% was demonstrated in the reconnaissance phase; 9% was demonstrated in the delivery phase of the cybersecurity kill chain. The findings in this study shows that existing cyber defence infrastructures will become inadequate to address the increasing speed, and complex decision logic of AI-driven attacks. Hence, organizations need to invest in AI cybersecurity infrastructures to combat these emerging threats.

Cyber attacks are increasing rapidly due to advanced digital technologies used by hackers. In addition, cybercriminals are conducting cyber attacks, making cyber security a rapidly growing field. Although machine learning techniques worked well in solving large-scale cybersecurity problems, an emerging concept of deep learning (DL) that caught on during this period caused information security specialists to improvise the result. The deep learning techniques analyzed in this study are convolution neural networks, recurrent neural networks, and deep neural networks in the context of cybersecurity.A framework is proposed, and a real-time laboratory setup is performed to capture network packets and examine this captured data using various DL techniques. A comparable interpretation is presented under the DL techniques with essential parameters, particularly accuracy, false alarm rate, precision, and detection rate. The DL techniques experimental output projects improvise the performance of various real-time cybersecurity applications on a real-time dataset. CNN model provides the highest accuracy of 98.64% with a precision of 98% with binary class. The RNN model offers the second-highest accuracy of 97.75%. CNN model provides the highest accuracy of 98.42 with multiclass class. The study shows that DL techniques can be effectively used in cybersecurity applications. Future research areas are being elaborated, including the potential research topics to improve several DL methodologies for cybersecurity applications.

Malaria fever is a potentially fatal disease caused by the Plasmodium parasite. Identifying Plasmodium parasites in blood smear images can help diagnose malaria fever rapidly and precisely. According to the World Health Organization (WHO), there were 241 million malaria cases and 627 000 deaths worldwide in 2020, while 95% of malaria cases and 96% of malaria deaths occurred in Africa. Also in Africa, children that are less than five years old accounted for an estimated 80% of all malaria deaths. To address the menace of malaria, this paper proposes a novel deep learning model, called a data augmentation convolutional neural network (DACNN), trained by reinforcement learning to tackle this problem. The performance of the proposed DACNN model is compared with CNN and directed acyclic graph convolutional neural network (DAGCNN) models. Results show that DACNN outperforms previous studies in processing and classification images. It achieved 94.79% classification accuracy in malaria blood sample images of balanced class dataset obtained from the Kaggle dataset. The proposed model can serve as an effective tool for the detection of malaria parasites in blood smear images.

To guarantee that software does not fail, software quality assurance (SQA) teams play a critical part in the software development procedure. As a result, prioritizing SQA activities is a crucial stage in SQA. Software defect prediction (SDP) is a procedure for recognizing high-risk software components and determining the influence of software measurements on the likelihood of software modules failure. There is a continuous need for sophisticated and better SDP models. Therefore, this study proposed the use of dagging-based and baseline classifiers to predict software defects. The efficacy of the dagging-based SDP model for forecasting software defects was examined in this study. The models employed were naïve Bayes (NB), decision tree (DT), and k-nearest neighbor (kNN), and these models were used on nine NASA datasets. Findings from the experimental results indicated the superiority of SDP models based on dagging meta-learner. Dagging-based models significantly outperformed experimented baseline classifiers built on accuracy, the area under the curve (AUC), F-measure, and precision-recall curve (PRC) values. Specifically, dagging-based NB, DT, and kNN models had +6.62%, +3.26%, and +4.14% increments in average accuracy value over baseline NB, DT, and kNN models. Therefore, it can be concluded that the dagging meta-learner can advance the recognition performances of SDP methods and should be considered for SDP processes.

The advancement in technology has led to the integration of internet-connected devices and systems into emergency management and response, known as the Internet of Emergency Services (IoES). This integration has the potential to revolutionize the way in which emergency services are provided, by allowing for real-time data collection and analysis, and improving coordination among various agencies involved in emergency response. This paper aims to explore the use of IoES in emergency response and disaster management, with an emphasis on the role of sensors and IoT devices in providing real-time information to emergency responders. We will also examine the challenges and opportunities associated with the implementation of IoES, and discuss the potential impact of this technology on public safety and crisis management. The integration of IoES into emergency management holds great promise for improving the speed and efficiency of emergency response, as well as enhancing the overall safety and well-being of citizens in emergency situations. However, it is important to understand the possible limitations and potential risks associated with this technology, in order to ensure its effective and responsible use. This paper aims to provide a comprehensive understanding of the Internet of Emergency Services and its implications for emergency response and disaster management.

Patients with hereditary hemorrhagic telangiectasia (HHT) have arteriovenous malformations (AVMs) with genetic mutations involving the activin-A receptor like type 1 (ACVRL1 or ALK1) and endoglin (ENG). Recent studies have shown that Neuropilin-1 (NRP-1) inhibits ALK1. We investigated the expression of NRP-1 in livers of patients with HHT and found that there was a significant reduction in NRP-1 in perivascular smooth muscle cells (SMCs). We used Nrp1SM22KO mice (Nrp1 was ablated in SMCs) and found hemorrhage, increased immune cell infiltration with a decrease in SMCs, and pericyte lining in lungs and liver in adult mice. Histologic examination revealed lung arteriovenous fistulas (AVFs) with enlarged liver vessels. Evaluation of the retina vessels at P5 from Nrp1SM22KO mice demonstrated dilated capillaries with a reduction of pericytes. In inflow artery of surgical AVFs from the Nrp1SM22KO versus WT mice, there was a significant decrease in Tgfb1, Eng, and Alk1 expression and phosphorylated SMAD1/5/8 (pSMAD1/5/8), with an increase in apoptosis. TGF-β1–stimulated aortic SMCs from Nrp1SM22KO versus WT mice have decreased pSMAD1/5/8 and increased apoptosis. Coimmunoprecipitation experiments revealed that NRP-1 interacts with ALK1 and ENG in SMCs. In summary, NRP-1 deletion in SMCs leads to reduced ALK1, ENG, and pSMAD1/5/8 signaling and reduced cell death associated with AVM formation.

Abstract Percutaneous transluminal angioplasty (PTA) of stenotic arteriovenous fistulas (AVFs) is performed to maintain optimal function and patency. The one-year patency rate is 60% because of venous neointimal hyperplasia (VNH) and venous stenosis (VS) formation. Immediate early response gene X-1 (Iex-1) also known as Ier3 increases in response to wall shear stress (WSS), and can cause VNH/VS formation in murine AVF. In human stenotic samples from AVFs, we demonstrated increased gene expression of Ier3. We hypothesized that 1α, 25-dihydroxyvitamin D3, an inhibitor of IER3 delivered as 1α, 25-dihydroxyvitamin D3 encapsulated in poly lactic-co-glycolic acid (PLGA) nanoparticles loaded in Pluronic F127 hydrogel (1,25 NP) to the adventitia of the stenotic outflow vein after PTA would decrease VNH/VS formation by reducing Ier3 and chemokine (C–C motif) ligand 2 (Ccl2) expression. In our murine model of AVF stenosis treated with PTA, increased expression of Ier3 and Ccl2 was observed. Using this model, PTA was performed and 10-μL of 1,25 NP or control vehicle (PLGA in hydrogel) was administered by adventitial delivery. Animals were sacrificed at day 3 for unbiased whole genome transcriptomic analysis and at day 21 for immunohistochemical analysis. Doppler US was performed weekly after AVF creation. At day 3, significantly lower gene expression of Ier3 and Ccl2 was noted in 1,25 NP treated vessels. Twenty-one days after PTA, 1,25 NP treated vessels had increased lumen vessel area, with decreased neointima area/media area ratio and cell density compared to vehicle controls. There was a significant increase in apoptosis, with a reduction in CD68, F4/80, CD45, pro-inflammatory macrophages, fibroblasts, Picrosirius red, Masson’s trichrome, collagen IV, and proliferation accompanied with higher wall shear stress (WSS) and average peak velocity. IER3 staining was localized to CD68 and FSP-1 (+) cells. After 1,25 NP delivery, there was a decrease in the proliferation of α-SMA (+) and CD68 (+) cells with increase in the apoptosis of FSP-1 (+) and CD68 (+) cells compared to vehicle controls. RNA sequencing revealed a decrease in inflammatory and apoptosis pathways following 1,25 NP delivery. These data suggest that adventitial delivery of 1,25 NP reduces VNH and venous stenosis formation after PTA.

In the software development process, traceability links between unit tests and code are not explicitly maintained, and dependencies in most cases are manually identified. As a result, a large amount of effort and time is required during the comprehension process to establish the links between these artifacts. Although there are several methods that can infer such links based on different phenomenons, these methods usually produce different set of traceability links. This work expands upon previous traceability link recovery and visualization studies by implementing a combination of traceability recovery methods that automatically retrieve the links, and visualizing them to help developers to overview the links inferred by various recovery techniques, and also to select the right relations for analyses. The results of the usability study show that the visualization model presented herein can effectively support browsing, comprehension, and maintenance of Test-to Code Traceability (TCT) links in a system with enhanced efficiency, as well as visualization of TCT links from multiple sources is better than a visualization of single source of traceability links.

The challenge of Nigeria’s food insecurity in the era of the Covid-19 pandemic, insecurity, climate change, population growth, food wastage, etc., is a demanding task. This study addresses Nigeria’s food insecurity challenges by adopting agriculture 4.0 and commercial farming. Using data from six digital libraries, the Nigerian Bureau of Statistics, and other internet sources, we conducted a Systematic Literature Review (SLR using PRISMA) on Nigeria’s agriculture, food security, and agriculture 4.0. Our results show Nigeria’s current agricultural state, threats to food security, and modern digital agriculture technologies. We adapted our SLR findings to develop an implementation framework for agriculture 4.0 in solving Nigeria’s food insecurity challenge in the post-Covid-19 era. Our proposed framework integrates precision agriculture in Nigeria’s food production and the necessary enabling digital technologies in the agri-food supply chain. We analyzed the critical implementation considerations during each agri-food supply chain stage of farming inputs, farming scale, farming approach, farming operation, food processing, food preservation/storage, distribution/logistics, and the final consumers. This study will help researchers, investors, and the government address food security in Nigeria. The implementation of agriculture 4.0 will substantially contribute to SDG 2 (zero hunger), SDG 3 (good health and well-being), and SDG 8 (decent work and economic growth) of #Envision 2030 of the United Nations, for the benefit of Nigeria, Africa, and the entire world.

Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. The development of the Internet of Medical Things (IoMT) has made it possible for healthcare technology to offer the general public efficient medical services and make a significant contribution to patients’ recoveries. By using IoMT to diagnose and examine BreakHis v1 400× breast cancer histology (BCH) scans, disorders may be quickly identified and appropriate treatment can be given to a patient. Imaging equipment having the capability of auto-analyzing acquired pictures can be used to achieve this. However, the majority of deep learning (DL)-based image classification approaches are of a large number of parameters and unsuitable for application in IoMT-centered imaging sensors. The goal of this study is to create a lightweight deep transfer learning (DTL) model suited for BCH scan examination and has a good level of accuracy. In this study, a lightweight DTL-based model “MobileNet-SVM”, which is the hybridization of MobileNet and Support Vector Machine (SVM), for auto-classifying BreakHis v1 400× BCH images is presented. When tested against a real dataset of BreakHis v1 400× BCH images, the suggested technique achieved a training accuracy of 100% on the training dataset. It also obtained an accuracy of 91% and an F1-score of 91.35 on the test dataset. Considering how complicated BCH scans are, the findings are encouraging. The MobileNet-SVM model is ideal for IoMT imaging equipment in addition to having a high degree of precision. According to the simulation findings, the suggested model requires a small computation speed and time.

The commonly accepted definition of sustainability considers the availability of relevant resources to make an activity feasible and durable while also recognizing users’ support as an essential part of the social side of sustainability. IoT represents a disruption in the general scenario of computing for both users and professionals. The real expansion and integration of applications based on IoT depend on our capacity of exploring the necessary skills and professional profiles that are essential for the implementation of IoT projects, but also on the perception of relevant aspects for users, e.g., privacy, legal, IPR, and security issues. Our participation in several EU-funded projects with a focus on this area has enabled the collection of information on both sides of IoT sustainability through surveys but also by collecting data from a variety of sources. Thanks to these varied and complementary sources of information, this article will explore the user and professional aspects of the sustainability of the Internet of Things in practice.

Rationale & Objective: To facilitate the process of dialysis for patients with kidney failure, an arteriovenous fistula (AVF) is created using either a surgical or percutaneous approach. We sought to compare the efficacy and procedural outcomes in creating an AVF percutaneously using Ellipsys (Avenu Medical) or WavelinQ (Becton Dickinson Medical) with surgery in all patients with kidney failure requiring a permanent AVF for dialysis. Study Design: Systematic review and meta-analysis. Setting & Study Populations: All patients requiring a permanent AVF for dialysis. Selection Criteria for Studies: We included studies that compared either the Ellipsys device or WavelinQ directly with surgery to create an AVF for long-term dialysis. Data Extraction: Two reviewers independently reviewed the studies and extracted the data. Conflicts were resolved with a discussion and approval from the senior author. Analytical Approach: Fixed-effects or random-effects models were used to pool the fixed sizes and 95% CIs based on the level of heterogeneity. Results: There was no statistically significant difference observed between surgical AVF and endovascular AVF when comparing the primary outcomes of procedural success (OR = 1.44; 95% CI, 0.35, 5.88; P = 0.61; I2 = 0%), complications (OR = 0.28; 95% CI, 0.06, 1.46; P = 0.13; I2 = 69%), and the secondary outcomes of interest that included follow-up time (mean difference [MD] = −17.71; 95% CI, −189.53, 154.12; P = 0.84; I2 = 94%), failure rate (OR = 1.03; 95% CI, 0.21, 5.13; P = 0.97; I2 = 85%), and time to 2-needle cannulation (MD = −5.40; 95% CI, −38.88, 28.08; P = 0.75; I2 = 0%). However, a statistically significant difference was seen among the 2 groups for procedural time (MD = −54.25; 95% CI, −59.78, −48.71; P < 0.001; I2 = 98%), number of interventions needed to maintain patency (OR = 1.73; 95% CI, 1.22, 2.45; P < 0.01; I2 = 94%), and primary patency rate (OR = 0.34; 95% CI, 0.23,0.52; P < 0.001; I2 = 0%). Limitations: The total number of studies included in this review was limited, with 3 of the 4 included studies being retrospective and only 1 being prospective. There was a lack of heterogeneity and randomization. Conclusions: Percutaneous fistula creation using Ellipsys or WavelinQ is a unique and safe alternative with outcomes comparable to surgery. Future studies are needed, including observational studies in current clinical practice, to evaluate the efficacy and outcomes of endovascular AVF creation in clinical populations.

Stock prediction is one of the emerging applications in the field of data science which help the companies to make better decision strategy. Machine learning models play a vital role in the field of prediction. In this paper, we have proposed various machine learning models which predicts the stock price from the real-time streaming data. Streaming data has been a potential source for real-time prediction which deals with continuous ow of data having information from various sources like social networking websites, server logs, mobile phone applications, trading oors etc. We have adopted the distributed platform, Spark to analyze the streaming data collected from two different sources as represented in two case studies in this paper. The first case study is based on stock prediction from the historical data collected from Google finance websites through NodeJs and the second one is based on the sentiment analysis of Twitter collected through Twitter API available in Stanford NLP package. Several researches have been made in developing models for stock prediction based on static data. In this work, an effort has been made to develop scalable, fault tolerant models for stock prediction from the real-time streaming data. The Proposed model is based on a distributed architecture known as Lambda architecture. The extensive comparison is made between actual and predicted output for different machine learning models. Support vector regression is found to have better accuracy as compared to other models. The historical data is considered as a ground truth data for validation.

The need to make good use of resources has allowed metaheuristics to become a tool to achieve this goal. There are a number of complex problems to solve, among which is the Set-Covering Problem, which is a representation of a type of combinatorial optimization problem, which has been applied to several real industrial problems. We use a binary version of the optimization algorithm based on teaching and learning to solve the problem, incorporating various binarization schemes, in order to solve the binary problem. In this paper, several binarization techniques are implemented in the teaching/learning based optimization algorithm, which presents only the minimum parameters to be configured such as the population and number of iterations to be evaluated. The performance of metaheuristic was evaluated through 65 benchmark instances. The results obtained are promising compared to those found in the literature.

This paper examines the situation of e-commerce and m-commerce in SMEs in the European Union and analyses if their behaviour in this field is determined by a series of factors, such as the size of the company, the type of business, turnover, etc. which may also influence their needs of specific training for their employees. This study is based on an online survey responded by 674 SMEs from eight different countries in Europe, aimed at providing training assistance to SMEs to support their efforts towards m-commerce. The objective is the analysis of the skills and knowledge to be recommended to employees of SMEs for a better preparation to implement the companies' strategy of m-commerce. Although analysis of results from survey has supported the initial goal of developing training courses for m-commerce which include different economic, legal and technical topics, it has mainly provided very relevant conclusions on the factors which impact the adoption of e-commerce and m-commerce by SMEs.

Risk-driven requirements elicitation represents an approach that allows assignment of appropriate countermeasure for the protection of the Information System (IS) depending on the risk level. Elicitation of safety requirements based on risk analysis is essential for those IS which will run on the open and dynamic Internet platform. Traditionally, misuse cases are used to find the weak points of an IS but cannot differentiate between the weak point that can lead to lenient hazard and/or serious hazard. In this paper, we present an enhanced misuse case approach to support IS safety risk assessment at the early stages of software process. We extensively examined and identified concepts which constitute a modelling technique for IS safety risk assessment and build a conceptual model for achieving IS safety risk assessment during the requirement analysis phase of software process. The risk assessment process follows an approach of consequential analysis based on misuse cases for safety hazard identification and qualitative risk measurement. The safety requirements are elicited according to the results of the risk assessment. A medical IS is used as a case study to validate the proposed model.

Background: Refactoring is changing a software system without affecting the software functionality. The current researchers aim i to identify the appropriate method(s) or class(s) that needs to be refactored in object-oriented software. Ensemble learning helps to reduce prediction errors by amalgamating different classifiers and their respective performances over the original feature data. Other motives are added in this paper regarding several ensemble learners, errors, sampling techniques, and feature selection techniques for refactoring prediction at the class level. Objective: This work aims to develop an ensemble-based refactoring prediction model with structural identification of source code metrics using different feature selection techniques and data sampling techniques to distribute the data uniformly. Our model finds the best classifier after achieving fewer errors during refactoring prediction at the class level. Methodology: At first, our proposed model extracts a total of 125 software metrics computed from object-oriented software systems processed for a robust multi-phased feature selection method encompassing Wilcoxon significant text, Pearson correlation test, and principal component analysis (PCA). The proposed multi-phased feature selection method retains the optimal features characterizing inheritance, size, coupling, cohesion, and complexity. After obtaining the optimal set of software metrics, a novel heterogeneous ensemble classifier is developed using techniques such as ANN-Gradient Descent, ANN-Levenberg Marquardt, ANN-GDX, ANN-Radial Basis Function; support vector machine with different kernel functions such as LSSVM-Linear, LSSVM-Polynomial, LSSVM-RBF, Decision Tree algorithm, Logistic Regression algorithm and extreme learning machine (ELM) model are used as the base classifier. In our paper, we have calculated four different errors i.e., Mean Absolute Error (MAE), Mean magnitude of Relative Error (MORE), Root Mean Square Error (RMSE), and Standard Error of Mean (SEM). Result: In our proposed model, the maximum voting ensemble (MVE) achieves better accuracy, recall, precision, and F-measure values (99.76, 99.93, 98.96, 98.44) as compared to the base trained ensemble (BTE) and it experiences less errors (MAE = 0.0057, MORE = 0.0701, RMSE = 0.0068, and SEM = 0.0107) during its implementation to develop the refactoring model. Conclusions: Our experimental result recommends that MVE with upsampling can be implemented to improve the performance of the refactoring prediction model at the class level. Furthermore, the performance of our model with different data sampling techniques and feature selection techniques has been shown in the form boxplot diagram of accuracy, F-measure, precision, recall, and area under the curve (AUC) parameters.

Posture detection targets toward providing assessments for the monitoring of the health and welfare of humans have been of great interest to researchers from different disciplines. The use of computer vision systems for posture recognition might result in useful improvements in healthy aging and support for elderly people in their daily activities in the field of health care. Computer vision and pattern recognition communities are particularly interested in fall automated recognition. Human sensing and artificial intelligence have both paid great attention to human posture detection (HPD). The health status of elderly people can be remotely monitored using human posture detection, which can distinguish between positions such as standing, sitting, and walking. The most recent research identified posture using both deep learning (DL) and conventional machine learning (ML) classifiers. However, these techniques do not effectively identify the postures and overfits of the model overfits. Therefore, this study suggested a deep convolutional neural network (DCNN) framework to examine and classify human posture in health monitoring systems. This study proposes a feature selection technique, DCNN, and a machine learning technique to assess the previously mentioned problems. The InceptionV3 DCNN model is hybridized with SVM ML and its performance is compared. Furthermore, the performance of the proposed system is validated with other transfer learning (TL) techniques such as InceptionV3, DenseNet121, and ResNet50. This study uses the least absolute shrinkage and selection operator (LASSO)-based feature selection to enhance the feature vector. The study also used various techniques, such as data augmentation, dropout, and early stop, to overcome the problem of model overfitting. The performance of this DCNN framework is tested using benchmark Silhouettes of human posture and classification accuracy, loss, and AUC value of 95.42%, 0.01, and 99.35% are attained, respectively. Furthermore, the results of the proposed technology offer the most promising solution for indoor monitoring systems.

Container-based virtualization has gained significant popularity in recent years because of its simplicity in deployment and adaptability in terms of cloud resource provisioning. Containerization technology is the recent development in cloud computing systems that is more efficient, reliable, and has better overall performance than a traditional virtual machine (VM) based technology. Containerized clouds produce better performance by maximizing host-level resource utilization and using a load-balancing technique. To this end, this article concentrates on distributing the workload among all available servers evenly. In this paper, we propose a Grey Wolf Optimization (GWO) based Simulated Annealing approach to counter the problem of load balancing in the containerized cloud that also considers the deadline miss rate. We have compared our results with the Genetic and Particle Swarm Optimization algorithm and evaluated the proposed algorithms by considering the parameter load variation and makespan. Our experimental result shows that, in most cases, more than 97% of the tasks were meeting their deadline and the Grey Wolf Optimization Algorithm with Simulated Annealing (GWO-SA) performs better than all other approaches in terms of load variation and makespan.

Human posture classification (HPC) is the process of identifying a human pose from a still image or moving image that was recorded by a digicam. This makes it easier to keep a record of people’s postures, which is helpful for many things. The intricate surroundings that are depicted in the image, such as occlusion and the camera view angle, make HPC a difficult process. Consequently, the development of a reliable HPC system is essential. This study proposes the “DeneSVM”, an innovative deep transfer learning-based classification model that pulls characteristics from image datasets to detect and classify human postures. The paradigm is intended to classify the four primary postures of lying, bending, sitting, and standing. These positions are classes of sitting, bending, lying, and standing. The Silhouettes for Human Posture Recognition dataset has been used to train, validate, test, and analyze the suggested model. The DeneSVM model attained the highest test precision (94.72%), validation accuracy (93.79%) and training accuracy (97.06%). When the efficiency of the suggested model was validated using the testing dataset, it too had a good accuracy of 95%.

Background: The technological revolution has allowed users to exchange data and information in various fields, and this is one of the most prevalent uses of computer technologies. However, in a world where third parties are capable of collecting, stealing, and destroying information without authorization, cryptography remains the primary tool that assists users in keeping their information secure using various techniques. Blowfish is an encryption process that is modest, protected, and proficient, with the size of the message and the key size affecting its performance. Aim: the goal of this study is to design a modified Blowfish algorithm by changing the structure of the F function to encrypt and decrypt video data. After which, the performance of the normal and modified Blowfish algorithm will be obtained in terms of time complexity and the avalanche effect. Methods: To compare the encryption time and security, the modified Blowfish algorithm will use only two S-boxes in the F function instead of the four used in Blowfish. Encryption and decryption times were calculated to compare Blowfish to the modified Blowfish algorithm, with the findings indicating that the modified Blowfish algorithm performs better. Results: The Avalanche Effect results reveal that normal Blowfish has a higher security level for all categories of video file size than the modified Blowfish algorithm, with 50.7176% for normal Blowfish and 43.3398% for the modified Blowfish algorithm of 187 kb; hence, it is preferable to secure data and programs that demand a high level of security with Blowfish. Conclusions: From the experimental results, the modified Blowfish algorithm performs faster than normal Blowfish in terms of time complexity with an average execution time of 250.0 ms for normal Blowfish and 248.4 ms for the modified Blowfish algorithm. Therefore, it can be concluded that the modified Blowfish algorithm using the F-structure is time-efficient while normal Blowfish is better in terms of security.

The decision-making process for attaining Sustainable Development Goals (SDGs) can be enhanced through the use of predictive modelling. The application of predictive tools like deep neural networks (DNN) empowers stakeholders with quality information and promotes open data policy for curbing corruption. The anti-corruption drive is a cardinal component of SDG 16 which is aimed at strengthening state institutions and promoting social justice for the attainment of all 17 SDGs. This study examined the implementation of the SDGs in Nigeria and modelled the 2017 national corruption survey data using a DNN. We experimentally tested the efficacy of DNN optimizers using a standard image dataset from the Modified National Institute of Standards and Technology (MNIST). The outcomes validated our claims that predictive analytics could enhance decision-making through high-level accuracies as posted by the optimizers: Adam 98.2%; Adadelta 98.4%; SGD 94.9%; RMSProp 98.1%; Adagrad 98.1%.

The development of any country is closely related to its ability to provide access to electricity for productive labor. Many countries in sub-Saharan Africa have low electrification rates for commercial, industrial and residential consumers. This study focuses on Nigeria, which has one of the largest populations and economies in sub-Saharan Africa. Although Nigeria possesses abundant renewable energy resources that can increase electricity generation, it has suffered a significant setback in electricity generation. However, for Nigeria to become one of the leading industrialized countries by 2030, access to clean, reliable, and sustainable energy sources is vital (Vision 20: 2030). This study assesses the possibility of Nigeria developing and transitioning to the use of various energy sources. Additionally, this study evaluates greenhouse gas (GHG) mitigation plans and future trends in energy sustainability through multi-criteria decision analysis (MCDA), considering the technical, social, economic, and environmental dimensions of the sustainability structure. A total of twelve (12) sustainability indexes were taken into consideration; these consist of two (2) technical, three (3) social, three (3) environmental, and four (4) economic indicators. A scenario-based software called Long-range Energy Alternative Plan (LEAP) was used to integrate the analysis criteria and forecast a sustainable energy generation mix for the future. It considered three scenarios, namely: the business as usual scenario (BAU); renewables, natural gas and biomass scenario (RNB); and renewables and coal scenario (REC). It was concluded that the renewables, natural gas, and biomass scenario (RNB) is the best scenario to solve Nigeria’s energy problem based on the aim of the study.

Water and food are two of the most important commodities in the world, which makes agriculture crucial to mankind as it utilizes water (irrigation) to provide us with food. Climate change and a rapid increase in population have put a lot of pressure on agriculture which has a snowball effect on the earth's water resource, which has been proven to be crucial for sustainable development. The need to do away with fossil fuel in powering irrigation systems cannot be over emphasized due to climate change. Smart Irrigation systems powered by renewable energy sources (RES) have been proven to substantially improve crop yield and the profitability of agriculture. Here we show how the control and monitoring of a solar powered smart irrigation system can be achieved using sensors and environmental data from an Internet of Everything (IoE). The collected data is used to predict environment conditions using the Radial Basis Function Network (RBFN). The predicted values of water level, weather forecast, humidity, temperature and irrigation data are used to control the irrigation system. A web platform was developed for monitoring and controlling the system remotely.