Task offloading in distributed computing involves complex tradeoffs among delay, scalability, cost, and resource utilization. Cloud platforms face long communication delays, while edge nodes have constrained capacity. Static, rule-based schedulers cannot adapt to fluctuating loads or per-instance heterogeneity. Similarly, Reinforcement Learning (RL) schemes typically address only a single layer or assume homogeneous servers, overlooking the hierarchical and multi-instance nature of deployments. To address these challenges, we introduce a server-aware Proximal Policy Optimization (PPO) framework that performs fine-grained offloading across a three-tier (Edge, Regional, Cloud) multi-instance architecture. We formulate offloading as a Markov Decision Process whose state vector includes per-instance delay, CPU/memory utilization, network congestion, cost, and energy metrics. The PPO agent learns to offload tasks to the best server in real time. Our developed RegionalEdgeSimPy simulation shows that PPO agent makes optimal offloading choices for over 90% of tasks, keeping each server near; however, below a 70% utilization. This optimized decision making drives up to 66.9% delay reduction, 78.6% energy savings, and 47.8% cost reductions relative to cloud-only and edge-only baselines.

Blockchain is a disruptive technology that has attracted considerable attention from scholars. The blockchain underlies cryptocurrencies and has rapidly expanded to other areas, including financial transactions and social media networks. However, concerns regarding the information security of social media users still exist regarding blockchain technology. The literature on blockchain online social media (BOSM) networks is growing rapidly because of their critical role in securing users’ information privacy and security. Cybersecurity remains a challenge faced by users on social media networks. Since the publication of BOSM, blockchain has become a widely discussed method for users’ information security. This comprehensive review identifies peer-reviewed articles on BOSM that underpin smart contracts, social media challenges, and research gaps. In this work, Kitchenham’s review guidelines are followed to conduct an in-depth review of the use of blockchain technology in the social media network literature published between January 2016 and March 2024, which reveals a significant increase in publications over the last eight years. A search of major academic databases, including Springer, ScienceDirect, ACM, IEEE Xplore, World Scientific, Taylor & Francis, and Wiley Online, yielded a final pool of 158 articles. The findings of the review indicate key insights concerning the techniques and applications of blockchain technology and challenges for the public via social media networks such as Twitter, Facebook, and Google+. This paper identifies important challenges such as deploying smart contracts, user information privacy, a lack of platform support, users’ reactions to blockchain technology, privacy protection and compensation, security system validation, online disinformation, scalability, and miscellaneous challenges to blockchain technology. Additionally, this review suggests several future research directions to improve the role of blockchain technology in overcoming the challenges of privacy, security, reliability, scalability, and trust in the area of social media networks.

Software engineering, along with the incorporation of Artificial Intelligence (AI), has emerged as a new technological vantage point that has permanently changed classical development practices and processes for any phase and aspect of the software lifecycle. In particular, this systematic literature review, which includes 135 peer‐reviewed papers extracted from the years 2010 to 2025, follows PRISMA guidelines. It examines modern instances of AI‐based requirements analysis, automated code transformation, predictive system modeling, proactive fault monitoring and detection, and advanced project guidance systems. Technologies can be powerful tools for increasing productivity and effectiveness and strengthening the quality of software development while making technology more complex—technologically, organizationally, and ethically. The generalization, explainability, privacy and algorithmic bias challenges of the model are discussed in detail. This paper shows how AI is helping companies to predict defects, automatically identify errors and optimize the software development. It also highlights the significant adoption barriers to these technologies for organizations. The review combines new industry research with existing practice to offer practical guidance on how these implementation challenges can be overcome and the ethical use of AI can be promoted. In contrast to existing reviews concentrating on isolated stages, the study offers an integrated review through life phases, distinctive ethical frameworks and a roadmap for adoption. Takeaway: Sustainable AI deployment in SE needs interdisciplinary collaboration, ethical oversight, and a mixture of guidelines to balance technology efficiency with responsibility. The paper highlights that interdisciplinary cooperation and ethical framings are requirements to integrate AI into software engineering in a sustainable, straightforward way. This review can be utilized as a guide for authors, scientists/practitioners, and policymakers in articulating the intellectual‐practical gap.

Crowd-Sourced software development (CSSD) is getting a good deal of attention from the software and research community in recent times. One of the key challenges faced by CSSD platforms is the task selection mechanism which in practice, contains no intelligent scheme. Rather, rule-of-thumb or intuition strategies are employed, leading to biasness and subjectivity. Effort considerations on crowdsourced tasks can offer good foundation for task selection criteria but are not much investigated. Software development effort estimation (SDEE) is quite prevalent domain in software engineering but only investigated for in-house development. For open-sourced or crowdsourced platforms, it is rarely explored. Moreover, Machine learning (ML) techniques are overpowering SDEE with a claim to provide more accurate estimation results. This work aims to conjoin ML-based SDEE to analyze development effort measures on CSSD platform. The purpose is to discover development-oriented features for crowdsourced tasks and analyze performance of ML techniques to find best estimation model on CSSD dataset. TopCoder is selected as target CSSD platform for the study. TopCoder’s development tasks data with development-centric features are extracted, leading to statistical, regression and correlation analysis to justify features’ significance. For effort estimation, 10 ML families with 2 respective techniques are applied to get broader aspect of estimation. Five performance metrices (MSE, RMSE, MMRE, MdMRE, Pred (25) and Welch’s statistical test are incorporated to judge the worth of effort estimation model’s performance. Data analysis results show that selected features of TopCoder pertain reasonable model significance, regression, and correlation measures. Findings of ML effort estimation depicted that best results for TopCoder dataset can be acquired by linear, non-linear regression and SVM family models. To conclude, the study identified the most relevant development features for CSSD platform, confirmed by in-depth data analysis. This reflects careful selection of effort estimation features to offer good basis of accurate ML estimate.

The use of Massive Online Open Courses (MOOCs) has been noticeably increased in recent times, especially after the COVID-19 pandemic. In the absence of one-to-one interaction with the students, the instructors are no longer able to understand the demands of their students in an intrinsic way. To overcome this problem, the MOOC platforms provide a discussion forum in which students can share their thoughts and problems about the course. The instructors must closely monitor the performance of their students so that they can improve their teaching methodology to enhance the students' understanding. The instructors must go through the long chats in the discussion forums to identify specific problem areas faced by students. In this study, we propose a method that first categorizes discussion threads into topics and subtopics with the help of topic modeling and then performs sentiment analysis on comments to identify the sentiment of the posts. The primary objective of the study is to facilitate the instructors so that they can improve their teaching methodology, thus enhancing the understanding level of the students.

Qualitative analysis of citations received by a scientific manuscript is a challenging task in the field of citation analysis. In most cases, the existing approaches that involve citations for the scientific impact evaluation normally employ a quantitative parameter, such as the number of received citations, while ignoring the qualitative feature, such as the context of citations, while, in reality, a received citation might hold positive feedback and negative or neutral feedback. In this study, a measure is purposed for the scientific evaluation of the articles based on the context of the citations named the context-based article impact factor (CBAIF). CBAIF not only considers the positive, negative, or neutral context of the citations but also involves the citing and cited author’s conflict-of-interest relationship for the evaluation of their scientific impact. With the help of experimentation, it is observed that CBAIF performs a fair ranking of articles based on citation’s context, whether it is cited positively or being criticized by some authors. Experimental results show that the CBAIF value with the context of citations revealed accurate results rather than the article impact factor (AIF) value without the context of citations.

Identifying rising stars is very useful for faster growth of any organization. Rising entities has been explored in academics, sports, and blogs in the recent past, but business side is ignored. However, predicting rising business managers (RBMs) can result in significant business growth of any business. In order to maintain a competitive edge, machine learning techniques should be adopted to devise intelligent business strategies and perform predictions. In this article, RBMs are classified by exploring features of co-business managers (Co-BMs), rather than their own work history. Since ignoring their work history enables such prediction in a cold-start scenario, where work history is not available. After formulating features of Co-BM, the dataset is classified into two different evaluation setups. One is average revenue (AR) and the other one is average relative increase in revenue (ARIR)—class labels. All instances for both labels are randomly sorted into multisize (10, 20, and 30–100) datasets. Later on, these datasets are explored through machine learning classifiers using fivefold cross validation. In order to compare the prediction results with baseline and to measure the effectiveness of the proposed methods, the candidates’ business scores are used, which are calculated by the business definition and formulation. In terms of precision, recall, and f-measure, the feature, category, and model-based experimental results show that the generative models, particularly Bayesian networks, produce better results for an AR-based dataset. Also, overall results show the effective the proposed method.

Although the system for annual rankings of higher education institutions (HEIs) faces considerable criticism, these rankings are here to stay. Having become competent in assigning holistic ranking scores to HEIs, reputed ranking entities have now started focusing on subject-specific and regional rankings. However, in experts’ opinion, the process of assigning rankings should be more consistent, transparent, and representative. This study focuses on enhancing the credibility of the academic ranking process, by performing fine-grained assessment of the academic data pertaining to the computing discipline. The proposed assessment approach explores the data at the sub-discipline level, analyzing several ranking dimensions, including the research productivity, research impact, and research contribution of influential research scholars affiliated with renowned HEIs in the computing discipline. The analysis considers highly curated data published by three well-known international academic ranking entities, namely, Academic Rankings of World Universities (ARWU), Quacquarelli Symonds (QS), and Times Higher Education (THE), in 2018, 2019, and 2020, respectively. Researchers’ profiles are obtained from the Scopus repository, and the DBpedia repository is used to retrieve information about HEIs and their locations. For a stable comparison of the subject-specific academic rankings, the grand average rank measure is employed, whereas for finding the most influential researchers in computing, the ResRank measure is used. The sub-discipline-specific academic rankings provide more detailed insight into the academic rankings, thereby providing more robust decision support. This analysis, which focuses on the computing sub-discipline, is among the first few such efforts.

People are afraid about COVID-19 and are actively talking about it on social media platforms such as Twitter. People are showing their emotions openly in their tweets on Twitter. It's very important to perform sentiment analysis on these tweets for finding COVID-19's impact on people's lives. Natural language processing, textual processing, computational linguists, and biometrics are applied to perform sentiment analysis to identify and extract the emotions. In this work, sentiment analysis is carried out on a large Twitter dataset of English tweets. Ten emotional themes are investigated. Experimental results show that COVID-19 has spread fear/anxiety, gratitude, happiness and hope, and other mixed emotions among people for different reasons. Specifically, it is observed that positive news from top officials like Trump of chloroquine as cure to COVID-19 has suddenly lowered fear in sentiment, and happiness, gratitude, and hope started to rise. But, once FDA said, chloroquine is not effective cure, fear again started to rise.

Web spam is the unwanted request on websites, low-quality backlinks, emails, and reviews which is generated by an automated program. It is the big threat for website owners; because of it, they can lose their top keywords ranking from search engines, which will result in huge financial loss to the business. Over the years, researchers have tried to identify malicious domains based on specific features. However, lighthouse plugin, Ahrefs tool, and social media platforms features are ignored. In this paper, the authors are focused on detection of the spam domain name from a mixture of legit and spam domain name dataset. The dataset is taken from Google webmaster tools. Machine learning models are applied on individual, distributed, and hybrid features, which significantly improved the performance of existing malicious domain machine learning techniques. Better accuracy is achieved for support vector machine (SVM) classifier, as compared to Naïve Bayes, C4.5, AdaBoost, LogitBoost.

Recommender systems use machine-learning techniques to make predictions about resources. The medical field is one where much research is currently being conducted on recommender system utility. In the last few years, the amount of information available online that relates to healthcare has increased tremendously. Patients nowadays are more aware and look for answers to healthcare problems online. This has resulted in a dire need of an effective reliable online system to recommend the physician that is best suited to a particular patient in a limited time. In this article, a hybrid doctor-recommender system is proposed, by combining different recommendation approaches: content base, collaborative and demographic filtering to effectively tackle the issue of doctor recommendation. The proposed system addresses the issue of personalization through analysing patient's interest towards selecting a doctor. It uses a novel adoptive algorithm to construct a doctor's ranking function. Moreover, this ranking function is used to translate patients’ criteria for selecting a doctor into a numerical base rating, which will eventually be used in the recommendation of doctors. The system has been evaluated thoroughly, and result show that recommendations are reasonable and can fulfil patient's demand for reliable doctor's selection effectively.

In the present era of Big Data, with continuously increasing amounts of user-generated content, it is becoming a challenge to understand the relation between the content that is available on the Web and the users who are generating that content. Researchers have come up with many ways to understand today's Web better. One of the recently introduced concepts is a Web observatory (WO). This article provides a deep understanding about web observatories. It discusses the status of existing WO systems. The article investigates and gathers the common practices of WOs. This research has implications for researchers and communities in the adoption of the WO concept. The article highlights the challenges of WOs, such as data crawling, privacy and security. It also provides future research and development directions. The article provides a comparative analysis of existing WOs. It discusses the architecture of WOs. It presents components of a WO in a coherent manner and finally provides insights into challenges and limitations of WOs.

This article investigates the fundamental problem of traditional language models used for expert finding in bibliometric networks. It introduces novel Venue-Influence Language Modeling methods based on entropy, which can accommodate citation links based weights in an indirect way without using links information. Intuitively, an author publishing in topic-specific venues, either journals or for conferences, will be an expert on a topic as compared to an author publishing in multi-topic venues. The proposed methods are evaluated on real world data, the Digital Bibliography and Library Project (DBLP) dataset to test the performance. Experimental results show that their proposed venue influence language models (ViLMs) based methods outperform the traditional (non-venue based) language models (LM).

In this article we present a new semantic and syntactic-based method for external plagiarism detection. In the proposed approach, latent dirichlet allocation (LDA) and parts of speech (POS) tags are used together to detect plagiarism between the sample and a number of source documents. The basic hypothesis is that considering semantic and syntactic information between two text documents may improve the performance of the plagiarism detection task. Our method is based on two steps, naming, which is a pre-processing where we detect the topics from the sentences in documents using the LDA and convert each sentence in POS tags array; then a post processing step where the suspicious cases are verified purely on the basis of semantic rules. For two types of external plagiarism (copy and random obfuscation), we empirically compare our approach to the state-of-the-art N-gram based and stop-word N-gram based methods and observe significant improvements.

Nowadays, wireless body area networks (WBANs) systems have adopted cloud computing (CC) technology to overcome limitations such as power, storage, scalability, management, and computing. This amalgamation of WBANs systems and CC technology, as sensor‐cloud infrastructure (S‐CI), is aiding the healthcare domain through real‐time monitoring of patients and the early diagnosis of diseases. Hence, the distributed environment of S‐CI presents new threats to patient data privacy and security. In this paper, we review the techniques for patient data privacy and security in S‐CI. Existing techniques are classified as multibiometric key generation, pairwise key establishment, hash function, attribute‐based encryption, chaotic maps, hybrid encryption, Number Theory Research Unit, Tri‐Mode Algorithm, Dynamic Probability Packet Marking, and Priority‐Based Data Forwarding techniques, according to their application areas. Their pros and cons are presented in chronological order. We also provide our six‐step generic framework for patient physiological parameters (PPPs) privacy and security in S‐CI: (1) selecting the preliminaries; (2) selecting the system entities; (3) selecting the technique; (4) accessing PPPs; (5) analysing the security; and (6) estimating performance. Meanwhile, we identify and discuss PPPs utilized as datasets and provide the performance evolution of this research area. Finally, we conclude with the open challenges and future directions for this flourishing research area.

Social media has revolutionized human communication and styles of interaction. Due to its effectiveness and ease, people have started using it increasingly to share and exchange information, carry out discussions on various events, and express their opinions. Various communities may have diverse sentiments about events and it is an interesting research problem to understand the sentiments of a particular community for a specific event. In this article, the authors propose a framework CommuniMents which enables us to identify the members of a community and measure the sentiments of the community for a particular event. CommuniMents uses automated snowball sampling to identify the members of a community, then fetches their published contents (specifically tweets), pre-processes the contents and measures the sentiments of the community. The authors perform qualitative and quantitative evaluation for a variety of real world events to validate the effectiveness of the proposed framework.

Modeling ontology of folksonomy provides a way of learning light weight ontology's which is a hot topic investigated recently. Previous approaches for modeling ontology of folksonomy either ignores semantics (synonymy, hyponymy or polysemy) or do not simultaneously consider relationships between actors (users), concepts (tags) and instances(resources) or are based on the idea that title words are responsible for generating tags for resources. Latent semantics and user-tag dependencies instead of user-word dependencies however are extremely important. In this paper we address these problems by introducing a latent topic layer into the traditional tripartite Actor-Concept-Instance graph. We thus propose an Actor-Concept-Instance-Topic (ACIT) approach to model ontology from folksonomy in a unified way by directly using tags and users of resources. We illustrate on Bibsonomy dataset that our proposed approach ACIT outperforms title words based approaches Tag-Topic (TT) and (User-Word-Topic) UWT for modeling the ontology of folksonomy.