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Deep study on autonomous learning techniques for complex pattern recognition in interconnected information systems Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-09-20 Zahra Amiri, Arash Heidari, Nima Jafari, Mehdi Hosseinzadeh
Artificial Intelligence (AI) and Machine Learning (ML) are being used more and more to handle complex tasks in many different areas. As a result, interconnected information systems are growing, which means that autonomous systems are needed to help them adapt, find complex patterns, and make better decisions in areas like cybersecurity, finance, healthcare, authentication, marketing, and supply chain
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ISO/IEC quality standards for AI engineering Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-09-14 Jesús Oviedo, Moisés Rodriguez, Andrea Trenta, Dino Cannas, Domenico Natale, Mario Piattini
Artificial Intelligence (AI) plays a crucial role in the digital transformation of organizations, with the influence of AI applications expanding daily. Given this context, the development of these AI systems to guarantee their effective operation and usage is becoming more essential. To this end, numerous international standards have been introduced in recent years. This paper offers a broad review
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Digital to quantum watermarking: A journey from past to present and into the future Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-09-14 Swapnaneel Dhar, Aditya Kumar Sahu
With the amplification of digitization, the surge in multimedia content, such as text, video, audio, and images, is incredible. Concomitantly, the incidence of multimedia tampering is also apparently increasing. Digital watermarking (DW) is the means of achieving privacy and authentication of the received content while preserving integrity and copyright. Literature has produced a plethora of state-of-the-art
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Comprehensive survey on resource allocation for edge-computing-enabled metaverse Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-09-09 Tanmay Baidya, Sangman Moh
With the rapid evaluation of virtual and augmented reality, massive Internet of Things networks and upcoming 6 G communication give rise to an emerging concept termed the “metaverse,” which promises to revolutionize how we interact with the digital world by offering immersive experiences between reality and virtuality. Edge computing, another novel paradigm, propels the metaverse functionality by enhancing
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Quantum secure authentication and key agreement protocols for IoT-enabled applications: A comprehensive survey and open challenges Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-31 Ponnuru Raveendra Babu, Sathish A.P. Kumar, Alavalapati Goutham Reddy, Ashok Kumar Das
This study provides an in-depth survey of quantum secure authentication and key agreement protocols, investigating the dynamic landscape of cryptographic techniques within the realm of quantum computing. With the potential threat posed by quantum computing advancements to classical cryptographic protocols, the exploration of secure authentication and key agreement in the quantum era becomes imperative
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Internet of everything meets the metaverse: Bridging physical and virtual worlds with blockchain Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-30 Wajid Rafique, Junaid Qadir
The Metaverse is an evolving technology that leverages the Internet infrastructure and the massively connected Internet of Everything (IoE) to create an immersive virtual world. In the Metaverse, humans engage in activities similar to those in the real world, such as socializing, working, attending events, exploring virtual landscapes, creating and trading digital assets, participating in virtual economies
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Role of human physiology and facial biomechanics towards building robust deepfake detectors: A comprehensive survey and analysis Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-30 Rajat Chakraborty, Ruchira Naskar
AI based multimedia content generation, already having achieved hyper-realism, deeply influences human perception and trust. Since emerging around late 2017, deepfake technology has rapidly gained popularity due to its diverse applications, raising significant concerns regarding its malicious and unethical use. Although many deepfake detectors have been developed by forensic researchers in recent years
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Hazy to hazy free: A comprehensive survey of multi-image, single-image, and CNN-based algorithms for dehazing Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-26 Jehoiada Jackson, Kwame Obour Agyekum, kwabena Sarpong, Chiagoziem Ukwuoma, Rutherford Patamia, Zhiguang Qin
The natural and artificial dispersal of climatic particles transforms images obtained in open-air conditions. Due to visibility diminishing aerosols, unfavorable climate situations such as mist, fog, and haze cause color change and reduce the contrast of the obtained image. Images seem deformed and inadequate in contrast saturation, affecting computer vision techniques considerably. Haze removal aims
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A survey on reconfigurable intelligent surfaces assisted multi-access edge computing networks: State of the art and future challenges Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-26 Manzoor Ahmed, Salman Raza, Aized Amin Soofi, Feroz Khan, Wali Ullah Khan, Fang Xu, Symeon Chatzinotas, Octavia A. Dobre, Zhu Han
This survey provides a comprehensive analysis of the integration of Reconfigurable Intelligent Surfaces (RIS) with edge computing, underscoring RIS’s critical role in advancing wireless communication networks. The examination begins by demystifying edge computing, contrasting it with traditional cloud computing, and categorizing it into several types. It further delves into advanced edge computing
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A systematic literature review on chaotic maps-based image security techniques Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-26 Dilbag Singh, Sharanpreet Kaur, Mandeep Kaur, Surender Singh, Manjit Kaur, Heung-No Lee
Images play a substantial role in various applications such as medical imaging, satellite imaging, and military communications. These images often contain confidential and sensitive information, and are typically transmitted over public networks. As a result, they are vulnerable to various security threats. Therefore, efficient image security techniques are necessary to protect these images from unauthorized
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A survey of blockchain, artificial intelligence, and edge computing for Web 3.0 Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-24 Jianjun Zhu, Fan Li, Jinyuan Chen
Web 3.0, as the third generation of the World Wide Web, aims to solve contemporary problems of trust, centralization, and data ownership. Driven by the latest advances in cutting-edge technologies, Web 3.0 is moving towards a more open, decentralized, intelligent, and interconnected network. Currently, increasingly widespread data breaches have raised awareness of online privacy and security of personal
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A comprehensive survey of text classification techniques and their research applications: Observational and experimental insights Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-23 Kamal Taha, Paul D. Yoo, Chan Yeun, Dirar Homouz, Aya Taha
The exponential growth of textual data presents substantial challenges in management and analysis, notably due to high storage and processing costs. Text classification, a vital aspect of text mining, provides robust solutions by enabling efficient categorization and organization of text data. These techniques allow individuals, researchers, and businesses to derive meaningful patterns and insights
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Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-22 Deepak Adhikari, Wei Jiang, Jinyu Zhan, Danda B. Rawat, Asmita Bhattarai
This paper provides a comprehensive survey of anomaly detection for the Internet of Things (IoT). Anomaly detection poses numerous challenges in IoT, with broad applications, including intrusion detection, fraud monitoring, cybersecurity, industrial automation, etc. Intensive attention has been received by network security analytics and researchers, particularly on anomaly detection in the network
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A comprehensive review of vulnerabilities and AI-enabled defense against DDoS attacks for securing cloud services Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-08 Surendra Kumar, Mridula Dwivedi, Mohit Kumar, Sukhpal Singh Gill
The advent of cloud computing has made a global impact by providing on-demand services, elasticity, scalability, and flexibility, hence delivering cost-effective resources to end users in pay-as-you-go manner. However, securing cloud services against vulnerabilities, threats, and modern attacks remains a major concern. Application layer attacks are particularly problematic because they can cause significant
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Digital image watermarking using deep learning: A survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-07 Khalid M. Hosny, Amal Magdi, Osama ElKomy, Hanaa M. Hamza
Lately, a lot of attention has been paid to securing the ownership rights of digital images. The expanding usage of the Internet causes several problems, including data piracy and data tampering. Image watermarking is a typical method of protecting an image's copyright. Robust watermarking for digital images is a process of embedding watermarks on the cover image and extracting them correctly under
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A survey on the parameterized complexity of reconfiguration problems Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-06 Nicolas Bousquet, Amer E. Mouawad, Naomi Nishimura, Sebastian Siebertz
A graph vertex-subset problem defines which subsets of the vertices of an input graph are feasible solutions. We view a feasible solution as a set of tokens placed on the vertices of the graph. A reconfiguration variant of a vertex-subset problem asks, given two feasible solutions of size , whether it is possible to transform one into the other by a sequence of token slides (along edges of the graph)
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A systematic survey on fault-tolerant solutions for distributed data analytics: Taxonomy, comparison, and future directions Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-05 Sucharitha Isukapalli, Satish Narayana Srirama
Fault tolerance is becoming increasingly important for upcoming exascale systems, supporting distributed data processing, due to the expected decrease in the Mean Time Between Failures (MTBF). To ensure the availability, reliability, dependability, and performance of the system, addressing the fault tolerance challenge is crucial. It aims to keep the distributed system running at a reduced capacity
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Deep learning for hyperspectral image classification: A survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-08-01 Vinod Kumar, Ravi Shankar Singh, Medara Rambabu, Yaman Dua
Hyperspectral image (HSI) classification is a significant topic of discussion in real-world applications. The prevalence of these applications stems from the precise spectral information offered by each pixelś data in hyperspectral imaging (HS). Classical machine learning (ML) methods face challenges in precise object classification with HSI data complexity. The intrinsic non-linear relationship between
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Unsupervised affinity learning based on manifold analysis for image retrieval: A survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-07-29 V.H. Pereira-Ferrero, T.G. Lewis, L.P. Valem, L.G.P. Ferrero, D.C.G. Pedronette, L.J. Latecki
Despite the advances in machine learning techniques, similarity assessment among multimedia data remains a challenging task of broad interest in computer science. Substantial progress has been achieved in acquiring meaningful data representations, but how to compare them, plays a pivotal role in machine learning and retrieval tasks. Traditional pairwise measures are widely used, yet unsupervised affinity
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The interaction design of 3D virtual humans: A survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-07-17 Xueyang Wang, Nan Cao, Qing Chen, Shixiong Cao
Virtual humans have become a hot research topic in recent years due to the development of AI technology and computer graphics. In this survey, we provide a comprehensive review of the interaction design of 3D virtual humans. We first categorize the interac- tion design of virtual humans into speech, eye, facial expressions, and posture interactions. Then we describe the combination of different modalities
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Mobile robot localization: Current challenges and future prospective Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-07-05 Inam Ullah, Deepak Adhikari, Habib Khan, M. Shahid Anwar, Shabir Ahmad, Xiaoshan Bai
Mobile Robots (MRs) and their applications are undergoing massive development, requiring a diversity of autonomous or self-directed robots to fulfill numerous objectives and responsibilities. Integrating MRs with the Intelligent Internet of Things (IIoT) not only makes robots innovative, trackable, and powerful but also generates numerous threats and challenges in multiple applications. The IIoT combines
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Reproducibility, Replicability and Repeatability: A survey of reproducible research with a focus on high performance computing Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-07-03 Benjamin Antunes, David R.C. Hill
Reproducibility is widely acknowledged as a fundamental principle in scientific research. Currently, the scientific community grapples with numerous challenges associated with reproducibility, often referred to as the “reproducibility crisis”. This crisis permeated numerous scientific disciplines. In this study, we examined the factors in scientific practices that might contribute to this lack of reproducibility
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Applicability of genetic algorithms for stock market prediction: A systematic survey of the last decade Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-07-03 Ankit Thakkar, Kinjal Chaudhari
Stock market is one of the attractive domains for researchers as well as academicians. It represents highly complex non-linear fluctuating market behaviours where traders, investors, and organizers look forward to reliable future predictions of the market indices. Such prediction problems can be computationally addressed using various machine learning, deep learning, sentiment analysis, as well as
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A survey on computation offloading in edge systems: From the perspective of deep reinforcement learning approaches Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-06-29 Peng Peng, Weiwei Lin, Wentai Wu, Haotong Zhang, Shaoliang Peng, Qingbo Wu, Keqin Li
Driven by the demand of time-sensitive and data-intensive applications, edge computing has attracted wide attention as one of the cornerstones of modern service architectures. An edge-based system can facilitate a flexible processing of tasks over heterogeneous resources. Hence, computation offloading is the key technique for systematic service improvement. However, with the proliferation of devices
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A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-06-22 Arturo Montejo-Ráez, M. Dolores Molina-González, Salud María Jiménez-Zafra, Miguel Ángel García-Cumbreras, Luis Joaquín García-López
For years, the scientific community has researched monitoring approaches for the detection of certain mental disorders and risky behaviors, like depression, eating disorders, gambling, and suicidal ideation among others, in order to activate prevention or mitigation strategies and, in severe cases, clinical treatment. Natural Language Processing is one of the most active disciplines dealing with the
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Adding relevance to rigor: Assessing the contributions of SLRs in Software Engineering through Citation Context Analysis Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-06-18 Oscar Díaz, Marcela Genero, Jeremías P. Contell, Mario Piattini
Research in Software Engineering greatly benefits from Systematic Literature Reviews (SLRs), in view of the citations they receive. While there has been a focus on improving the quality of SLRs in terms of the process, it remains unclear if this emphasis on rigor has also led to an increase in relevance. This study introduces Citation Context Analysis for SLRs as a method to go beyond simple citation
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A comprehensive review on transformer network for natural and medical image analysis Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-06-14 Ramkumar Thirunavukarasu, Evans Kotei
The Transformer network is the main application area for natural language processing. It has gained traction lately and exhibits potential in the field of computer vision. This cutting-edge method has proven to offer a significant impact on image analysis, a crucial area of computer vision. The transformer's outstanding performance in vision computing places it as an alternative to the convolutional
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Auto-scaling mechanisms in serverless computing: A comprehensive review Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-06-13 Mohammad Tari, Mostafa Ghobaei-Arani, Jafar Pouramini, Mohsen Ghorbian
The auto-scaling feature is fundamental to serverless computing, and it automatically allows applications to scale as needed. Hence, this allows applications to be configured to adapt to current traffic and demands and acquire resources as necessary without the need to manage servers directly. Auto-scaling is an important principle in developing serverless applications that is considered and increasingly
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Chaos Game Optimization: A comprehensive study of its variants, applications, and future directions Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-06-07 Raja Oueslati, Ghaith Manita, Amit Chhabra, Ouajdi Korbaa
Chaos Game Optimization Algorithm (CGO) is a novel advancement in metaheuristic optimization inspired by chaos theory. It addresses complex optimization problems in dynamical systems, exhibiting unique behaviours such as fractals and self-organized patterns. CGO’s design exemplifies adaptability and robustness, making it a significant tool for tackling intricate optimization scenarios. This study presents
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Backbones-review: Feature extractor networks for deep learning and deep reinforcement learning approaches in computer vision Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-06-07 Omar Elharrouss, Younes Akbari, Noor Almadeed, Somaya Al-Maadeed
To understand the real world using various types of data, Artificial Intelligence (AI) is the most used technique nowadays. While finding the pattern within the analyzed data represents the main task. This is performed by extracting representative features step, which is proceeded using the statistical algorithms or using some specific filters. However, the selection of useful features from large-scale
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Deep learning with the generative models for recommender systems: A survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-06-04 Ravi Nahta, Ganpat Singh Chauhan, Yogesh Kumar Meena, Dinesh Gopalani
The variety of enormous information on the web encourages the field of recommender systems (RS) to flourish. In recent times, deep learning techniques have significantly impacted information retrieval tasks, including RS. The probabilistic and non-linear views of neural networks emerge to generative models for recommendation tasks. At present, there is an absence of extensive survey on deep generative
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DDoS attacks & defense mechanisms in SDN-enabled cloud: Taxonomy, review and research challenges Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-06-04 Jasmeen Kaur Chahal, Abhinav Bhandari, Sunny Behal
Software-defined Networking (SDN) is a transformative approach for addressing the limitations of legacy networks due to decoupling of control planes from data planes. It offers increased programmability and flexibility for designing of cloud-based data centers. SDN-Enabled cloud data centers help in managing the huge traffic very effectively and efficiently. However, the security of SDN-Enabled Cloud
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More than a framework: Sketching out technical enablers for natural language-based source code generation Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-05-25 Chen Yang, Yan Liu, Changqing Yin
Natural Language-based Source Code Generation (NLSCG) holds the promise to revolutionize the way how software is developed by means of facilitating a collection of intelligent technical enablers, based on sustained improvements on the natural language to source code pipelines and continuous adoption of new coding paradigms. In recent years, a large variety of NLSCG technical solutions have been proposed
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A comprehensive review on applications of Raspberry Pi Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-05-14 Sudha Ellison Mathe, Hari Kishan Kondaveeti, Suseela Vappangi, Sunny Dayal Vanambathina, Nandeesh Kumar Kumaravelu
Raspberry Pi is an invaluable and popular prototyping tool in scientific research for experimenting with a wide variety of ideas, ranging from simple to complex projects. This review article explores how Raspberry Pi is used in various studies, discussing its pros and cons along with its applications in various domains such as home automation, agriculture, healthcare, industrial control, and advanced
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A survey on modeling for behaviors of complex intelligent systems based on generative adversarial networks Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-04-27 Yali Lv, Jingpu Duan, Xiong Li
This paper provides an extensive and in-depth survey of behavior modeling for complex intelligent systems, focusing specifically on the innovative applications of Generative Adversarial Networks (GANs). The survey not only delves into the fundamental principles of GANs, but also elucidates their pivotal role in accurately modeling the behaviors exhibited by complex intelligent systems. By categorizing
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Harnessing Heterogeneous Information Networks: A systematic literature review Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-04-27 Leila Outemzabet, Nicolas Gaud, Aurélie Bertaux, Christophe Nicolle, Stéphane Gerart, Sébastien Vachenc
The integration of multiple heterogeneous data into graph models has been the subject of extensive research in recent years. Harnessing these resulting Heterogeneous Information Networks (HINs) is a complex task that requires reasoning to perform various prediction tasks.
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Twenty-two years since revealing cross-site scripting attacks: A systematic mapping and a comprehensive survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-04-23 Abdelhakim Hannousse, Salima Yahiouche, Mohamed Cherif Nait-Hamoud
Cross-site scripting (XSS) is one of the major threats menacing the privacy of data and the navigation of trusted web applications. Since its disclosure in late 1999 by Microsoft security engineers, several techniques have been developed with the aim of securing web navigation and protecting web applications against XSS attacks. XSS has been and is still in the top 10 list of web vulnerabilities reported
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A contemporary review on chatbots, AI-powered virtual conversational agents, ChatGPT: Applications, open challenges and future research directions Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-04-09 Avyay Casheekar, Archit Lahiri, Kanishk Rath, Kaushik Sanjay Prabhakar, Kathiravan Srinivasan
This review paper offers an in-depth analysis of AI-powered virtual conversational agents, specifically focusing on OpenAI’s ChatGPT. The main contributions of this paper are threefold: (i) an exhaustive review of prior literature on chatbots, (ii) a background of chatbots including existing chatbots/conversational agents like ChatGPT, and (iii) a UI/UX design analysis of prominent chatbots. Another
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AI techniques for IoT-based DDoS attack detection: Taxonomies, comprehensive review and research challenges Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-03-30 Bindu Bala, Sunny Behal
Distributed Denial of Service (DDoS) attacks in IoT networks are one of the most devastating and challenging cyber-attacks. The number of IoT users is growing exponentially due to the increase in IoT devices over the past years. Consequently, DDoS attack has become the most prominent attack as vulnerable IoT devices are becoming victims of it. In the literature, numerous techniques have been proposed
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Multiple clusterings: Recent advances and perspectives Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-02-26 Guoxian Yu, Liangrui Ren, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang
Clustering is a fundamental data exploration technique to discover hidden grouping structure of data. With the proliferation of big data, and the increase of volume and variety, the complexity of data multiplicity is increasing as well. Traditional clustering methods can provide only a single clustering result, which restricts data exploration to one single possible partition. In contrast, multiple
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Deep learning for intelligent demand response and smart grids: A comprehensive survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-02-14 Prabadevi Boopathy, Madhusanka Liyanage, Natarajan Deepa, Mounik Velavali, Shivani Reddy, Praveen Kumar Reddy Maddikunta, Neelu Khare, Thippa Reddy Gadekallu, Won-Joo Hwang, Quoc-Viet Pham
Electricity is one of the mandatory commodities for mankind today. To address challenges and issues in the transmission of electricity through the traditional grid, the concepts of smart grids and demand response have been developed. In such systems, a large amount of data is generated daily from various sources such as power generation (e.g., wind turbines), transmission and distribution (microgrids
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Sustainable computing across datacenters: A review of enabling models and techniques Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-02-13 Muhammad Zakarya, Ayaz Ali Khan, Mohammed Reza Chalak Qazani, Hashim Ali, Mahmood Al-Bahri, Atta Ur Rehman Khan, Ahmad Ali, Rahim Khan
The growth rate in big data and internet of things (IoT) is far exceeding the computer performance rate at which modern processors can compute on the massive amount of data. The cluster and cloud technologies enriched by machine learning applications had significantly helped in performance growths subject to the underlying network performance. Computer systems have been studied for improvement in performance
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Fundamental design aspects of UAV-enabled MEC systems: A review on models, challenges, and future opportunities Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-02-06 Mohd Hirzi Adnan, Zuriati Ahmad Zukarnain, Oluwatosin Ahmed Amodu
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Content-driven music recommendation: Evolution, state of the art, and challenges Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-01-30 Yashar Deldjoo, Markus Schedl, Peter Knees
The music domain is among the most important ones for adopting recommender systems technology. In contrast to most other recommendation domains, which predominantly rely on collaborative filtering (CF) techniques, music recommenders have traditionally embraced content-based (CB) approaches. In the past years, music recommendation models that leverage collaborative and content data – which we refer
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Systematic literature review: Quantum machine learning and its applications Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-01-25 David Peral-García, Juan Cruz-Benito, Francisco José García-Peñalvo
Quantum physics has changed the way we understand our environment, and one of its branches, quantum mechanics, has demonstrated accurate and consistent theoretical results. Quantum computing is the process of performing calculations using quantum mechanics. This field studies the quantum behavior of certain subatomic particles (photons, electrons, etc.) for subsequent use in performing calculations
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Deep learning for unmanned aerial vehicles detection: A review Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-01-03 Nader Al-lQubaydhi, Abdulrahman Alenezi, Turki Alanazi, Abdulrahman Senyor, Naif Alanezi, Bandar Alotaibi, Munif Alotaibi, Abdul Razaque, Salim Hariri
As a new type of aerial robotics, drones are easy to use and inexpensive, which has facilitated their acquisition by individuals and organizations. This unequivocal and widespread presence of amateur drones may cause many dangers, such as privacy breaches by reaching sensitive locations of authorities and individuals. In this paper, we summarize the performance-affecting factors and major obstacles
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Server placement in mobile cloud computing: A comprehensive survey for edge computing, fog computing and cloudlet Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-01-03 Ali Asghari, Mohammad Karim Sohrabi
The growing technology of the fifth generation (5G) of mobile telecommunications has led to the special attention of cloud service providers (CSPs) to mobile cloud computing (MCC). Due to the limitations in processing power, storage space and energy capacity of mobile devices, cloud resources can be moved to the edge of the network to improve the quality of service (QoS). Server placement is a crucial
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A survey on algorithms for Nash equilibria in finite normal-form games Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-12-28 Hanyu Li, Wenhan Huang, Zhijian Duan, David Henry Mguni, Kun Shao, Jun Wang, Xiaotie Deng
Nash equilibrium is one of the most influential solution concepts in game theory. With the development of computer science and artificial intelligence, there is an increasing demand on Nash equilibrium computation, especially for Internet economics and multi-agent learning. This paper reviews various algorithms computing the Nash equilibrium and its approximation solutions in finite normal-form games
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Systematic review on weapon detection in surveillance footage through deep learning Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-12-26 Tomás Santos, Hélder Oliveira, António Cunha
In recent years, the number of crimes with weapons has grown on a large scale worldwide, mainly in locations where enforcement is lacking or possessing weapons is legal. It is necessary to combat this type of criminal activity to identify criminal behavior early and allow police and law enforcement agencies immediate action. Despite the human visual structure being highly evolved and able to process
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Intelligent computational techniques for physical object properties discovery, detection, and prediction: A comprehensive survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-12-13 Shaili Mishra, Anuja Arora
The exploding usage of physical object properties has greatly facilitated real-time applications such as robotics to perceive exactly as it appears in existence. Changes in the nature and properties of diverse real-time systems are associated with physical properties modification due to environmental factors. These physics-based object properties features attract the researchers’ attention while developing
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Secret sharing: A comprehensive survey, taxonomy and applications Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-11-30 Arup Kumar Chattopadhyay, Sanchita Saha, Amitava Nag, Sukumar Nandi
The emergence of ubiquitous computing and different disruptive technologies caused magnificent development in information and communication technology. Likewise, cybercriminals are also carefully considering different newer ways of attacks. Protecting the confidentiality, integrity, and authentication of sensitive information is the day’s major challenge. Secret sharing is a method that allows a trusted
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Model-based joint analysis of safety and security:Survey and identification of gaps Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-11-07 Stefano M. Nicoletti, Marijn Peppelman, Christina Kolb, Mariëlle Stoelinga
We survey the state-of-the-art on model-based formalisms for safety and security joint analysis, where safety refers to the absence of unintended failures, and security to absence of malicious attacks. We conduct a thorough literature review and – as a result – we consider fourteen model-based formalisms and compare them with respect to several criteria: (1) Modeling capabilities and Expressiveness:
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Flow based containerized honeypot approach for network traffic analysis: An empirical study Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-10-28 Sibi Chakkaravarthy Sethuraman, Tharshith Goud Jadapalli, Devi Priya Vimala Sudhakaran, Saraju P. Mohanty
The world of connected devices has been attributed to applications that relied upon multitude of devices to acquire and distribute data over extremely diverse networks. This caused a plethora of potential threats. In the field of IT security, the concept of digital baits, or honeypots, which are typically network components (computer systems, access points, or switches) launched to be interrogated
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A comprehensive survey on data aggregation techniques in UAV-enabled Internet of things Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-11-01 Asif Mahmud Raivi, Sangman Moh
In recent years, unmanned aerial vehicles (UAVs) have been used to extend the Internet of things (IoT) framework owing to their vast applications, monitoring and surveillance capability, ubiquity, and mobility. To support IoT requirements, UAVs must be capable of aggregating, processing, and transmitting data in real-time basis. As not only the number of IoT devices but also the amount of data to be
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IoT systems modeling and performance evaluation Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-10-31 Alem Čolaković
The continuous increase of IoT applications leads to a vast amount of data that needs to be transmitted, stored, and processed. Many IoT applications rely on the Cloud infrastructure to handle these specific application demands. However, the integration of IoT and Cloud poses challenges such as network delays, throughput, energy consumption, reliability, etc. Therefore, a new computing concept is required
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Graph-based deep learning techniques for remote sensing applications: Techniques, taxonomy, and applications — A comprehensive review Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-10-05 Manel Khazri Khlifi, Wadii Boulila, Imed Riadh Farah
In the last decade, there has been a significant surge of interest in machine learning, primarily driven by advancements in deep learning (DL). DL has emerged as a powerful solution to address various challenges in numerous fields, including remote sensing (RS). Graph Deep Learning (GDL), a sub-field of DL, has recently gained increasing attention in the RS community. Tasks in RS requiring detailed
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Asynchronous federated learning on heterogeneous devices: A survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-10-04 Chenhao Xu, Youyang Qu, Yong Xiang, Longxiang Gao
Federated learning (FL) is a kind of distributed machine learning framework, where the global model is generated on the centralized aggregation server based on the parameters of local models, addressing concerns about privacy leakage caused by the collection of local training data. With the growing computational and communication capacities of edge and IoT devices, applying FL on heterogeneous devices
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Blockchain-based solutions for mobile crowdsensing: A comprehensive survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-09-16 Ruiyun Yu, Ann Move Oguti, Mohammad S. Obaidat, Shuchen Li, Pengfei Wang, Kuei-Fang Hsiao
Mobile crowdsensing (MCS) is an emerging data-driven paradigm that leverages the collective intelligence of the crowd, their mobility, and the crowd-companioned smart mobile devices embedded with powerful sensors to acquire information from the physical environment for crowd intelligence extraction and human-centric service delivery. However, existing MCS systems operate in a centralized manner, giving
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A quest for research and knowledge gaps in cybersecurity awareness for small and medium-sized enterprises Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-09-12 Sunil Chaudhary, Vasileios Gkioulos, Sokratis Katsikas
The proliferation of information and communication technologies in enterprises enables them to develop new business models and enhance their operational and commercial activities. Nevertheless, this practice also introduces new cybersecurity risks and vulnerabilities. This may not be an issue for large organizations with the resources and mature cybersecurity programs in place; the situation with small
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A systematic review of federated learning incentive mechanisms and associated security challenges Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-09-13 Asad Ali, Inaam Ilahi, Adnan Qayyum, Ihab Mohammed, Ala Al-Fuqaha, Junaid Qadir
In response to various privacy risks, researchers and practitioners have been exploring different paradigms that can leverage the increased computational capabilities of consumer devices to train machine learning (ML) models in a distributed fashion without requiring the uploading of the training data from individual devices to central facilities. For this purpose, federated learning (FL) was proposed