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Design practices in visualization driven data exploration for non-expert audiences Comput. Sci. Rev. (IF 13.3) Pub Date : 2025-02-12 Natasha Tylosky, Antti Knutas, Annika Wolff
Data exploration is increasingly relevant to the average person in our data-driven world, as data is now often open source and available to the general public and other non-expert users via open data portals and other similar data sources. This has introduced the need for data exploration tools, methods and techniques to engage non-expert users in data exploration, and thus a proliferation of new research
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A comprehensive survey of golden jacal optimization and its applications Comput. Sci. Rev. (IF 13.3) Pub Date : 2025-02-11 Mehdi Hosseinzadeh, Jawad Tanveer, Amir Masoud Rahmani, Abed Alanazi, Monji Mohamed Zaidi, Khursheed Aurangzeb, Hamid Alinejad-Rokny, Thantrira Porntaveetus, Sang-Woong Lee
In recent decades, there has been an increasing interest from the research community in various scientific and engineering fields, including robotic control, signal processing, image processing, feature selection, classification, clustering, and other issues. Many optimization problems are inherently complicated and complex. They cannot be solved by traditional optimization methods, such as mathematical
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Offloading decision and resource allocation in aerial computing: A comprehensive survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2025-02-07 Ahmadun Nabi, Sangman Moh
Aerial computing can facilitate the successful execution of tasks, ensuring low latency for Internet of things (IoT) devices. It gains greater significance and practicality by offering both edge and cloud computing services for IoT applications. However, in aerial computing, resources such as computing power, energy, and bandwidth are limited and constrained. Consequently, certain tasks must be offloaded
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Advances in natural language processing for healthcare: A comprehensive review of techniques, applications, and future directions Comput. Sci. Rev. (IF 13.3) Pub Date : 2025-02-06 Fatmah Alafari, Maha Driss, Asma Cherif
Natural Language Processing (NLP) techniques have gained significant traction within the healthcare domain for analyzing textual healthcare-related datasets, sourced primarily from Electronic Health Records (EHR) and increasingly from social networks. This study delves into applying NLP technologies within the healthcare sector, drawing insights from textual datasets from various sources. It reviews
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A survey of heuristics for matrix bandwidth reduction Comput. Sci. Rev. (IF 13.3) Pub Date : 2025-02-05 S.L. Gonzaga de Oliveira
This paper surveys heuristic methods for matrix bandwidth reduction, including low-cost methods and metaheuristics. This optimization min–max problem represents a demanding problem for heuristic methods. This paper poses the graph layout problem with its formal definition. The study also considers the application domains in which practitioners employ the linear graph layout problem on general matrices
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Machine learning in automated diagnosis of autism spectrum disorder: a comprehensive review Comput. Sci. Rev. (IF 13.3) Pub Date : 2025-02-01 Khosro Rezaee
Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition characterized by social communication challenges, repetitive behaviors, and restricted interests. Early and accurate diagnosis is paramount for effective intervention and treatment, significantly improving the quality of life for individuals with ASD. This comprehensive review aims to elucidate the various methodologies employed
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WebAssembly and security: A review Comput. Sci. Rev. (IF 13.3) Pub Date : 2025-01-30 Gaetano Perrone, Simon Pietro Romano
WebAssembly is revolutionizing the approach to developing modern applications. Although this technology was born to create portable and performant modules in web browsers, currently, its capabilities are extensively exploited in multiple and heterogeneous use-case scenarios. With the extensive effort of the community, new toolkits make the use of this technology more suitable for real-world applications
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Advancing smart transportation: A review of computer vision and photogrammetry in learning-based dimensional road pavement defect detection Comput. Sci. Rev. (IF 13.3) Pub Date : 2025-01-22 Adamu Tafida, Wesam Salah Alaloul, Noor Amila Bt Wan Zawawi, Muhammad Ali Musarat, Adamu Abubakar Sani
Road infrastructure networks are crucial in facilitating smart mobility, as indicated by the emergence of innovative transportation concepts that offer improved efficiency and environmental sustainability. This study seeks to review the literature regarding road pavement condition assessment performance improvement tools which utilize various computer vision and photogrammetry tools aided by machine
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Artificial hummingbird algorithm: Theory, variants, analysis, applications, and performance evaluation Comput. Sci. Rev. (IF 13.3) Pub Date : 2025-01-18 Buddhadev Sasmal, Arunita Das, Krishna Gopal Dhal, Ramesh Saha, Rebika Rai, Totan Bharasa, Essam H. Houssein
The Artificial Hummingbird Algorithm (AHA) is a metaheuristic optimization technique inspired by the behaviours and foraging strategies of hummingbirds. Known for their extraordinary agility and accuracy in collecting nectar, hummingbirds provide an exemplary framework for tackling complex optimization problems. Developed by Zhao et al. in 2022, AHA has swiftly attracted interest within the research
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A systematic review on cover selection methods for steganography: Trend analysis, novel classification and analysis of the elements Comput. Sci. Rev. (IF 13.3) Pub Date : 2025-01-17 Muhammad Harith Noor Azam, Farida Ridzuan, M. Norazizi Sham Mohd Sayuti, A H Azni, Nur Hafiza Zakaria, Vidyasagar Potdar
Cover selection is the process of selecting a suitable cover for steganography. Cover selection is crucial to maintain the steganographic characteristics performances and further avoid detection of hidden messages by eavesdroppers. Numerous existing reviews have focused mainly on the implementation and performance of steganography methods. Existing reviews have demonstrated inadequate depth of analysis
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Advances in attention mechanisms for medical image segmentation Comput. Sci. Rev. (IF 13.3) Pub Date : 2025-01-13 Jianpeng Zhang, Xiaomin Chen, Bing Yang, Qingbiao Guan, Qi Chen, Jian Chen, Qi Wu, Yutong Xie, Yong Xia
Medical image segmentation plays an important role in computer-aided diagnosis. Attention mechanisms that distinguish important parts from irrelevant parts have been widely used in medical image segmentation tasks. This paper systematically reviews the basic principles of attention mechanisms and their applications in medical image segmentation. First, we review the basic concepts of attention mechanism
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An inclusive analysis for performance and efficiency of graph neural network models for node classification Comput. Sci. Rev. (IF 13.3) Pub Date : 2025-01-11 S. Ratna, Sukhdeep Singh, Anuj Sharma
Graph Neural Networks (GNNs) have become a prominent technique for the analysis of graph-based data and knowledge extraction. This data can be either structured or unstructured. GNN approaches are particularly beneficial when it comes to examining non-euclidean data. Graph data formats are well-known for their capability to represent intricate systems and understand their relationships. GNNs have significantly
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Artificial intelligence based classification for waste management: A survey based on taxonomy, classification & future direction Comput. Sci. Rev. (IF 13.3) Pub Date : 2025-01-09 Dhanashree Vipul Yevle, Palvinder Singh Mann
Waste management has grown to become one of the leading global challenges due to the massive generation of thousands of tons of waste that is produced daily, leading to severe environmental degradation, the risk of public health, and resource depletion. Despite efforts directed towards solving these problems, traditional methods of sorting and categorizing waste are inefficient and unsustainable, thus
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Multimodal missing data in healthcare: A comprehensive review and future directions Comput. Sci. Rev. (IF 13.3) Pub Date : 2025-01-09 Lien P. Le, Thu Nguyen, Michael A. Riegler, Pål Halvorsen, Binh T. Nguyen
The rapid advancement in healthcare data collection technologies and the importance of using multimodal data for accurate diagnosis leads to a surge in multimodal data characterized by different types, structures, and missing values. Machine learning algorithms for predicting or analyzing usually demand the completeness of data. As a result, handling missing data has become a critical concern in the
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The emergence of artificial intelligence in autism spectrum disorder research: A review of neuro imaging and behavioral applications Comput. Sci. Rev. (IF 13.3) Pub Date : 2025-01-06 Indra Devi K.B., Durai Raj Vincent P.M.
The quest to find reliable biomarkers in autism spectrum disorders (ASD) is an ongoing endeavour to identify both underlying causes and measurable indicators of this neurodevelopmental condition. Machine learning (ML) and advanced deep learning (DL) techniques have enhanced biomarker identification in neuroimaging and behavioral studies, aiding in diagnostic accuracy and early detection. This review
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Advancements in AI for cardiac arrhythmia detection: A comprehensive overview Comput. Sci. Rev. (IF 13.3) Pub Date : 2025-01-03 Jagdeep Rahul, Lakhan Dev Sharma
Cardiovascular diseases (CVDs) are a global health concern, demanding advanced healthcare solutions. Accurate identification of CVDs via electrocardiogram (ECG) analysis is complex. Artificial Intelligence (AI) offers potential in improving diagnostic accuracy and uncovering new associations between ECG patterns and heart health risks. This paper reviews AI's historical evolution in CVD diagnosis,
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A comprehensive survey of Federated Intrusion Detection Systems: Techniques, challenges and solutions Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-12-20 Ioannis Makris, Aikaterini Karampasi, Panagiotis Radoglou-Grammatikis, Nikolaos Episkopos, Eider Iturbe, Erkuden Rios, Nikos Piperigkos, Aris Lalos, Christos Xenakis, Thomas Lagkas, Vasileios Argyriou, Panagiotis Sarigiannidis
Cyberattacks have increased radically over the last years, while the exploitation of Artificial Intelligence (AI) leads to the implementation of even smarter attacks which subsequently require solutions that will efficiently confront them. This need is indulged by incorporating Federated Intrusion Detection Systems (FIDS), which have been widely employed in multiple scenarios involving communication
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Knowledge graph representation learning: A comprehensive and experimental overview Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-12-19 Dorsaf Sellami, Wissem Inoubli, Imed Riadh Farah, Sabeur Aridhi
Knowledge graph embedding (KGE) is a hot topic in the field of Knowledge graphs (KG). It aims to transform KG entities and relations into vector representations, facilitating their manipulation in various application tasks and real-world scenarios. So far, numerous models have been developed in KGE to perform KG embedding. However, several challenges must be addressed when designing effective KGE models
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A comprehensive review of usage control frameworks Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-12-09 Ines Akaichi, Sabrina Kirrane
The sharing of data and digital assets in a decentralized settling is entangled with various legislative challenges, including, but not limited to, the need to adhere to legal requirements with respect to privacy and copyright. In order to provide more control to data and digital asset owners, usage control could be used to make sure that consumers handle data according to privacy, licenses, regulatory
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Integrating Explainable AI with Federated Learning for Next-Generation IoT: A comprehensive review and prospective insights Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-12-06 Praveer Dubey, Mohit Kumar
The emergence of the Internet of Things (IoT) signifies a transformative wave of innovation, establishing a network of devices designed to enrich everyday experiences. Developing intelligent and secure IoT applications without compromising user privacy and the transparency of model decisions causes a significant challenge. Federated Learning (FL) serves as a innovative solution, encouraging collaborative
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Cloud continuum testbeds and next-generation ICTs: Trends, challenges, and perspectives Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-12-06 Fran Casino, Peio Lopez-Iturri, Constantinos Patsakis
As society’s dependence on Information and Communication Technologies (ICTs) grows, providing efficient and resourceful services entails many complexities that require, among others, scalable systems that are provided with intelligent and automated management. In parallel, the different components of cloud computing are continuously evolving to enhance their capabilities towards leveraging the next
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Ontology learning towards expressiveness: A survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-12-05 Pauline Armary, Cheikh Brahim El-Vaigh, Ouassila Labbani Narsis, Christophe Nicolle
Ontology learning, particularly axiom learning, is a challenging task that focuses on building expressive and decidable ontologies. The literature proposes several research efforts aimed to resolve the complexities inherent in axiom and rule learning, which seeks to automatically infer logical constructs from diverse data sources. The goal of this paper is to conduct a comprehensive review of existing
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Non-square grids: A new trend in imaging and modeling? Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-12-04 Paola Magillo
The raster format of images and data is commonly intended as a synonymous of a square grid. Indeed, the square is not the only shape that can tessellate the plane. Other grids are well-known, and recently they have moved out of the fields of art and mathematics, and have started being of interest for technological applications. After introducing the main types of non-square grids, this paper presents
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A comprehensive review on current issues and advancements of Internet of Things in precision agriculture Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-11-28 S. Dhanasekar
The Internet of Things (IoT) is the basis of smart agriculture technology since it connects all aspects of intelligent systems in other industries and agricultural applications. The current farming methods are sufficient to supply adequate food in the future due to the fast-expanding global population. Smart farming aims to increase farm output and efficiency by leveraging state-of-the-art information
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A comprehensive review on Software-Defined Networking (SDN) and DDoS attacks: Ecosystem, taxonomy, traffic engineering, challenges and research directions Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-11-23 Amandeep Kaur, C. Rama Krishna, Nilesh Vishwasrao Patil
Software Defined network (SDN) represents a sophisticated networking approach that separates the control logic from the data plane. This separation results in a loosely coupled architecture between the control and data planes, enhancing flexibility in managing and transforming network configurations. Additionally, SDN provides a centralized management model through the SDN controller, simplifying network
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From accuracy to approximation: A survey on approximate homomorphic encryption and its applications Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-11-22 Weinan Liu, Lin You, Yunfei Shao, Xinyi Shen, Gengran Hu, Jiawen Shi, Shuhong Gao
Due to the increasing popularity of application scenarios such as cloud computing, and the growing concern of users about the security and privacy of their data, information security and privacy protection technologies are facing new challenges. Consequently, Homomorphic Encryption (HE) technology has been developed. HE technology has evolved from Partially Homomorphic Encryption (PHE) to fully homomorphic
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Image processing and artificial intelligence for apple detection and localization: A comprehensive review Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-11-13 Afshin Azizi, Zhao Zhang, Wanjia Hua, Meiwei Li, C. Igathinathane, Liling Yang, Yiannis Ampatzidis, Mahdi Ghasemi-Varnamkhasti, Radi, Man Zhang, Han Li
This review provides an overview of apple detection and localization using image analysis and artificial intelligence techniques for enabling robotic fruit harvesting in orchard environments. Classic methods for detecting and localizing infield apples are discussed along with more advanced approaches using deep learning algorithms that have emerged in the past few years. Challenges faced in apple detection
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A systematic review on security aspects of fog computing environment: Challenges, solutions and future directions Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-10-25 Navjeet Kaur
The dynamic and decentralized architecture of fog computing, which extends cloud computing closer to the edge of the network, offers benefits such as reduced latency and enhanced bandwidth. However, the existing fog architecture introduces unique security challenges due to the large number of distributed fog nodes, often deployed in diverse and resource-constrained environments. Further, the proximity
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A survey of deep learning techniques for detecting and recognizing objects in complex environments Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-10-24 Ashish Kumar Dogra, Vipal Sharma, Harsh Sohal
Object detection has been used extensively in daily life, and in computer vision, this sub-field is highly significant and challenging. The field of object detection has been transformed by deep learning. Deep learning-based methods have shown to be remarkably effective at identifying and localizing objects in images and video streams when it comes to object detection. Deep learning algorithms can
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Intervention scenarios and robot capabilities for support, guidance and health monitoring for the elderly Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-10-22 Saja Aldawsari, Yi-Ping Phoebe Chen
Demographic change in the world is a reality, and as a result, the number of elderly people is growing in both developed and developing countries, posing several social and economic issues. Most elderly people choose to stay alone at home rather than living with their families who can take care of them. Robots have the potential to revolutionize elderly care by providing aid, companionship, and monitoring
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Resilience of deep learning applications: A systematic literature review of analysis and hardening techniques Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-10-21 Cristiana Bolchini, Luca Cassano, Antonio Miele
Machine Learning (ML) is currently being exploited in numerous applications, being one of the most effective Artificial Intelligence (AI) technologies used in diverse fields, such as vision, autonomous systems, and the like. The trend motivated a significant amount of contributions to the analysis and design of ML applications against faults affecting the underlying hardware. The authors investigate
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AI-driven cluster-based routing protocols in WSNs: A survey of fuzzy heuristics, metaheuristics, and machine learning models Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-10-07 Mohammad Shokouhifar, Fakhrosadat Fanian, Marjan Kuchaki Rafsanjani, Mehdi Hosseinzadeh, Seyedali Mirjalili
Cluster-based routing techniques have become a key solution for managing data flow in Wireless Sensor Networks (WSNs), which often struggle with limited resources and dynamic network conditions. With the growing need for efficient data management in these networks, it is more important than ever to understand and enhance these techniques. This survey evaluates recent cluster-based routing protocols
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Unleashing the prospective of blockchain-federated learning fusion for IoT security: A comprehensive review Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-10-03 Mansi Gupta, Mohit Kumar, Renu Dhir
Internet-of-things (IoT) is a revolutionary paragon that brings automation and easiness to human lives and improves their experience. Smart Homes, Healthcare, and Agriculture are some of their amazing use cases. These IoT applications often employ Machine Learning (ML) techniques to strengthen their functionality. ML can be used to analyze sensor data for various, including optimizing energy usage
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A survey of automated negotiation: Human factor, learning, and application Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-09-28 Xudong Luo, Yanling Li, Qiaojuan Huang, Jieyu Zhan
The burgeoning field of automated negotiation systems represents a transformative approach to resolving conflicts and allocating resources with enhanced efficiency. This paper presents a thorough survey of this discipline, emphasising the implications of human factors, the application of machine learning techniques, and the real-world deployments of these systems. In traditional manual negotiation
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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