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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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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
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Uncertainty in runtime verification: A survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-09-07 Rania Taleb, Sylvain Hallé, Raphaël Khoury
Runtime Verification can be defined as a collection of formal methods for studying the dynamic evaluation of execution traces against formal specifications. Aside from creating a monitor from specifications and building algorithms for the evaluation of the trace, the process of gathering events and making them available for the monitor and the communication between the system under analysis and the
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A Comprehensive review of ‘Internet of Healthcare Things’: Networking aspects, technologies, services, applications, challenges, and security concerns Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-09-07 Himanshu Verma, Naveen Chauhan, Lalit Kumar Awasthi
The Internet of Things (IoT) is a smart, internet-connected, and omnipresent network. Healthcare is one of the most critical sectors that could benefit from IoT technology. In the medical sphere, the rise of the IoT transforms traditional healthcare services by encouraging technological, social, and economic factors. This study rigorously analyzes various aspects of the Internet of Healthcare Things
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Distributed ledger technologies for authentication and access control in networking applications: A comprehensive survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-09-02 Fariba Ghaffari, Emmanuel Bertin, Noel Crespi, Julien Hatin
The accelerated growth of networking technologies highlights the importance of Authentication and Access Control (AAC) as protection against associated attacks. Controlling access to resources, facilitating resource sharing, and managing user mobility are some of the notable capabilities provided by AAC methods. Centralized methods are the most common deployment architectures, that can be threatened
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Integration of hyperspectral imaging and autoencoders: Benefits, applications, hyperparameter tunning and challenges Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-08-31 Garima Jaiswal, Ritu Rani, Harshita Mangotra, Arun Sharma
Hyperspectral imaging (HSI) is a powerful tool that can capture and analyze a range of spectral bands, providing unparalleled levels of precision and accuracy in data analysis. Another technology gaining popularity in many industries is Autoencoders (AE). AE uses advanced deep learning algorithms for encoding and decoding data, leading to highly precise and efficient neural network-based models. Within
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An empirical investigation of task scheduling and VM consolidation schemes in cloud environment Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-09-01 Sweta Singh, Rakesh Kumar, Dayashankar Singh
Cloud computing has evolved as a new paradigm in Internet computing, offering services to the end-users and large-organizations, on-demand and pay-per-the-usage basis with high availability, elasticity, scalability and resiliency. In order to improve the performance of the Cloud system, handling multiple heterogeneous tasks concurrently, an appropriate task scheduler is required. To meet the user’s
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A review of IoT security and privacy using decentralized blockchain techniques Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-09-01 Vinay Gugueoth, Sunitha Safavat, Sachin Shetty, Danda Rawat
IoT security is one of the prominent issues that has gained significant attention among the researchers in recent times. The recent advancements in IoT introduces various critical security issues and increases the risk of privacy leakage of IoT data. Implementation of Blockchain can be a potential solution for the security issues in IoT. This review deeply investigates the security threats and issues
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Comprehensive survey of the solving puzzle problems Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-08-25 Seçkin Yılmaz, Vasif V. Nabiyev
Solving puzzle problems using computer-aided methods is becoming more common with applications in forensic science, restoration, banking system, and multimedia. However, only a few surveys have been published on this topic, the most recent being more than a decade old. The scope of 2D puzzle problems is extensive, and the number of computer-aided methods has increased in recent years. In this paper
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Understanding blockchain: Definitions, architecture, design, and system comparison Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-08-16 Mohammad Hossein Tabatabaei, Roman Vitenberg, Narasimha Raghavan Veeraragavan
The explosive advent of the blockchain technology has led to hundreds of blockchain systems in the industry, thousands of academic papers published over the last few years, and an even larger number of new initiatives and projects. Despite the emerging consolidation efforts, the area remains highly turbulent without systematization, educational materials, or cross-system comparative analysis. In this
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Defense strategies for Adversarial Machine Learning: A survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-08-11 Panagiotis Bountakas, Apostolis Zarras, Alexios Lekidis, Christos Xenakis
Adversarial Machine Learning (AML) is a recently introduced technique, aiming to deceive Machine Learning (ML) models by providing falsified inputs to render those models ineffective. Consequently, most researchers focus on detecting new AML attacks that can undermine existing ML infrastructures, overlooking at the same time the significance of defense strategies. This article constitutes a survey
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Systematic review on privacy categorisation Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-08-10 Paola Inverardi, Patrizio Migliarini, Massimiliano Palmiero
In the modern digital world users need to make privacy and security choices that have far-reaching consequences. Researchers are increasingly studying people’s decisions when facing with privacy and security trade-offs, the pressing and time consuming disincentives that influence those decisions, and methods to mitigate them. This work aims to present a systematic review of the literature on privacy
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Aspect based sentiment analysis using deep learning approaches: A survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-08-06 Ganpat Singh Chauhan, Ravi Nahta, Yogesh Kumar Meena, Dinesh Gopalani
The wealth of unstructured text on the online web portal has made opinion mining the most thrust area for researchers, academicians, and businesses to extract information for gathering, analyzing, and aggregating human emotions. The extraction of public sentiment from the text at an aspect level has contributed exceptionally to various businesses in the marketplace. In recent times, deep learning-based
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Optimized traffic engineering in Software Defined Wireless Network based IoT (SDWN-IoT): State-of-the-art, research opportunities and challenges Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-06-30 Rohit Kumar, Venkanna U., Vivek Tiwari
Wireless networks have been in focus since the last few decades due to their indispensable role in the future generation networks like the Internet of Things (IoT). However, the associated challenges in wireless network implementation such as distance, line-of-sight, interference, weather, power issues, etc., affect the performance adversely. Software Defined Networking (SDN) is a future generation
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A survey of the European Open Science Cloud services for expanding the capacity and capabilities of multidisciplinary scientific applications Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-06-07 Amanda Calatrava, Hernán Asorey, Jan Astalos, Alberto Azevedo, Francesco Benincasa, Ignacio Blanquer, Martin Bobak, Francisco Brasileiro, Laia Codó, Laura del Cano, Borja Esteban, Meritxell Ferret, Josef Handl, Tobias Kerzenmacher, Valentin Kozlov, Aleš Křenek, Ricardo Martins, Manuel Pavesio, Antonio Juan Rubio-Montero, Juan Sánchez-Ferrero
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Network resource management mechanisms in SDN enabled WSNs: A comprehensive review Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-06-01 Vikas Tyagi, Samayveer Singh
Wireless technologies usually have very limited computing, memory, and battery power that require the optimal management of network resources to increase network performance. The optimization of these network resources provides an efficient network topology, traffic control, routing, and data aggregation. This study presents a qualitative and quantitative investigation to evaluate the efficient network
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A survey of set accumulators for blockchain systems Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-06-02 Matteo Loporchio, Anna Bernasconi, Damiano Di Francesco Maesa, Laura Ricci
Set accumulators are cryptographic primitives used to represent arbitrarily large sets of elements with a single constant-size value and to efficiently verify whether a value belongs to that set. Accumulators support the generation of membership proofs, meaning that they can certify the presence of a given value among the elements of a set. In this paper we present an overview of the theoretical concepts
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Private set intersection: A systematic literature review Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-05-27 Daniel Morales, Isaac Agudo, Javier Lopez
Secure Multi-party Computation (SMPC) is a family of protocols which allow some parties to compute a function on their private inputs, obtaining the output at the end and nothing more. In this work, we focus on a particular SMPC problem named Private Set Intersection (PSI). The challenge in PSI is how two or more parties can compute the intersection of their private input sets, while the elements that
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Deep learning models for cloud, edge, fog, and IoT computing paradigms: Survey, recent advances, and future directions Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-05-25 Shahnawaz Ahmad, Iman Shakeel, Shabana Mehfuz, Javed Ahmad
In recent times, the machine learning (ML) community has recognized the deep learning (DL) computing model as the Gold Standard. DL has gradually become the most widely used computational approach in the field of machine learning, achieving remarkable results in various complex cognitive tasks that are comparable to, or even surpassing human performance. One of the key benefits of DL is its ability
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Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-05-22 Maha Nssibi, Ghaith Manita, Ouajdi Korbaa
The main objective of feature selection is to improve learning performance by selecting concise and informative feature subsets, which presents a challenging task for machine learning or pattern recognition applications due to the large and complex search space involved. This paper provides an in-depth examination of nature-inspired metaheuristic methods for the feature selection problem, with a focus
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Leveraging 6G, extended reality, and IoT big data analytics for healthcare: A review Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-05-04 Hafiz Farooq Ahmad, Wajid Rafique, Raihan Ur Rasool, Abdulaziz Alhumam, Zahid Anwar, Junaid Qadir
In recent years, the healthcare industry has faced new challenges around staffing, human interaction, and the adoption of telehealth. Technological innovations can improve efficiency, productivity, and patient outcomes, but healthcare has been slow to adopt them. However, the promise of 6G communication, extended reality (XR), and the Internet of Things (IoT) big data analytics may revolutionize healthcare
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Negative selection in anomaly detection—A survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2023-05-02 Praneet Saurabh, Bhupendra Verma
The remarkable ability to separate and identify self and non-self in a given problem space, makes negative selection a fascinating concept of artificial immune system. Therefore, negative selection has attracted research interest and is studied and explored for complex problem solving across different application areas. Anomaly detection in computer security is a thriving area of research and has witnessed