Computer Science Review最新文献

{"title":"A contemporary review on chatbots, AI-powered virtual conversational agents, ChatGPT: Applications, open challenges and future research directions","authors":"Avyay Casheekar,&nbsp;Archit Lahiri,&nbsp;Kanishk Rath,&nbsp;Kaushik Sanjay Prabhakar,&nbsp;Kathiravan Srinivasan","doi":"10.1016/j.cosrev.2024.100632","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100632","url":null,"abstract":"<div><p>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 contribution of this review is the comprehensive exploration of ChatGPT’s applications across a multitude of sectors, including education, business, public health, and more. This review highlights the transformative potential of ChatGPT, despite the challenges it faces such as hallucination, biases in training data, jailbreaks, and anonymous data collection. The review paper then presents a comprehensive survey of prior literature reviews on chatbots, identifying gaps in the prior work and highlighting the need for further research in areas such as chatbot evaluation, user experience, and ethical considerations. The paper also provides a detailed analysis of the UI/UX design of prominent chatbots, including their conversational flow, visual design, and user engagement. The paper also identifies key future research directions, including mitigating language bias, enhancing ethical decision-making capabilities, improving user interaction and personalization, and developing robust governance frameworks. By solving these issues, we can ensure that AI chatbots like ChatGPT are used responsibly and effectively across a broad variety of applications. This review will be a valuable resource for researchers and practitioners in understanding the current state and future potential of AI chatbots like ChatGPT.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"52 ","pages":"Article 100632"},"PeriodicalIF":12.9,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140540470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI techniques for IoT-based DDoS attack detection: Taxonomies, comprehensive review and research challenges","authors":"Bindu Bala ,&nbsp;Sunny Behal","doi":"10.1016/j.cosrev.2024.100631","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100631","url":null,"abstract":"<div><p>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 to detect IoT-based DDoS attacks. However, techniques based on Artificial Intelligence (AI) have proven to be effective in the detection of cyber-attacks in comparison to other alternative techniques. This paper presents a systematic literature review of AI-based tools and techniques used for analysis, classification, and detection of the most threatening, prominent, and dreadful IoT-based DDoS attacks between the years 2019 to 2023. A comparative study of real datasets having IoT traffic features has also been illustrated. The findings of this systematic review provide useful insights into the existing research landscape for designing AI-based models to detect IoT-based DDoS attacks specifically. Additionally, the study sheds light on IoT botnet lifecycle, various botnet families, the taxonomy of IoT-based DDoS attacks, prominent tools used to launch DDoS attack, publicly available IoT datasets, the taxonomy of AI techniques, popular software available for ML/DL modeling, a list of numerous research challenges and future directions that may aid in the development of novel and reliable methods for identifying and categorizing IoT-based DDoS attacks.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"52 ","pages":"Article 100631"},"PeriodicalIF":12.9,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140330585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiple clusterings: Recent advances and perspectives","authors":"Guoxian Yu ,&nbsp;Liangrui Ren ,&nbsp;Jun Wang ,&nbsp;Carlotta Domeniconi ,&nbsp;Xiangliang Zhang","doi":"10.1016/j.cosrev.2024.100621","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100621","url":null,"abstract":"<div><p>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 clustering can simultaneously or sequentially uncover multiple non-redundant and distinct clustering solutions, which can reveal multiple interesting hidden structures of the data from different perspectives. For these reasons, multiple clustering has become a popular and promising field of study. In this survey, we have conducted a systematic review of the existing multiple clustering methods. Specifically, we categorize existing approaches according to four different perspectives (i.e., multiple clustering in the original space, in subspaces and on multi-view data, and multiple co-clustering). We summarize the key ideas underlying the techniques and their objective functions, and discuss the advantages and disadvantages of each. In addition, we built a repository of multiple clustering resources (i.e., benchmark datasets and codes). Finally, we discuss the key open issues for future investigation.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"52 ","pages":"Article 100621"},"PeriodicalIF":12.9,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139975673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable computing across datacenters: A review of enabling models and techniques","authors":"Muhammad Zakarya ,&nbsp;Ayaz Ali Khan ,&nbsp;Mohammed Reza Chalak Qazani ,&nbsp;Hashim Ali ,&nbsp;Mahmood Al-Bahri ,&nbsp;Atta Ur Rehman Khan ,&nbsp;Ahmad Ali ,&nbsp;Rahim Khan","doi":"10.1016/j.cosrev.2024.100620","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100620","url":null,"abstract":"<div><p>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, driven by user’s applications demand, in the past few decades, particularly from 1990 to 2010. By the mid of 2010 to 2023, albeit parallel and distributed computing was omnipresent, but the total performance improvement rate of a single computing core had significantly reduced. Similarly, from 2010 to 2023, our digital world of big data and IoT has considerably increased from 1.2 Zettabytes (i.e., sextillion bytes) to approximately 120 zettabytes. Moreover, in 2022 cloud datacenters consumed <span><math><mo>∼</mo></math></span> 200TWh of energy worldwide. However, due to their ever-increasing energy demand which causes <span><math><msub><mrow><mi>CO</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions, over the past years the focus has shifted to the design of architectures, software, and in particular, intelligent algorithms to compute on the data more efficiently and intelligently. The energy consumption problem is even greater for large-scale systems that involve several thousand servers. Combining these fears, cloud service providers are presently facing more challenges than earlier because they fight to keep up with the extraordinary network traffic being produced by the world’s fast-tracked move to online due to global pandemics. In this paper, we deliberate the energy consumption and performance problems of large-scale systems and present several taxonomies of energy and performance aware methodologies. We debate over the energy and performance efficiencies, both, which make this study different from those previously published in the literature. Important research papers have been surveyed to characterise and recognise crucial and outstanding topics for further research. We deliberate numerous state-of-the-art methods and algorithms, stated in the literature, that claim to advance the energy efficiency and performance of large-scale computing systems, and recognise numerous open challenges.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"52 ","pages":"Article 100620"},"PeriodicalIF":12.9,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139726310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fundamental design aspects of UAV-enabled MEC systems: A review on models, challenges, and future opportunities","authors":"Mohd Hirzi Adnan ,&nbsp;Zuriati Ahmad Zukarnain ,&nbsp;Oluwatosin Ahmed Amodu","doi":"10.1016/j.cosrev.2023.100615","DOIUrl":"https://doi.org/10.1016/j.cosrev.2023.100615","url":null,"abstract":"<div><p>The huge prospects of the internet of things (IoT) have led to an ever-growing demand for computing power by IoT users to enable various applications. Multi-access edge computing (MEC) research and development has rapidly gained attention during the last decade. The ability to deploy edge servers at different points across a content delivery network that can offer communication and computing services close to mobile user devices is one of the main factors driving the evolution of MEC. Furthermore, MEC has been considered a potentially transformational approach for fifth-generation (5 G) and beyond 5 G (B5G) networks, as well as a potential improvement to conventional cloud computing. Unmanned aerial vehicles (UAVs) can be used as effective aerial platforms to offer reliable and ubiquitous connections in wireless communication networks due to their distinctive qualities, such as high cruising altitude, on-demand deployment, and three-dimensional (3D) maneuverability. The number of research studies published in this area has dramatically increased due to the growing interest in UAV-enabled MEC. Although UAV-enabled MEC systems have been well studied, the existing models are becoming increasingly heterogeneous and scattered without harmony. This paper provides a comprehensive analysis of the literature on UAV-enabled MEC systems with a special focus on the system modeling, and optimization techniques for five identified domains, such as energy efficiency, resource allocation, trajectory control, latency, and security. For each domain, we have highlighted the recent advances, critical findings, and the advantages and disadvantages. Additionally, the identified proposed techniques were analyzed and discussed, with emphasize on the constraints and performance metrics. We also discuss a general system model for each highlighted domain. Moreover, the lessons are also derived from the study on system optimization and system modeling techniques identified in this paper. Then we discuss open issues related to UAV-enabled MEC systems in each highlighted domain, including problem formulation and optimization techniques. Finally, this paper lay out directions for future research to solve the aforementioned problems associated with UAV-enabled MEC systems.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"51 ","pages":"Article 100615"},"PeriodicalIF":12.9,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574013723000825/pdfft?md5=82077d59b65835005a3894d0bb65ba35&pid=1-s2.0-S1574013723000825-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139699623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for intelligent demand response and smart grids: A comprehensive survey","authors":"Prabadevi Boopathy ,&nbsp;Madhusanka Liyanage ,&nbsp;Natarajan Deepa ,&nbsp;Mounik Velavali ,&nbsp;Shivani Reddy ,&nbsp;Praveen Kumar Reddy Maddikunta ,&nbsp;Neelu Khare ,&nbsp;Thippa Reddy Gadekallu ,&nbsp;Won-Joo Hwang ,&nbsp;Quoc-Viet Pham","doi":"10.1016/j.cosrev.2024.100617","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100617","url":null,"abstract":"<div><p>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 and fault detectors), load management (smart meters and smart electric appliances). Thanks to recent advancements in big data and computing technologies, Deep Learning (DL) can be leveraged to learn the patterns from the generated data and predict the demand for electricity and peak hours. Motivated by the advantages of deep learning in smart grids, this paper sets to provide a comprehensive survey on the application of DL for intelligent smart grids and demand response. Firstly, we present the fundamental of DL, smart grids, demand response, and the motivation behind the use of DL. Secondly, we review the state-of-the-art applications of DL in smart grids and demand response, including electric load forecasting, state estimation, energy theft detection, energy sharing and trading. Furthermore, we illustrate the practicality of DL via various use cases and projects. Finally, we highlight the challenges presented in existing research works and highlight important issues and potential directions in the use of DL for smart grids and demand response.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"51 ","pages":"Article 100617"},"PeriodicalIF":12.9,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574013724000017/pdfft?md5=37a2ff44234d359888c071095b8d9b65&pid=1-s2.0-S1574013724000017-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139738725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Content-driven music recommendation: Evolution, state of the art, and challenges","authors":"Yashar Deldjoo ,&nbsp;Markus Schedl ,&nbsp;Peter Knees","doi":"10.1016/j.cosrev.2024.100618","DOIUrl":"10.1016/j.cosrev.2024.100618","url":null,"abstract":"<div><p>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 to as content-driven models – have been replacing pure CF or CB models. In this survey, we review 55 articles on content-driven music recommendation. Based on a thorough literature analysis, we first propose an onion model comprising five layers, each of which corresponds to a category of music content we identified: signal, embedded metadata, expert-generated content, user-generated content, and derivative content. We provide a detailed characterization of each category along several dimensions. Second, we identify six overarching challenges, according to which we organize our main discussion: increasing recommendation diversity and novelty, providing transparency and explanations, accomplishing context-awareness, recommending sequences of music, improving scalability and efficiency, and alleviating cold start. Each article addresses one or more of these challenges and is categorized according to the content layers of our onion model, the article’s goal(s), and main methodological choices. Furthermore, articles are discussed in temporal order to shed light on the evolution of content-driven music recommendation strategies. Finally, we provide our personal selection of the persisting grand challenges which are still waiting to be solved in future research endeavors.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"51 ","pages":"Article 100618"},"PeriodicalIF":12.9,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574013724000029/pdfft?md5=26989268f58d86868e6b0424feb8917a&pid=1-s2.0-S1574013724000029-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139577454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic literature review: Quantum machine learning and its applications","authors":"David Peral-García ,&nbsp;Juan Cruz-Benito ,&nbsp;Francisco José García-Peñalvo","doi":"10.1016/j.cosrev.2024.100619","DOIUrl":"10.1016/j.cosrev.2024.100619","url":null,"abstract":"<div><p>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, as well as for large-scale information processing. These advantages are achieved through the use of quantum features, such as entanglement or superposition. These capabilities can give quantum computers an advantage in terms of computational time and cost over classical computers. Nowadays, scientific challenges are impossible to perform by classical computation due to computational complexity (more bytes than atoms in the observable universe) or the time it would take (thousands of years), and quantum computation is the only known answer. However, current quantum devices do not have yet the necessary qubits and are not fault-tolerant enough to achieve these goals. Nonetheless, there are other fields like machine learning, finance, or chemistry where quantum computation could be useful with current quantum devices. This manuscript aims to present a review of the literature published between 2017 and 2023 to identify, analyze, and classify the different types of algorithms used in quantum machine learning and their applications. The methodology follows the guidelines related to Systematic Literature Review methods, such as the one proposed by Kitchenham and other authors in the software engineering field. Consequently, this study identified 94 articles that used quantum machine learning techniques and algorithms and shows their implementation using computational quantum circuits or <em>ansatzs</em>. The main types of found algorithms are quantum implementations of classical machine learning algorithms, such as support vector machines or the k-nearest neighbor model, and classical deep learning algorithms, like quantum neural networks. One of the most relevant applications in the machine learning field is image classification. Many articles, especially within the classification, try to solve problems currently answered by classical machine learning but using quantum devices and algorithms. Even though results are promising, quantum machine learning is far from achieving its full potential. An improvement in quantum hardware is required for this potential to be achieved since the existing quantum computers lack enough quality, speed, and scale to allow quantum computing to achieve its full potential.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"51 ","pages":"Article 100619"},"PeriodicalIF":12.9,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574013724000030/pdfft?md5=44d55da038269bf79fd19532c50aed5d&pid=1-s2.0-S1574013724000030-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139550991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Server placement in mobile cloud computing: A comprehensive survey for edge computing, fog computing and cloudlet","authors":"Ali Asghari ,&nbsp;Mohammad Karim Sohrabi","doi":"10.1016/j.cosrev.2023.100616","DOIUrl":"10.1016/j.cosrev.2023.100616","url":null,"abstract":"<div><p>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 emerging problem in both typical and edge types of MCC, different proposed methods of which are reviewed and evaluated in this paper. Proper placement of servers leads to more efficient utilization of these servers, reduces their response time and optimizes their energy consumption. A variety of techniques and approaches, including machine learning-based techniques, evolutionary models, optimization algorithms, heuristics and meta-heuristics have been employed by different server placement methods of the literature to find the optimal deployment map of servers. This paper provides a comprehensive analysis of these server placement methods in edge computing, fog computing and cloudlet, investigates their various aspects, dimensions and objectives, and evaluates their strengths and weaknesses. Furthermore, open challenges for server placement in MCC are provided, and future research directions are also explained and discussed.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"51 ","pages":"Article 100616"},"PeriodicalIF":12.9,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574013723000837/pdfft?md5=4da580211e5e50a716f9f8bd0b27abec&pid=1-s2.0-S1574013723000837-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139081998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for unmanned aerial vehicles detection: A review","authors":"Nader Al-lQubaydhi ,&nbsp;Abdulrahman Alenezi ,&nbsp;Turki Alanazi ,&nbsp;Abdulrahman Senyor ,&nbsp;Naif Alanezi ,&nbsp;Bandar Alotaibi ,&nbsp;Munif Alotaibi ,&nbsp;Abdul Razaque ,&nbsp;Salim Hariri","doi":"10.1016/j.cosrev.2023.100614","DOIUrl":"10.1016/j.cosrev.2023.100614","url":null,"abstract":"<div><p>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 to drone use and provide a brief background of deep learning. Then, we summarize the types of UAVs and the related unethical behaviors, safety, privacy, and cybersecurity concerns. Then, we present a comprehensive literature review of current drone detection methods based on deep learning. This area of research has arisen in the last two decades because of the rapid advancement of commercial and recreational drones and their combined risk to the safety of airspace. Various deep learning algorithms and their frameworks with respect to the techniques used to detect drones and their areas of applications are also discussed. Drone detection techniques are classified into four categories: visual, radar, acoustics, and radio frequency-based approaches. The findings of this study prove that deep learning-based detection and classification of drones looks promising despite several challenges. Finally, we provide some recommendations to meet future expectations.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"51 ","pages":"Article 100614"},"PeriodicalIF":12.9,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574013723000813/pdfft?md5=e384a0694b41d423f4b974632d20c5e2&pid=1-s2.0-S1574013723000813-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139091506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}