Pub Date : 2025-03-06DOI: 10.1016/j.cosrev.2025.100739
Qinghui Zhang , Yi Chen , Xue Liang
The emergence of food safety big data poses a huge challenge to data analysis and the application of technology. Intelligent visual analytics combines the advantages of artificial intelligence and visual analytics methods to process complex information more efficiently and accurately, providing technical support for intelligent food safety supervision. In this paper, we review the development and application of intelligent visual analytics for food safety over the past decade. First, we explore food safety data sources, data characteristics, and analytical tasks. Second, artificial intelligence methods and visualization techniques in food safety are presented respectively. Third, in-depth insights and applications of intelligent visual analytics methods from the perspective of food safety data characterization are provided, and typical cases are given. Finally, opportunities and challenges in intelligent visual analytics for food safety are proposed, including emerging technologies such as few-shot learning, automatic visualization generation, and large language models. The review aims to encourage researchers to propose more practical intelligent visual analytics solutions.
{"title":"Intelligent visual analytics for food safety: A comprehensive review","authors":"Qinghui Zhang , Yi Chen , Xue Liang","doi":"10.1016/j.cosrev.2025.100739","DOIUrl":"10.1016/j.cosrev.2025.100739","url":null,"abstract":"<div><div>The emergence of food safety big data poses a huge challenge to data analysis and the application of technology. Intelligent visual analytics combines the advantages of artificial intelligence and visual analytics methods to process complex information more efficiently and accurately, providing technical support for intelligent food safety supervision. In this paper, we review the development and application of intelligent visual analytics for food safety over the past decade. First, we explore food safety data sources, data characteristics, and analytical tasks. Second, artificial intelligence methods and visualization techniques in food safety are presented respectively. Third, in-depth insights and applications of intelligent visual analytics methods from the perspective of food safety data characterization are provided, and typical cases are given. Finally, opportunities and challenges in intelligent visual analytics for food safety are proposed, including emerging technologies such as few-shot learning, automatic visualization generation, and large language models. The review aims to encourage researchers to propose more practical intelligent visual analytics solutions.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100739"},"PeriodicalIF":13.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550020","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}
Pub Date : 2025-03-03DOI: 10.1016/j.cosrev.2025.100740
Sang-Woong Lee , Amir Haider , Amir Masoud Rahmani , Bahman Arasteh , Farhad Soleimanian Gharehchopogh , Shengda Tang , Zhe Liu , Khursheed Aurangzeb , Mehdi Hosseinzadeh
Optimization, as a fundamental pillar in engineering, computer science, economics, and many other fields, plays a decisive role in improving the performance of systems and achieving desired goals. Optimization problems involve many variables, various constraints, and nonlinear objective functions. Among the challenges of complex optimization problems is the extensive search space with local optima that prevents reaching the global optimal solution. Therefore, intelligent and collective methods are needed to solve problems, such as searching for large problem spaces and identifying near-optimal solutions. Metaheuristic algorithms are a successful method for solving complex optimization problems. Usually, metaheuristic algorithms, inspired by natural and social phenomena, try to find optimal or near-optimal solutions by using random searches and intelligent explorations in the problem space. Beluga Whale Optimization (BWO) is one of the metaheuristic algorithms for solving optimization problems that has attracted the attention of researchers in recent years. The BWO algorithm tries to optimize the search space and achieve optimal solutions by simulating the collective behavior of whales. A study and review of published articles on the BWO algorithm show that this algorithm has been used in various fields, including optimization of mathematical functions, engineering problems, and even problems related to artificial intelligence. In this article, the BWO algorithm is classified according to four categories (combination, improvement, variants, and optimization). An analysis of 151 papers shows that the BWO algorithm has the highest percentage (49%) in the improvement field. The combination, variants, and optimization fields comprise 12%, 7%, and 32%, respectively.
{"title":"A survey of Beluga whale optimization and its variants: Statistical analysis, advances, and structural reviewing","authors":"Sang-Woong Lee , Amir Haider , Amir Masoud Rahmani , Bahman Arasteh , Farhad Soleimanian Gharehchopogh , Shengda Tang , Zhe Liu , Khursheed Aurangzeb , Mehdi Hosseinzadeh","doi":"10.1016/j.cosrev.2025.100740","DOIUrl":"10.1016/j.cosrev.2025.100740","url":null,"abstract":"<div><div>Optimization, as a fundamental pillar in engineering, computer science, economics, and many other fields, plays a decisive role in improving the performance of systems and achieving desired goals. Optimization problems involve many variables, various constraints, and nonlinear objective functions. Among the challenges of complex optimization problems is the extensive search space with local optima that prevents reaching the global optimal solution. Therefore, intelligent and collective methods are needed to solve problems, such as searching for large problem spaces and identifying near-optimal solutions. Metaheuristic algorithms are a successful method for solving complex optimization problems. Usually, metaheuristic algorithms, inspired by natural and social phenomena, try to find optimal or near-optimal solutions by using random searches and intelligent explorations in the problem space. Beluga Whale Optimization (BWO) is one of the metaheuristic algorithms for solving optimization problems that has attracted the attention of researchers in recent years. The BWO algorithm tries to optimize the search space and achieve optimal solutions by simulating the collective behavior of whales. A study and review of published articles on the BWO algorithm show that this algorithm has been used in various fields, including optimization of mathematical functions, engineering problems, and even problems related to artificial intelligence. In this article, the BWO algorithm is classified according to four categories (combination, improvement, variants, and optimization). An analysis of 151 papers shows that the BWO algorithm has the highest percentage (49%) in the improvement field. The combination, variants, and optimization fields comprise 12%, 7%, and 32%, respectively.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100740"},"PeriodicalIF":13.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529171","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}
Pub Date : 2025-02-25DOI: 10.1016/j.cosrev.2025.100735
Diptadip Maiti , Madhuchhanda Basak , Debashis Das
In digital era, human authentication and identification mostly relies on biometric traits of an individual. Amongst different biometrics, fingerprint has been playing a crucial role and employing as fundamental evidence due to some of its inherent properties. Moreover, it establishes itself as the strongest verification component in several applications like – court of law, criminal and forensic investigations. In the present study we primarily focus on various application domains of fingerprint based identification systems. We also highlight the different challenges and security threats that the system may encounter during its implementation. The review analyses the state of the art methods with its technical details along with their implementation and security issues which lead to a thematic analysis of the literature. To facilitate a better comprehension towards the system, we also provide a few fundamental knowledge on fingerprint datasets and system performance measures. Finally, the future prospects of fingerprint biometric in the identification system are highlighted.
{"title":"A review on fingerprint based authentication-its challenges and applications","authors":"Diptadip Maiti , Madhuchhanda Basak , Debashis Das","doi":"10.1016/j.cosrev.2025.100735","DOIUrl":"10.1016/j.cosrev.2025.100735","url":null,"abstract":"<div><div>In digital era, human authentication and identification mostly relies on biometric traits of an individual. Amongst different biometrics, fingerprint has been playing a crucial role and employing as fundamental evidence due to some of its inherent properties. Moreover, it establishes itself as the strongest verification component in several applications like – court of law, criminal and forensic investigations. In the present study we primarily focus on various application domains of fingerprint based identification systems. We also highlight the different challenges and security threats that the system may encounter during its implementation. The review analyses the state of the art methods with its technical details along with their implementation and security issues which lead to a thematic analysis of the literature. To facilitate a better comprehension towards the system, we also provide a few fundamental knowledge on fingerprint datasets and system performance measures. Finally, the future prospects of fingerprint biometric in the identification system are highlighted.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100735"},"PeriodicalIF":13.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487843","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}
Pub Date : 2025-02-25DOI: 10.1016/j.cosrev.2025.100736
Juan P. Martinez-Esteso, Francisco J. Castellanos, Jorge Calvo-Zaragoza, Antonio Javier Gallego
The speed of response by search and rescue teams at sea is of vital importance, as survival may depend on it. Recent technological advancements have led to the development of more efficient systems for locating individuals involved in a maritime incident, such as the use of Unmanned Aerial Vehicles (UAVs) equipped with cameras and other integrated sensors. Over the past decade, several researchers have contributed to the development of automatic systems capable of detecting people using aerial images, particularly by leveraging the advantages of deep learning. In this article, we provide a comprehensive review of the existing literature on this topic. We analyze the methods proposed to date, including both traditional techniques and more advanced approaches based on machine learning and neural networks. Additionally, we take into account the use of synthetic data to cover a wider range of scenarios without the need to deploy a team to collect data, which is one of the major obstacles for these systems. Overall, this paper situates the reader in the field of detecting people at sea using aerial images by quickly identifying the most suitable methodology for each scenario, as well as providing an in-depth discussion and direction for future trends.
{"title":"Maritime search and rescue missions with aerial images: A survey","authors":"Juan P. Martinez-Esteso, Francisco J. Castellanos, Jorge Calvo-Zaragoza, Antonio Javier Gallego","doi":"10.1016/j.cosrev.2025.100736","DOIUrl":"10.1016/j.cosrev.2025.100736","url":null,"abstract":"<div><div>The speed of response by search and rescue teams at sea is of vital importance, as survival may depend on it. Recent technological advancements have led to the development of more efficient systems for locating individuals involved in a maritime incident, such as the use of Unmanned Aerial Vehicles (UAVs) equipped with cameras and other integrated sensors. Over the past decade, several researchers have contributed to the development of automatic systems capable of detecting people using aerial images, particularly by leveraging the advantages of deep learning. In this article, we provide a comprehensive review of the existing literature on this topic. We analyze the methods proposed to date, including both traditional techniques and more advanced approaches based on machine learning and neural networks. Additionally, we take into account the use of synthetic data to cover a wider range of scenarios without the need to deploy a team to collect data, which is one of the major obstacles for these systems. Overall, this paper situates the reader in the field of detecting people at sea using aerial images by quickly identifying the most suitable methodology for each scenario, as well as providing an in-depth discussion and direction for future trends.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100736"},"PeriodicalIF":13.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487844","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}
Pub Date : 2025-02-12DOI: 10.1016/j.cosrev.2025.100731
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 in the field of Human Computer Interaction (HCI) that relates to engaging non-expert audiences with data. In particular data exploration that contains a data visualization component can be useful for making data understandable and engaging for non-expert audiences.
Currently, the range of design practices most commonly used in the field of HCI to engage non-expert audiences in data exploration that includes a visualization component has yet to be formalized or given a comprehensive overview. This paper is a systematic mapping study (SMS) which aims to fill that gap by analyzing design trends engaging non-expert audiences in visualization driven data exploration via interactive systems, providing an overview of existing design practices and engagement methods, as well as set of three recommendations for how future designers can best engage non-expert audiences in visualization driven data exploration.
{"title":"Design practices in visualization driven data exploration for non-expert audiences","authors":"Natasha Tylosky, Antti Knutas, Annika Wolff","doi":"10.1016/j.cosrev.2025.100731","DOIUrl":"10.1016/j.cosrev.2025.100731","url":null,"abstract":"<div><div>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 in the field of Human Computer Interaction (HCI) that relates to engaging non-expert audiences with data. In particular data exploration that contains a data visualization component can be useful for making data understandable and engaging for non-expert audiences.</div><div>Currently, the range of design practices most commonly used in the field of HCI to engage non-expert audiences in data exploration that includes a visualization component has yet to be formalized or given a comprehensive overview. This paper is a systematic mapping study (SMS) which aims to fill that gap by analyzing design trends engaging non-expert audiences in visualization driven data exploration via interactive systems, providing an overview of existing design practices and engagement methods, as well as set of three recommendations for how future designers can best engage non-expert audiences in visualization driven data exploration.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100731"},"PeriodicalIF":13.3,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387411","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}
Pub Date : 2025-02-11DOI: 10.1016/j.cosrev.2025.100733
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 programming, because most conventional optimization methods focus on evaluating first derivatives. On the other hand, metaheuristic algorithms have high ability and adaptability in finding near-optimal solutions in a reasonable time for different optimization problems due to parallel search and balance between exploration and exploitation. This study discusses the basic principles and mechanisms of the GJO algorithm and its challenges. This review aims to provide valuable insights into the potential of the GJO algorithm for real-world and scientific optimization tasks. In this paper, a complete review of the Golden Jackal Optimization (GJO) algorithm for various optimization problems is done. The GJO algorithm is one of the metaheuristic algorithms invented in 2022 and inspired by the life of natural jackals. This paper's complete classification of GJO in hybrid, improved, binary, multi-objective, and optimization problems is done. The analysis shows that the percentage of studies conducted in the four fields of hybrid, improved variants of GJO (binary, multi-objective), and optimization are 11 %, 44 %, 9 %, and 36 %, respectively. Studies have shown that this algorithm performs well in real-world challenges. GJO is a powerful tool for solving scientific and engineering problems flexibly.
{"title":"A comprehensive survey of golden jacal optimization and its applications","authors":"Mehdi Hosseinzadeh , Jawad Tanveer , Amir Masoud Rahmani , Abed Alanazi , Monji Mohamed Zaidi , Khursheed Aurangzeb , Hamid Alinejad-Rokny , Thantrira Porntaveetus , Sang-Woong Lee","doi":"10.1016/j.cosrev.2025.100733","DOIUrl":"10.1016/j.cosrev.2025.100733","url":null,"abstract":"<div><div>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 programming, because most conventional optimization methods focus on evaluating first derivatives. On the other hand, metaheuristic algorithms have high ability and adaptability in finding near-optimal solutions in a reasonable time for different optimization problems due to parallel search and balance between exploration and exploitation. This study discusses the basic principles and mechanisms of the GJO algorithm and its challenges. This review aims to provide valuable insights into the potential of the GJO algorithm for real-world and scientific optimization tasks. In this paper, a complete review of the Golden Jackal Optimization (GJO) algorithm for various optimization problems is done. The GJO algorithm is one of the metaheuristic algorithms invented in 2022 and inspired by the life of natural jackals. This paper's complete classification of GJO in hybrid, improved, binary, multi-objective, and optimization problems is done. The analysis shows that the percentage of studies conducted in the four fields of hybrid, improved variants of GJO (binary, multi-objective), and optimization are 11 %, 44 %, 9 %, and 36 %, respectively. Studies have shown that this algorithm performs well in real-world challenges. GJO is a powerful tool for solving scientific and engineering problems flexibly.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100733"},"PeriodicalIF":13.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387410","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}
Pub Date : 2025-02-07DOI: 10.1016/j.cosrev.2025.100734
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 to different platforms for sustained operation. Hence, the offloading decision (OD) and resource allocation (RA) are closely interconnected. Currently, numerous research efforts are underway to address efficient offloading and resource management. However, a comprehensive review of OD and RA in aerial computing platforms is yet to be explored. This study presents a thorough survey of OD and RA in aerial computing platforms, extensively reviewing and comparatively discussing various algorithms and approaches. The discussion delves deep into key design issues and also explores unresolved challenges, providing possible future directions. Our work can help researchers in studying and designing efficient methods for task offloading and resource management across different application scenarios.
{"title":"Offloading decision and resource allocation in aerial computing: A comprehensive survey","authors":"Ahmadun Nabi, Sangman Moh","doi":"10.1016/j.cosrev.2025.100734","DOIUrl":"10.1016/j.cosrev.2025.100734","url":null,"abstract":"<div><div>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 to different platforms for sustained operation. Hence, the offloading decision (OD) and resource allocation (RA) are closely interconnected. Currently, numerous research efforts are underway to address efficient offloading and resource management. However, a comprehensive review of OD and RA in aerial computing platforms is yet to be explored. This study presents a thorough survey of OD and RA in aerial computing platforms, extensively reviewing and comparatively discussing various algorithms and approaches. The discussion delves deep into key design issues and also explores unresolved challenges, providing possible future directions. Our work can help researchers in studying and designing efficient methods for task offloading and resource management across different application scenarios.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100734"},"PeriodicalIF":13.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350067","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}
Pub Date : 2025-02-06DOI: 10.1016/j.cosrev.2025.100725
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 the relevant articles from 2019 to 2023 and compares the pertinent solutions included therein. In addition, it explores the various NLP technologies used for processing healthcare datasets in multiple languages. The review focuses on existing studies related to various medical conditions, including cancer and chronic and infectious diseases. It categorizes these cutting-edge studies into four different NLP task categories: prediction and detection, text analysis and modeling, information processing, and other healthcare applications. Notably, the findings reveal that the most prevalent NLP tasks employed in healthcare revolve around risk prediction and text classification. Moreover, the study identifies a pressing need for more extensive research that encompasses the utilization of non-textual medical datasets from EHR, such as X-rays, computed tomography (CT) scans, and magnetic resonance imaging (MRI) scans. A key observation is that much of the current research studies about NLP related to the healthcare field were primarily using conventional data processing methods, such as ML and DL techniques. Despite their success, these methods frequently have several distinct limitations as they are not able to handle large-scale, complex datasets. In contrast, there is less focus on sophisticated technologies such as big data analytics and transformer-based modeling. Big data analytics can manage massive amounts of unstructured data from sources such as EHRs and social media, providing a more comprehensive insight into healthcare patterns. Transformer models, like BERT and GPT, are designed to detect complex patterns and contextual relationships in text, making them particularly useful for medical text classification, sentiment analysis, and disease prediction. Current research studies have not fully explored the potential of these advanced technologies, which could significantly increase the efficiency and scalability of natural language processing applications in healthcare. This highlights opportunities for further exploration and innovation within the domain of NLP in healthcare.
{"title":"Advances in natural language processing for healthcare: A comprehensive review of techniques, applications, and future directions","authors":"Fatmah Alafari , Maha Driss , Asma Cherif","doi":"10.1016/j.cosrev.2025.100725","DOIUrl":"10.1016/j.cosrev.2025.100725","url":null,"abstract":"<div><div>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 the relevant articles from 2019 to 2023 and compares the pertinent solutions included therein. In addition, it explores the various NLP technologies used for processing healthcare datasets in multiple languages. The review focuses on existing studies related to various medical conditions, including cancer and chronic and infectious diseases. It categorizes these cutting-edge studies into four different NLP task categories: prediction and detection, text analysis and modeling, information processing, and other healthcare applications. Notably, the findings reveal that the most prevalent NLP tasks employed in healthcare revolve around risk prediction and text classification. Moreover, the study identifies a pressing need for more extensive research that encompasses the utilization of non-textual medical datasets from EHR, such as X-rays, computed tomography (CT) scans, and magnetic resonance imaging (MRI) scans. A key observation is that much of the current research studies about NLP related to the healthcare field were primarily using conventional data processing methods, such as ML and DL techniques. Despite their success, these methods frequently have several distinct limitations as they are not able to handle large-scale, complex datasets. In contrast, there is less focus on sophisticated technologies such as big data analytics and transformer-based modeling. Big data analytics can manage massive amounts of unstructured data from sources such as EHRs and social media, providing a more comprehensive insight into healthcare patterns. Transformer models, like BERT and GPT, are designed to detect complex patterns and contextual relationships in text, making them particularly useful for medical text classification, sentiment analysis, and disease prediction. Current research studies have not fully explored the potential of these advanced technologies, which could significantly increase the efficiency and scalability of natural language processing applications in healthcare. This highlights opportunities for further exploration and innovation within the domain of NLP in healthcare.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100725"},"PeriodicalIF":13.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143232645","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}
Pub Date : 2025-02-05DOI: 10.1016/j.cosrev.2025.100724
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. Furthermore, this paper focuses on the techniques and procedures that provide excellent results and provides an extensive perspective of approaches to devise heuristics for matrix bandwidth reduction. Thus, this paper surveys the most significant research in the field and examines the current state-of-the-art heuristics for the bandwidth reduction problem.
{"title":"A survey of heuristics for matrix bandwidth reduction","authors":"S.L. Gonzaga de Oliveira","doi":"10.1016/j.cosrev.2025.100724","DOIUrl":"10.1016/j.cosrev.2025.100724","url":null,"abstract":"<div><div>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. Furthermore, this paper focuses on the techniques and procedures that provide excellent results and provides an extensive perspective of approaches to devise heuristics for matrix bandwidth reduction. Thus, this paper surveys the most significant research in the field and examines the current state-of-the-art heuristics for the bandwidth reduction problem.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100724"},"PeriodicalIF":13.3,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093447","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}
Pub Date : 2025-02-01DOI: 10.1016/j.cosrev.2025.100730
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 in the automated diagnosis of ASD, providing a comparative analysis of their diagnostic accuracy, privacy considerations, non-invasiveness, cost implications, computational complexity, and feasibility for clinical and therapeutic use. The study encompasses a wide range of techniques including neuroimaging, EEG signal analysis, speech and crying signal analysis, eye tracking, facial recognition, and body movement analysis, highlighting their potential and limitations in the context of ASD diagnosis. By exploring these diverse diagnostic approaches, the review seeks to offer insights into the most promising methods and identify areas for future research and development.
{"title":"Machine learning in automated diagnosis of autism spectrum disorder: a comprehensive review","authors":"Khosro Rezaee","doi":"10.1016/j.cosrev.2025.100730","DOIUrl":"10.1016/j.cosrev.2025.100730","url":null,"abstract":"<div><div>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 in the automated diagnosis of ASD, providing a comparative analysis of their diagnostic accuracy, privacy considerations, non-invasiveness, cost implications, computational complexity, and feasibility for clinical and therapeutic use. The study encompasses a wide range of techniques including neuroimaging, EEG signal analysis, speech and crying signal analysis, eye tracking, facial recognition, and body movement analysis, highlighting their potential and limitations in the context of ASD diagnosis. By exploring these diverse diagnostic approaches, the review seeks to offer insights into the most promising methods and identify areas for future research and development.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100730"},"PeriodicalIF":13.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072454","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}
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