Article
Computer Science, Artificial Intelligence
Mohammed Alshutbi, Zhiyong Li, Moath Alrifaey, Masoud Ahmadipour, Muhammad Murtadha Othman
Summary: The decisions of experts and the evaluation of patient data play crucial roles in breast cancer analysis. Machine learning techniques can aid in quickly examining and diagnosing medical data, reducing potential errors caused by inexperienced decision-makers. This study proposes an intelligent cancer classification method that selects a feature subset and optimizes the parameters of the SVM classifier using the Jaya algorithm. The method is applied to accurately characterize a breast cancer dataset and compared with other classifiers, demonstrating its effectiveness.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Qiao Jin
Summary: With the rapid development of computer technology, text data has increased exponentially, posing challenges due to complexity. Developing effective text classification methods has become a popular research topic. This study proposes a combination of GA-SVM and GA-FCM models to improve the efficiency and accuracy of text classification. Experimental results show significant improvements and the potential to filter irrelevant and harmful information in English text data, benefiting various industries and fields.
Article
Engineering, Electrical & Electronic
Mahdi Ajdani, Hamidreza Ghaffary
Summary: The study presented a method to design an analytical framework for detecting destructive data based on time, users' information, and scale factors, which can be applied to big data. The method divides time into subperiods to train data using users' review information, and storage methods are applied for scalability. The combination of hardware-software method is used to detect destructive data, along with a new modified vector machine algorithm, showing higher efficiency than traditional support vector machine methods. The proposed method achieved an accuracy of 0.97, making it more acceptable than previous methods.
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yinan Guo, Zirui Zhang, Fengzhen Tang
Summary: Feature selection is important in machine learning to reduce complexity and simplify interpretation. A novel non-linear method proposed in this paper uses kernelized multi-class support vector machines and fast recursive feature elimination to select features that work well for all classes, resulting in lower computational time complexity.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Information Systems
Shubhra Dwivedi, Manu Vardhan, Sarsij Tripathi
Summary: The EFSGOA method, a combination of ensemble feature selection and grasshopper optimization algorithm, achieved excellent performance in intrusion detection, with high detection rates, accuracy, and low false alarm rates. The method significantly improved accuracy and reduced false alarms, outperforming other existing techniques.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Article
Management
Asuncion Jimenez-Cordero, Juan Miguel Morales, Salvador Pineda
Summary: Feature selection has become a challenging issue in machine learning, particularly in classification problems. Support Vector Machine is a widely used technique in classification tasks, with various methodologies proposed for selecting the most relevant features in SVM. The authors introduce an embedded feature selection method based on a min-max optimization problem to balance model complexity and classification accuracy, showcasing efficiency and usefulness in benchmark datasets.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Information Systems
Danijela Protic, Miomir Stankovic, Radomir Prodanovic, Ivan Vulic, Goran M. Stojanovic, Mitar Simic, Gordana Ostojic, Stevan Stankovski
Summary: Anomaly-based intrusion detection systems classify computer network behavior by identifying deviations from the statistical model of typical behavior. Feature selection and feature scaling are commonly used techniques to improve classifier performance.
Article
Computer Science, Artificial Intelligence
A. Ponmalar, V Dhanakoti
Summary: This paper presents a novel technique to enhance intrusion detection by addressing the complexities of heterogeneous security data in big data. The proposed methodology significantly improves accuracy and can identify different types of attacks. Comparisons with baseline models demonstrate the effectiveness of the approach.
APPLIED SOFT COMPUTING
(2022)
Article
Biology
Jianfu Xia, Zhifei Wang, Daqing Yang, Rizeng Li, Guoxi Liang, Huiling Chen, Ali Asghar Heidari, Hamza Turabieh, Majdi Mafarja, Zhifang Pan
Summary: This research aimed to construct a new intelligent diagnostic method that is accurate, fast, noninvasive, and cost-effective in distinguishing between complicated and uncomplicated appendicitis. The study analyzed the data of 298 patients with acute appendicitis and identified the most significant variables, then built a diagnostic model using an improved grasshopper optimization algorithm-based support vector machine.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Information Systems
Mohamed Gihan Ali, Ismail Ibrahim Gomaa, Saad Mohamed Darwish
Summary: This research proposes an intelligent decision model for predicting ICOs' success by employing feature selection and classifier optimization, which improves the ability to support investment decisions in the ICO market.
Article
Chemistry, Multidisciplinary
Merve Ozkan-Okay, Refik Samet, Omer Aslan, Selahattin Kosunalp, Teodor Iliev, Ivaylo Stoyanov
Summary: The fast development of communication technologies and computer systems poses security challenges, with growing and sophisticated network-related attacks. Traditional methods are no longer effective in detecting complicated cyber attacks, calling for new techniques that utilize data mining, machine learning, and deep learning to distinguish intrusions from normal network traffic.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Hardware & Architecture
Yosef Masoudi-Sobhanzadeh, Shabnam Emami-Moghaddam
Summary: This study proposes a machine learning-based method to predict botnets, addressing the limitations of existing methods in real-time application, functionality, and consideration of attack types. The results show that the proposed method accurately classifies data streams into relevant groups and achieves a trade-off between feature selection and prediction model performance.
Article
Computer Science, Information Systems
B. Sakthi Karthi Durai, J. Benadict Raja
Summary: The early detection of retinal abnormalities like diabetic retinopathy (DR) can be achieved using computerized analysis of retinal fundus images. This study presents an automated process that employs an optimized SVM classifier and a new feature extraction method for more accurate and efficient detection of DR. The proposed technique is validated using a standard dataset and achieves high sensitivity, specificity, and accuracy.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Telecommunications
Jie Zhang, Jinguang Sun, Hua He
Summary: This paper proposes a clustering detection method of network intrusion feature based on support vector machine and LCA block algorithm. By deleting useless features, establishing multi-level support vector models, and using the LCA algorithm to identify intrusion features, the method achieves better clustering detection results and reduces the average detection time effectively.
WIRELESS PERSONAL COMMUNICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Xin Yan, Hongmiao Zhu
Summary: This paper proposes a novel support vector machine model with feature mapping and kernel trick to handle datasets with different distributions. The model improves robustness by pre-selecting training points, and converts the problem into a convex quadratic programming problem solved efficiently by the sequential minimal optimization algorithm. Numerical tests demonstrate the superior performance of the proposed method compared to other classification methods.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Dhruba Jyoti Kalita, Shailendra Singh
Article
Engineering, Electrical & Electronic
Susheel Kumar Gupta, Shailendra Singh
Summary: This paper evaluates the performance of WSN routing under dynamic sink locations and compares various existing protocols. It also examines the importance of clustering protocols for improving network lifetime through a case study evaluation.
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
(2022)
Article
Telecommunications
Susheel Kumar Gupta, Shailendra Singh
Summary: This paper proposes an extended distributed clustering-based EE routing protocol, which improves the heterogeneity of nodes by introducing an additional intermediate advanced nodes layer, thus enhancing the energy efficiency and stability period of the network.
WIRELESS PERSONAL COMMUNICATIONS
(2023)
Article
Computer Science, Information Systems
Chandan Kumar, Shailendra Singh
Summary: This paper presents a survey and case study of various encryption standards for video surveillance, addressing the security issues in real-time videos. By designing content-based video encryption standards and lightweight crypto-encryption standards, the paper successfully meets the real-time requirement for securing surveillance videos.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Imaging Science & Photographic Technology
Chandan Kumar, Shailendra Singh
Summary: The use of video surveillance has increased exponentially with the integration of the internet. However, the security concerns of video transmission need to be addressed. While current encryption standards are effective, they are computationally demanding and not suitable for real-time video surveillance applications. Lightweight crypto-encryption standards are necessary to meet these needs. This paper thoroughly analyzes various encryption standards and proposes a Lightweight Fast Security Standard (LFSS) that meets real-time requirements by adopting a dual key fusion approach.
IMAGING SCIENCE JOURNAL
(2022)
Proceedings Paper
Computer Science, Information Systems
Prakash Narayan Hardaha, Shailendra Singh
2019 9TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING (CONFLUENCE 2019)
(2019)
Proceedings Paper
Automation & Control Systems
Prakash Narayan Hardaha, Shailendra Singh
2018 INTERNATIONAL CONFERENCE ON AUTOMATION AND COMPUTATIONAL ENGINEERING (ICACE)
(2018)
Article
Computer Science, Information Systems
Prakash Narayan Hardaha, Shailendra Singh
Article
Computer Science, Theory & Methods
Parnika De, Shailendra Singh
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
(2016)
Article
Engineering, Multidisciplinary
Rajesh Kumar Boghey, Shailendra Singh
SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES
(2016)
Proceedings Paper
Computer Science, Theory & Methods
Vivek Kumar, Krishna Mohan P. D. Shrivastva, Shailendra Singh
INTERNATIONAL CONFERENCE ON COMPUTATIONAL MODELLING AND SECURITY (CMS 2016)
(2016)
Proceedings Paper
Computer Science, Information Systems
Nikhil Agrawal, Shailendra Singh
2016 THIRD INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION PROCESSING, DATA MINING, AND WIRELESS COMMUNICATIONS (DIPDMWC)
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
Sonam Sharma, Shailendra Singh
PROCEEDINGS OF FIRST INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY FOR INTELLIGENT SYSTEMS: VOL 2
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
Sangeeta Kumari, Ravi Kant Kapoor, Shailendra Singh
2015 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMMUNICATION NETWORKS (CICN)
(2015)