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Automatic feature selection using enhanced dynamic Crow Search Algorithm

International Journal of Information technology (Singapore) 2023年第5期15卷 2777-2782页

作者： Ranjan, Rajesh Chhabra, Jitender Kumar Computer Engineering Department National Institute of Technology Kurukshetra 136119 India

Data clustering is an unsupervised machine-learning technique that segregates similar data points. Most real-life datasets have redundant and irrelevant features, which deteriorate the clustering accuracy. Feature selection selects the most relevant subset of features, which improves clustering accuracy and reduces execution time. Different swarm and evolutionary-based algorithms are known to give an optimal solution for complex optimization problems like clustering and feature selection. The present work employs a wrapper-based feature selection technique. It incorporates an evolutionary search-based algorithm for simultaneous clustering and feature selection. An Enhanced Dynamic Cluster Search Algorithm (EDCSA) that wraps the K-means method to pick optimal features for clustering has been proposed. The results are compared with other works using various cluster validity indices. The results show that the EDCSA performs better at selecting relevant features, improving overall accuracy. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Clustering Feature selection Machine-learning Meta-Heuristic algorithm

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AquaMap: Empowering Communities to Report and Map Water-Related Issues in Real-Time with Deep Learning 5th

AquaMap: Empowering Communities to Report and Map Water-Rela...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Nalla, Harshitha Lakshmi Durga Bhuchupalli, Anusha Addala, Tejasree Sabbineni, Yasasri Geetha, Koppisetti Sravya Aasha, Ghantasala Bonthu, Sridevi Department of Computer Science and Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031770746

In today’s era of rapid urbanization and environmental challenges, effective disaster and crisis management demand innovative solutions. This paper presents a novel approach focusing on community-level water-related issues through an intelligently designed application. The primary objective is to develop a user-friendly platform facilitating the reporting of water-related problems by both social media posts and the general public, subsequently enabling prompt action by relevant government authorities. Leveraging deep learning techniques and user-generated data, our solution introduces real-time detection and classification of six distinct water-related problems. A custom dataset is curated to train a ResNet-18 model, achieving an impressive accuracy of 73%. The application, developed with a React-based frontend and Flask-powered backend, acts as a centralized hub for reporting and managing water issues. Notably, it employs user-inputted data to accurately pinpoint problem locations, thereby enhancing the precision of reporting. By presenting a holistic approach, this research significantly contributes to the development of efficient crisis management and response strategies for water-related disasters. © ICST institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Budget control

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Abstraction of Design Information from Legacy C++ Program 3

Abstraction of Design Information from Legacy C++ Program

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Edara, Venkata Sandeep Kulkarni, R.N. Sasi Institute Of Technology And Engineering Affiliated To Vtu Belagavi Department Of Computer Science And Engineering Andhra Pradesh Tadepalligudem India Ballari Institute Of Technology And Management Department Of Computer Science And Engineering Karnataka Ballari India

ISBN: (纸本)9798350318609

During the last three decades, the importance of the application software is tremendously increased. Every organization has automated all their day-to-day activities. For automation purpose they used the procedural or object-oriented programming languages. During the 1990's to 2010 many organizations used C++ programming language for the development of application software. Today because of the changes in Hardware and Software technology the C++ programming is not much widely used. But these applications require trained experts to maintain the system otherwise it becomes legacy system. These Legacy systems are at the heart of many outstanding companies. Although they are still essential to the general operation of the business, their solutions are out-of-date and, in certain circumstances, no longer even supported by the original software developer or vendor. Abstracting any reusable components from these systems is difficult. These systems can be modernized as to stay competitive by adding current sustainable features. In this paper, a methodology is proposed to abstract the design information which is a reusable component from the given input legacy C++ program in form of Control Flow and Data Flow Tables. © 2024 IEEE.

关键词： Application programs

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Integrating a Rule-Based Approach to Malware Detection with an LSTM-Based Feature Selection Technique

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SN computer Science 2023年第6期4卷 1-18页

作者： Bhardwaj, Sonam Dave, Mayank Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra India

technology has amplified malware activity, affecting network and users. Before being forwarded to the next host, network traffic must be dynamically analysed for malware. By exploiting network vulnerabilities, attackers gain control of the system and implement their own network rules to enable malicious traffic. Yet, another recursive acronym (YARA) rules are effective string and pattern-matching malware analysis approaches. The quality and amount of YARA rules utilized in analysis determine its effectiveness. YARA rules focus on whether to activate a rule for a suspicious sample after examining its rule condition. YARA rules rely on binary conclusion on malware analysis, which may limit its use and results. Thus, the proposed approach selects malware features using the ML-based LSTM model. Rule-based traffic analysis and long-short term memory (LSTM)-based feature selection strengthen the malware detection model in the proposed approach. By comparing performance results with and without LSTM-based feature (parameter) selection, this research assesses model integrity. Due to LSTM-based feature selection, the model achieved its best accuracy of 97%, proving its suitability for malware detection on diverse datasets belonging to different network environments. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Feature importance Feature selection LSTM Malware detection Rule-based YARA rules

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A survey on non-factoid question answering systems

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International Journal of computers and Applications 2022年第9期44卷 830-837页

作者： Breja, Manvi Jain, Sanjay Kumar Department of Computer Engineering National Institute of Technology Kurukshetra India

Question Answering System (QAS) aims at providing the most appropriate answer to the user’s question asked in any natural language. It emerged as a future of web search that crawls structured and unstructured collections of documents to find answers. A lot of research has already been done on answering factoid questions and achieved promising accuracy, whereas non-factoid question answering is a challenging task and explores newer avenues for fact-finding and analysis. The research on handling different modules of non-factoid question answering has drastically increased from 2012 which evokes the need for summarizing and providing a comprehensive analysis of the work that has already been done in this field. The survey identifies different challenges faced in each module, how they have been addressed by the researchers with their advantages or disadvantages and further improvements. The survey is written with an objective to help the researchers understand the field with how it has been undertaken by different approaches with their impact on performance. The paper finally concludes by investigating research gaps and research objectives that are promising to get an insight into future research directions. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Surveys

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An Investigational Study of Detecting Acute Lymphoblastic Leukemia using computer Vision 2

An Investigational Study of Detecting Acute Lymphoblastic Le...

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2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Ashok, M. Tharani, K. Venkatasriram, S. Ramasamy, Kumar Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350348002

Among all divesting cancers, Hematologists predict that the Leukemia is mostly occur on the children, teenagers, and young adults. Moreover 85% of cancer cases are detected younger than the age of 15. Due to a Genetic abnormality in the bone marrow, Acute lymphoblastic leukemia (ALL) is particularly susceptible to life-threatening infections. The laboratory method to detect the ALL is prolonged and slow process. Reviewing prior research on detection methods in ALL is the goal of this study. This review includes (a) the parameters necessary for the ALL detection (b) methods involved in the detection process (c) which algorithms gives the accurate prediction and (d) finally all the necessary context for the detection of the ALL-affected cells. The proposed system possesses the automated machine learning (Chabot) approach used to categorize the infected and healthy cells in the blood smears (microscopic images) in order to detect ALL. The Blood smears are converted into the CMYK color space and separated into clusters using K-means Algorithm. A cell from each cluster is picked and detected whether the cell is affected with ALL or not using the nuclei of the cell using supervised learning algorithm like SVM, XGBoost Classifier, Etc., The proposed system aids in enhancing the acute lymphoblastic leukemia detection system. © 2023 IEEE.

关键词： Cells

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ResNet-CPDS: Colonoscopy Polyp Detection and Segmentation Using Modified ResNet101V2 1

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10th International Conference on Mathematics and Computing, ICMC 2024

作者： Islam, S. K. Hafizul Pradhan, Jitesh Vashistha, Purnendu Singh, Aman P. Kishore, Aman Department of Computer Science and Engineering Indian Institute of Information Technology West Bengal Kalyani741235 India Department of Computer Science and Engineering National Institute of Technology Jharkhand Jamshedpur831014 India

ISBN: (数字)9789819720699

ISBN: (纸本)9789819720682

Colorectal cancer (CRC) is a global public health concern, and early detection through screening reduces mortality rates. It is one of the common types of cancer with a high mortality rate. Traditionally, colonoscopy is used to detect CRC, which is inefficient. Therefore, an automated Colonoscopy Polyp Detection and Segmentation (CPDS) system can significantly increase the efficiency of colonoscopy. We propose an automated model, ResNet-CPDS, using the modified ResNet101V2 model. We evaluate the performance of ResNet-CPDS and other CPDS models, and compare their accuracy. We also demonstrate that the ResNet-CPDS model outperforms other models for the CVC-ClinicDB dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diseases

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Differential Evolution Strategy with Chebyshev Chaos Based Mutation

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 613-628页

作者： Sitenda, Amos Song, Pei-Cheng Chu, Shu-Chuan Chen, Shi-Huang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Computer Science and Information Engineering Shu-Te UniversityKaohsiung Kaohsiung824 Taiwan

Differential Evolution (DE) is a potent stochastic evolutionary optimization algorithm garnering increasing research attention. Over the years, it has been found applicable in solving diverse real-world problems. DE employs mutation, crossover, and selection operators to guide populations toward optimal or nearly optimal solutions. However, the standard DE mutation strategies have found limitations in balancing exploration and exploitation effectively, thus prompting research into possible improvements. This study introduces a novel mutation strategy named Chebyshev Infused Chaos Mutation Strategy (CICMS). where a chaotic sequence partially guides the process of donor vector generation. Rigorous evaluations were conducted, comparing our modified DE against the standard DE and seven other metaheuristic algorithms, including Genetic Algorithm, Particle Swarm Optimization and Gravitational Search Algorithm. Experiments were performed using the challenging CEC 2014 benchmark functions, consisting of 30 objective functions. Results indicate substantial improvements in convergence speed and solution quality, highlighting the potential of our novel mutation strategy to enhance DE’s practicality in addressing complex optimization problems. This research contributes valuable insights to the dynamic field of optimization algorithms with implications for a wide range of applications. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Chaos theory

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks 1

Unified Approach Integrating Machine Learning Algorithms for...

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Mathur, Abeer Dubey, Abhinav Sudhakar Mahara, Pranav Singh Gandhi, Usha Devi School of Computer Science & Engineering Vellore Institute of Technology Department of Software Systems Vellore India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798350353648

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment. © 2024 IEEE.

关键词： Phishing

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Efficient Scheduling of Real-Time Messages over Heterogeneous in-Vehicle Network Domains 100

Efficient Scheduling of Real-Time Messages over Heterogeneou...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Chowdary Kota, Ram Mohan Mayank, Jaishree Sarkar, Amab Indian Institute of Information Technology Design and Manufacturing Department of Computer Science and Engineering Kancheepuram Chennai India Indian Institute of Technology Kharagpur Advanced Technology Development Centre Department of Computer Science and Engineering India

ISBN: (纸本)9798331517786

Time Sensitive Ethernet is quickly emerging to be the preferred choice as the backbone network for in-vehicle communication, due to its high bandwidth, reliability, scalability, backward compatibility, and support for diverse traffic types. However, individual real-time control subsystems which may need to communicate via this backbone network, may be driven by non-Ethernet protocols like CAN, FlexRay, etc. Beyond ad hoc approaches, this highlights the need for a systematic message scheduling mechanism that enables seamless real-time message transmission over heterogeneous network domains. In this context, this work proposes a formal SMT (Satisfiability Modulo Theories) formulation for statically scheduling a set of persistent, periodic real-time messages. While the sources and destinations of these messages are in different CAN-network domains, the message transmissions must be conducted via an intermediate Time Sensitive Networking (TSN;IEEE802.1Q) backbone. Although, this formal strategy achieves high resource utilization while guaranteeing end-to-end timeliness, it is computationally exponential in nature with overheads becoming prohibitively expensive even for moderate problem sizes. Hence, we propose a lower overhead scheduling strategy called CAN-THER which can deliver efficient and quick solutions even for large problem sizes. The proposed scheduling strategies have been analyzed and compared using extensive simulation based experiments. Results reveal that CAN-THER is able to achieve performance that is up to 90% of the SMT formulation, while producing solutions at speeds that are approximately 104 times faster for problems having up to 12 CAN flows and 15 TSN switches. © 2024 IEEE.

关键词： Internet protocols

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