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Statistical methods for feature selection: unlocking the key to improved accuracy

International Journal of computers and Applications 2023年第6期45卷 433-443页

作者： Thiyam, Bidyapati Dey, Shouvik Department of Computer Science and Engineering National Institute of Technology Nagaland Dimapur India

The ever-growing amount of data generated by modern networks poses significant challenges for intrusion detection systems (IDS) in effectively analyzing and classifying security risks. Therefore, it is crucial to identify the most biased characteristics for building efficient and effective IDS algorithms. However, not all features are equally informative or relevant for intrusion detection. In response to these problems, this study proposes a Hybrid approach that uses traditional and advanced statistical techniques. The proposed method effectively validates the features generated from the hybrid model and set-operation theorem to provide the best optimal subset of features for IDS. Various machine learning methods are used to test the proposed model on three popular IDS datasets: NSL-KDD, UNSW NB15, and CIC-DDoS2019. The experimental findings show that the suggested hybrid technique improves IDS performance effectively and efficiently, providing a viable answer to the issues that intrusion detection systems confront. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Intrusion detection

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Optimal trained ensemble of classification model for satellite image classification

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Multimedia Tools and Applications 2025年第7期84卷 3745-3766页

作者： Singh, Sarabjeet Kundra, Harish Kundra, Sheetal Pratima, P. Venkata Devi, M. V. Anjana Kumar, Sushil Hassan, Mariya Department of Computer Science and Engineering Chandigarh College of Engineering and Technology Chandigarh160019 India Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Ranga Reddy District Telangana Ibrahimpatnam501506 India Department of Artificial Intelligence and Data Science Guru Nanak Institute of Technology Ranga Reddy District Telangana Ibrahimpatnam501506 India Department of Computer Science and Engineering Central University of Haryana Jant-Pali Mahendergarh123031 India

Satellite image classification is the most significant remote sensing method for computerized analysis and pattern detection of satellite data. This method relies on the image's diversity structures and necessitates a thorough validation of the training data using the chosen classification algorithm. The classifying of remotely sensed images refers to the process of classifying pixels into a fixed set of distinct classes based on their data values. The pixel is categorized into that class if it complies with a specific set of requirements. A new SIC scheme is suggested in this work, where, Contrast limited adaptive histogram equalization (CLAHE) is deployed for pre-processing the image. Further, statistical features, Vegetation Indices (VI) and Improved Hybrid Vegetation Index (IHVI) are extracted from the preprocessed image. Then, we deploy 2 phase classification process for classifying the image. In the first phase, Bi-Directional Gated Recurrent Unit (BI-GRU), Convolutional Neural Network (CNN) and Deep Max Out (DMO) are used. In 2nd phase, optimized RNN is used for determining the final classification result. Also, the training of the model is done by the new Chaotic Opposition Behavior Improvised Coot Algorithm (COBI-CA) optimization via tuning the optimal weights. The COOT method has been enhanced and is now the suggested algorithm. Lastly, we conduct an analysis to compare the suggested Satellite Image Classification (SIC) model's performance to the traditional approaches in terms of statistical analysis, precision, accuracy, and error measure. In comparison to other conventional methods, the accuracy of the suggested model is 97%. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2024.

关键词： Pixels

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MindCare: Machine Learning based Personalized Mental Health Monitor 4

MindCare: Machine Learning based Personalized Mental Health ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ushashree, P. Naik, Archana Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331540364

Mental health challenges are growing in recent years, emphasizing the need for effective monitoring and inter- vention systems. This paper presents a comprehensive approach to detect mental unstabilities by analyzing the text entered by the user. Utilizing a combination of sentiment analysis and machine learning techniques, the proposed system preprocesses text data to extract sentiment scores and other key features, which are then used to train models for accurate mental health assessments. The system also offers personalized recommendations based on the user's mental state. Experimental results demonstrate the effectiveness of the approach, with the model achieving accuracy of 82.31% for negative text classification. These results underscore the potential of the system to serve as a valuable tool in the early detection and management of mental health issues. © 2024 IEEE.

关键词： Electronic health record

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IoV-fog-cloud framework for road anomalies detection using SVM-nAVDD approach

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 10899-10915页

作者： Kumar, Navin Sood, Sandeep Saini, Munish Department of Computer Engineering and Technology Guru Nanak Dev University Punjab Amritsar India Department of Computer Applications National Institute of Technology Haryana Kurukshetra India

The detection of road anomalies in highway management is a crucial yet manpower-demanding task. Although there have been many previous types of research based on visual and acceleration data collected from vehicles. However, to distinctively differentiate a pothole and speedbump from acceleration remains a challenge. The visual methods require high computation supported by expensive GPUs. This paper proposes a novel contribution to depth detection using angular velocity for pothole detection. The solution is accorded by three-layer architecture inclusive of fog-based cloud infrastructure where each layer performs a respective function based on computing capability. The data acquisition is accomplished using an IoV setup equipped with M8N GPS, and MPU-6050 mounted on an Arduino MEGA 2560. The results are compared with the state-of-art algorithms and methodologies. The proposed SVM-nAVDD achieved the precision of 0.9092–0.9123 for the tested datasets and depth/height detection with 92% accuracy. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Data acquisition

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A Novel and Optimized Collaborative Diversity-Driven Routing Mechanism in MANETs

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SN computer Science 2024年第1期5卷 22页

作者： Malyadri, Neelam Ramakrishna, M. Department of Computer Science and Engineering Vemana Institute of Technology VTU Belagavi India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Computer Science and Engineering Vemana Institute of Technology Karnataka Bengaluru India

Wireless networks such as MANETs present unique challenges due to their dynamic and decentralized nature. Efficient routing protocols are essential for achieving reliable and robust communication in such networks. In this research, we propose a novel routing mechanism called collaborative diversity-driven routing (CollabNet) that integrates collaborative diversity to enhance the routing process in ad hoc networks. CollabNet utilizes a collaborative approach to address the challenges posed by dynamic network environments. CollabNet incorporates collaborative power control, adaptive power allocation, and energy-efficient routing to automatically adjust the dynamic power of nodes. By promoting collaborative behavior and optimizing power levels. It fosters collaborative behavior among nodes by establishing cooperation groups and leveraging multiple relay nodes. Through collaborative multipath diversity CollabNet facilitates the transmission of packets toward the destination via multiple paths within the transmission range. This approach increases the probability of successful packet delivery by establishing redundant connections. By promoting collaboration among nodes, CollabNet improves the reliability and connectivity of wireless ad hoc networks. The mechanism leverages the strength of collaborative diversity to enhance the overall performance of routing protocols. Extensive simulations demonstrate that CollabNet outperforms traditional routing protocols in terms of network connectivity, reliability, and efficiency. The proposed mechanism offers a promising solution for efficient routing in MANETs contributing to the advancement of wireless communication in dynamic ad hoc network environments. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Collaborative diversity Collaborative diversity-driven routing MANETs Routing protocols Wireless ad hoc networks

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Sensitive information leakage analysis of database code by abstract interpretation

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International Journal of Security and Networks 2023年第2期18卷 91-105页

作者： Jana, Angshuman Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

An information system stores outside data in the backend database to process them efficiently and protects sensitive data from illegitimate flow or unauthorised users. However, most information systems are made in such a way that the sensitive information stored in a database may be leaked explicitly or implicitly during data processing along with the control structure of the program to the output channels. Therefore, sensitive data leakage is one of the crucial security threat. In this paper, the main objective is to detect the illegitimate flow of confidential information in an information system. We propose a framework to detect sensitive information leakage through the data-flow paths of an information system. In particular, to compute the precise set of data-flow paths, we use the non-relational abstract property of the interval domain and the relational abstract property of the polyhedra domain that enables the framework to produce efficient security analysis results. © 2023 Inderscience Enterprises Ltd.

关键词： Formal methods

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Fuzzy modelling techniques for improving multi-label classification of software bugs

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International Journal of Innovative Computing and Applications 2023年第3期14卷 141-154页

作者： Panda, Rama Ranjan Nagwani, Naresh Kumar Department of Computer Science and Engineering National Institute of Technology Chhattisgarh Raipur India

Software bug repositories stores a wealth of information related to the problems that occurred during the software development. Today’s software development is a modular approach, with multiple developers working in different locations all around the world. A software bug may belong to multiple categories and can be resolved by more than one developer. For understanding the multiple causes of software bugs and proper bug information management at large bug repositories, better classification of software bugs is needed. In the proposed work, a multi-label fuzzy system-based classification (ML-FBC) is proposed. A fuzzy system is used to compute the membership of software bugs into multiple categories. Then a fuzzy c-means clustering algorithm is used to create various clusters. Once the clusters are created, the cluster-category mapping is done for various software bugs. For a new bug, the fuzzy similarity values are computed, and the created cluster-category mappings are utilised to categorise it. Using a user-defined threshold value, a new bug is classified into multi-label categories. Experiments are carried out on available benchmark datasets to compare the performance measures F1 score, BEP score, Hloss, accuracy, training time, and testing time of various multi-label classifiers. The proposed ML-FBC outperforms existing multi-label classifiers. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Information management

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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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Energy-efficient resource allocation in blockchain-based Cybertwin-driven 6G

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 103-114页

作者： Salim, Mikail Mohammed Pan, Yi Park, Jong Hyuk Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of Department of Computer Science Georgia State University Atlanta United States

Inefficient task scheduling schemes compromise network performance and increase latency for delay intolerant tasks. Cybertwin based 6G services support data logging of operational queries for appropriate resource allocation in Multi-Access Edge and Cloud Computing. In this paper, a Cybertwin assisted task scheduling scheme is proposed for energy-efficient job allocation to Edge-Cloud based on delay tolerance. Cybertwins using the Logger Function and the Digital Asset Function exchange Digital Assets with cloud operators using Smart Contracts to ensure maximum computational resources. The novelty of this study resides in using Communication Assistant Cybertwins to build profiles for all network entities and assign tasks based on their delay tolerance and the available resources of each Edge Datacenter and Cloud service provider. Compared with the Full Offloading scheme, experimental results demonstrate the higher energy efficiency of the task scheduling scheme. Offloading tasks to multiple data centers and smart contract-based selection of cloud operators result in reduced computation time. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Smart contract

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