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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Chidambaranathan, Senthilnathan Santhanaraj, M. Ajitha, E. Syed Fiaz, A.S. Suchithra, B. Kathirvel, T. Virtusa Corporation NJ United States Kongunadu College of Engineering and Technology Department of IT Tamil Nadu Trichy India St. Joseph's Institute of Technology Computer Science and Engineering India Sona College of Technology Department of Information Technology Salem India Sri Eshwar College of Engineering Department of CSE Coimbatore India

ISBN: (纸本)9798331518097

In today's digital era, cryptographic algorithms play a vital role in safeguarding sensitive information, maintaining confidentiality, and defending against cyberattacks. With the growing variety of encryption methods and modes, identifying the specific algorithm used to generate a ciphertext has become increasingly challenging. To address this, we propose a machine learning solution that automatically identifies cryptographic algorithms using the Random Forest classifier. The system processes a vast dataset called 'encrypted-data,' which contains 39 different algorithms and modes, by extracting essential features like byte frequency and entropy from the ciphertexts. These features form a 257-dimensional Feature vector used for classification. Random Forest models are trained with these data. During testing, models are loaded is used to predict the encryption algorithm for a given ciphertext. Our method offers a scalable and efficient approach to cryptographic identification, surpassing traditional techniques. © 2024 IEEE.

关键词： Encryption algorithms

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Identification of Texts Using ML Algorithm for The Accurate Recognition

Identification of Texts Using ML Algorithm for The Accurate ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Pasha, M Jahir Kajendran, K. Deshmukh, Shrikala Kumar, G. Sreekanth, N. Pund, Sachin G.Pullaiah College of Engineering and Technology Computer Science and Engineering Kurnool India Panimalar Engineering College Dept. of Computer Science and Engineering Chennai India Amity University Mumbai India Bharati Vidyapeeth College of Engineering Pune India Srm Institute of Science and Technology Faculty of Management Kattankulathur India Mallareddy Engineering College for Women Hyderabad India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350357769

A Comparative evaluation of device mastering Algorithms for textual content classification textual content category is an vital assignment in herbal language processing, where the aim is to routinely assign a label or category to a given text file. With the increasing quantity of text facts available in diverse domain names, the need for efficient and accurate text class algorithms has turn out to be extra vital. In this paper, we gift a comparative analysis of machine mastering algorithms for text category. We experiment with one of a kind algorithms, such as aid Vector Machines, Naive Bayes, Random Forests, and Multilayer Perceptron, on numerous benchmark datasets. Our results reveal that the choice of set of rules considerably affects the overall performance of textual content classification, and there may be no unmarried high-quality algorithm that works well for all datasets. We also provide insights into the strengths and weaknesses of each set of rules and talk their applicability in specific eventualities. Our findings can guide researchers and practitioners in selecting the maximum suitable system gaining knowledge of algorithm for their text category duties. Textual content classification, device learning, assist vector machines, naive bays, random forests, multilayer perceptron, and comparative analysis. © 2023 IEEE.

关键词： Multilayer neural networks

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Optimized deep transfer learning techniques for spine fracture detection using CT scan images

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Multimedia Tools and Applications 2025年 1-34页

作者： Kanna, G. Prabu Kumar, Jagadeesh Parthasarathi, P. Bhardwaj, Priya Kumar, Yogesh VIT Bhopal University Bhopal India Department of Computer Science and Engineering Kathir College of Engineering Coimbatore India Department of CS Bannari Amman Institute of Technology Sathyamangalam India School of Computing DIT University Dehradun India Department of CSE School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India

Spine fractures pose an important health concern that requires a quick diagnosis to protect people from any long-term complications. This paper aims to propose an automatic prediction system that uses deep transfer learning techniques for spine fractures and minimizes the time required for classification compared to traditional techniques. The CT scan images of the cervical spine have been collected in the form of JPEG format and subsequently segmented using several phases such as mask exploring, detecting, zooming, and cropping the affected area at 6 different angles followed by the computation of contour features to analyze their geometric as well as intensity-based parameters. Ten advanced deep learning models along with ADAM optimizer are trained where InceptionResNetV2 obtained the highest accuracy of 99.70% while the lowest loss, as well as root mean error square score, has been obtained by ResNet50V2 with 0.0493 and 0.2220.36 respectively. In the case of precision, recall, and F1 score, DenseNet169 computed the highest precision and recall values of 99.99% and 98.495% respectively while Xception did well in terms of F1 score with 98.32%. These models perform better because InceptionResNetV2’s architecture is well-suited for capturing intricate spatial hierarchies, DenseNet169 benefits from dense connectivity for feature reuse, and ResNet50V2’s optimized residual blocks contribute to minimizing error rates. Apart from this, less diversity and class imbalance are also seen within the dataset which affects model performance. These limitations highlight the need for future work to expand the dataset and ensure a balanced distribution of classes to enhance the robustness and clinical relevance of the proposed AI-based system. However, despite these challenges, the results underscore the transformative potential of deep learning in improving clinical diagnosis of spine fractures. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part

关键词： computerized tomography

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Deep Reinforcement Learning-Based Framework for Enhancing Cybersecurity

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cybersecurity

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ML-Based Financial Forecasting in ERP: Improving Budgeting Accuracy with Long Short-Term Memory Networks

ML-Based Financial Forecasting in ERP: Improving Budgeting A...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Al-Attabi, Kassem Rethik, Kumar Ranjusha, J.P. Sindhuja, N. Yadav, Sanjay Shivakalyan, B. The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Science Engineering Punjab Mohali India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Aditya Engineering College Department of Computer Science & Engineering Surampalem India Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India Saveetha Institute of Medical and Technical Sciences Department of CSE-AI Tamil Nadu Chennai India

ISBN: (纸本)9798350390759

This research examines the feasibility of using LSTM networks for financial prediction in frameworks such as the ERP system with an objective of enhancing budgetary prediction. Simple forecasting techniques do not take into account certain dynamics within the data that are non-linear, hence result in small forecast accuracy. Autoencoders can be employed to extract such features and LSTM networks capable of handling sequential data and long term dependencies could be used to anticipate the occurrence of emergencies. The process includes data culling that is composed of extensive datasets, data pre-processing, LSTM model development and training, and ERP system implementation. The findings illustrate that by extending the number of timesteps in LSTM models, it offers more reliability compared to conventional methods that help to improve decision-making and forecasting in finance. It is possible to upgrade them from time to time and make the models as accurate and as current as possible. These specific findings corroborate the overall proposition that discrete advancements in ML algorithms can converge to radically reshape a key area of ERP systems - financial forecasting. © 2024 IEEE.

关键词： Data accuracy

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Optimal Feature Selection and Classification Using Convolutional Neural Network-Based Plant Disease Prediction

Optimal Feature Selection and Classification Using Convoluti...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Lakineni, Prasanna Kumar Reddy, D Jayanarayana Chitra, M. Umapriya, R. Kannan, L. Vadivel Barkunan, S.R. Dadi Institute of Engineering & Technology Department of Computer Science and Engineering Visakhapatnam India G Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chennai India Kpr Institute of Engineering and Technology Department of Chemical Engineering Coimbatore India Psna College of Engineering and Technology Department of Mechanical Engineering Dindigul India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798350398465

Plant disease diagnosis remains a maj or unsolved problem in the changing scientific world. One of the most important agricultural sectors is growing plants with artificial intelligence support. Image analysis and classification techniques are aimed at predicting plant diseases. So far, well-versed farmers or plant specialists can only diagnose plant diseases. Similar results have been shown for almost different plant diseases in the methods thus introduced. To solve such a problem, we have developed an improved Convolutional Neural Network (CNN) method to identify plant diseases faster and better. Initially, the preprocessing task was done by using a wrapping filter. After that, the best features of the plant disease can be selected from the Logistic Decision Regression (LDR). LDR feature selection is used to reduce the classification problem to selecting features of medicinal plants. Leaves are commonly used to identify medicinal plants, branches, flowers, petals, seeds and whole plants for use in automated procedures. Automated disease detection methods are developed based on changes in plant foliar disease states. Convolutional neural networks (CNNs) are the most accurate of the complex feed- forward neural networks in image classification and recognition. By estimating the results, various images can be training have efficient image recognition functions with accuracy and strong reliability. © 2023 IEEE.

关键词： Classification (of information)

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Improving Power Allocation and Installation in WSN Using Novel Nature-Inspired Optimization for Cyber Security Applications 1

Improving Power Allocation and Installation in WSN Using Nov...

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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Balamurugan, K.S. Kalyani, Gedela Al-Fatlawy, Ramy Kurdi, Waleed Hadi Madhloom Alsalamy, Fatima Al-Aboudy, H.M. Manikandan, K. Karpaga Vinayaga College of Engineering and Technology Department of ECE Tamil Nadu Chengalpattu India Karpaga Vinayaga College of Engineering and Technology Big Data Analytics Tamil Nadu Chengalpattu India The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Altoosi University College Department of Computer Science Najaf Iraq Al-Mustaqbal University Medical Laboratories Techniques Department Hillah Iraq Mazaya University College DhiQar Department of Computer Techniques Engineering Iraq Sona College of Technology Department of Biomedical Engineering Tamil Nadu Salem India

ISBN: (纸本)9798350367003

Wireless sensor network (WSN) installation and power allocation optimization for cybersecurity purposes are difficult tasks which include a well-thought-out method that strikes a balance between goals such as the energy efficiency, connectivity and cyber threat resistance while improving overall system performance and cybersecurity. A novel multi-task walrus optimization (MTWO) technique is offered in this study. The installation and power allocation problem (IPAP) with several jobs is established in this research. The suggested MTWO is used to split the IPAP into numerous scalar components, which are consequently grouped and treated by their desired goals. We evaluate the proposed MTWO approach using simulations and show that it works well in practice. The findings demonstrate that power distribution and installation for cybersecurity activities on WSNs can be improved by the MTWO. The superiority of the problem specific MTWO over the MOGA has been demonstrated by simulation outcome in several network cases, offering a wide range of excellent network designs to aid in the decision-maker selection. © 2024 IEEE.

关键词： Energy efficiency

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Local Overlapping Attentional Selection with VIT for Image Classification in Remote Sensing

Local Overlapping Attentional Selection with VIT for Image C...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Zou, ChangMan Jeon, Wang-Su Rhee, Sang-Yong University of Kyungnam Department of IT Convergence Engineering Changwon51767 Korea Republic of College of Computer Science and Technology Beihua University Jilin132013 China University of Kyungnam Department of Computer Engineering Changwon51767 Korea Republic of

ISBN: (纸本)9798331510756

Remote sensing image classification presents unique challenges due to the complexity of the scenes and the variability in visual features caused by the overhead perspective. Traditional convolutional neural networks (CNNs) struggle with long-range dependencies and extracting sufficient context for accurate classification. To address this, we propose the LSViT model, a novel approach that incorporates a local attention selection module into the Transformer architecture, specifically designed for remote sensing scene classification. LSViT enhances the classification performance by selecting discriminative tokens and removing redundant information. Our model leverages the Transformer's capacity for capturing both global and local features, improving the focus on important image regions. We evaluate LSViT on two benchmark datasets, AID and NWPU-RESISC45, and demonstrate its competitive performance, achieving state-of-the-art accuracy compared to traditional CNN and other Transformer-based methods. Notably, the local attention selection module enhances the model's ability to classify scenes with overlapping objects and complex backgrounds, making it an effective solution for large-scale remote sensing image classification tasks. © 2025 IEEE.

关键词： Convolutional neural networks

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Identifying legitimate user in DDoS attack using Petri net

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Materials Today: Proceedings 2022年 64卷 859-864页

作者： Kavitha, K. Babitha, T. Praveena, V. Devika, P. Department of Mathematics Dr. NGP Arts and Science college Coimbatore India Department of Mathematics Government Arts and Science College Mettupalayam India Department of Computer Science and Engineering Dr. N.G.P Institute of Technology Coimbatore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

The effective construction of systems and that are embedded in an automated or organisational context is a major difficulty in modern research. These systems are built so that customers, manufacturers, users, and better understand of a system and how it can be installed, used, altered, and enhanced with the help of the model. Petri nets are used to represent and investigate discrete systems such as running in parallel, web - based, concurrent, and non-deterministic systems, among others. It has a strong mathematical foundation and also the ability to model in a graphical form. With its significant development and advancements in recent years, network has pervaded every part of our lives. As a result of this process, a growing number of critical network security issues have arisen, including network intrusion issues. The aim of this article is to present an approach to develop and verify a method for modeling of network attacks either at server side or client side. Moreover the goal is to prove that the method enables one to design models resembling the same behaviour of attacks that support the detection process of selected network attacks and facilitate the application of countermeasures. The most common network threat targeting end users and servers are caused by DDoS. The DDoS detection process can be performed either by understanding the properties of attacks or analysing its behavioural models. The proposed approach to modeling of attacks is based on Petri nets. The designed framework is designed detecting DDoS attack at the server side and mitigates the network attacks. The system's major elements are detection and packet classification based on the confidence index value. The CI value helps to detect untrusted network attacks and prioritize the attacks. An algorithm for detecting untrusted network attack is designed and modelled using Petri nets. © 2022

关键词： Petri nets

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Biomechanically Sourced Hybrid Energy Harvester - Towards a Sustainable Environment

Biomechanically Sourced Hybrid Energy Harvester - Towards a ...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Linganiveth, S.G. Amrudha Harshan, P.R. Shyam Sittharth, E. Abhilash, R. Umesh, M.V. Hariharan, J. Kumaraguru College of Technology Department of Electronics and Communication Engineering Coimbatore India Kumaraguru College of Technology Department of Electrical and Electronics Engineering Coimbatore India Kumaraguru College of Technology Department of Computer Science Engineering Coimbatore India Kumaraguru College of Technology Department of Electronics and Instrumentation Engineering Coimbatore India

ISBN: (纸本)9781665462631

With the growing population and increased modernization, the demand for Power and energy is still increasing swiftly. Increase in the demand for Power and energy, non-renewable resources like oil, petroleum and coal are being exhausted, causing more threats to Mother Earth. Depletion and exhaustion of natural resources result in global warming and pollution. Introduce energy scavenging from renewable resources and other forms of green energy, which replenish itself on a human timescale. Green energies are becoming increasingly popular as an alternative to diminishing pollution. Energy harvesters constitute a feasible solution to overcome the depletion of non-renewable resources and the pollution caused by extracting those resources. This paper describes the process of optimum energy harvesting by using piezoelectric, triboelectric, and electromagnetic generators pasted on a tile structure. The design of the energy harvester floor tile is for electricity generation. In this work, Multiple energy harvesting techniques like piezoelectric-triboelectric Nanogenerator (TENG)-Electro Magnetic Generator (EMG) based hybrid floor-tile developed to utilise the biomechanical energy from human footsteps. Electrical energy generation with harvester floor tiles depends on the amount of load applied to the sensors. The load applied on sensors depends on various factors, like the pressure exerted on the tile area of contact by the pressure exerting the object in the tile. The developed hybrid-based floor tile allows for cost-effective, green, and sustainable energy conversion and promotes sustainable development. © 2023 IEEE.

关键词： Power generation

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