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A Novel Fuzzy-Based Modified GAN and Faster RCNN for Classification of Banana Leaf Disease

Journal of The Institution of Engineers (India): Series A 2023年第3期104卷 529-540页

作者： Raja, N. Bharathi Rajendran, P. Selvi Department of Computer Science and Engineering Hindustan Institute of Technology and Science Tamil Nadu Kelambakkam India

In the global marketplace, agriculture plays an important role. However, diseases produced in plants mostly affect the financial system. Pest occurrence and climatic changes are the leading trouble that the banana plant leaf faces, which influences the quality of fruit and crop yields. To predict the leaf disease symptoms in an early stage, the automatic banana leaf disease detection technique is more significant. The traditional deep neural network methods used in banana plant leaf detection need more parameters and utilize complex architectures. To address such problems, modified generative adversarial networks–modified faster region-based convolutional neural networks with fuzzy (MGAN–MFRCNN with Fuzzy) is proposed for identifying banana leaf disease. The most effective framework in deep learning is GAN. The autoencoder is incorporated within the fundamental GAN design for developing a modified GAN model;it is used for data augmentation. To perform feature extraction from high-resolution networks, an MFRCNN is used in our proposed method. The improved and more accurate classification scores are obtained by combining fuzzy with the ensemble model. The experimentation is carried out using a banana plant leaf dataset in which the healthy leaf, xanthomonas-affected leaf, and sigatoka-affected leaf diseases are detected and classified. The proposed model reached 98% accuracy, 97% precision, 96% F1-score, 98% specificity, and 98% sensitivity, which is better than other existing methods. © 2023, The Institution of Engineers (India).

关键词： Generative adversarial networks

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REGAC: Multi-Class Offensive Content Identification Using Graphical Approach

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 931-952页

作者： Chinivar, Sneha Roopa, M.S. Arunalatha, J.S. Venugopal, K.R. Department of Computer Science and Engineering Bangalore University K R Circle Karnataka Bengaluru India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering Bangalore University K R Circle Karnataka Bengaluru India Karnataka Bengaluru India

Online offensive behaviour continues to rise with the increasing popularity and use of social media. Various techniques have been used to address this issue. However, most existing studies consider offensive content identification as a binary or ternary problem, disregarding the potential for multi-class classification of derogatory content. In this work, we propose RoBERTa Embedding-based Graphical Approach Classifier (REGAC), which aims to designate the identified offensive content into varied classes,like age, gender, ethnicity, religion, and others to understand what exact qualities the bully usually targets in their victim so that pertinent measures can be taken to battle them. Additionally, the models’ efficiency is evaluated using an unbalanced dataset to identify types of offensive content. The word embeddings are generated for a balanced dataset using RoBERTa embedding model to bring out the best-fitting vectors. These vectors are then input into traditional Machine Learning algorithms (SVM, KNN, Logistic Regression, Random Forest, and XGBoost) and graph-based algorithms (Graph Convolution Network (GCN), GraphSAGE, and Graph Attention Network (GAT)) for fine-grained categorization of offensive textual content. The experimental results demonstrate the efficiency of combating social media’s offensive content of the proposed work with higher Precision, Recall, F1-Score, and Accuracy compared with most state-of-art approaches. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Graph embeddings

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Task scheduling and data replication in cloud with improved correlation strategy

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International Journal of computers and Applications 2023年第11期45卷 697-708页

作者： Rambabu, D. Govardhan, A. Department of Computer Science and Engineering Sreenidhi Institute of Science and Technology Hyderabad India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Hyderabad Hyderabad India

Cloud providers frequently utilize two tightly coupled resource management strategies like task scheduling & data replication to boost the performance of the system generally, guaranteeing service level agreement (SLA) compliance, as well as protecting their own financial gain. An Improved Correlation strategy-based Task Scheduling and Data Replication in Cloud (ICTSDC) is what this work aims to give. The suggested system's primary phases are as follows: Management of replication and task scheduling. Initial job scheduling will be optimization-based and take into account goals such bottleneck value, migration cost, VM load, enhanced correlation, and replication, respectively. For this, a brand-new extended DMO algorithm called Self-adaptive Dwarf Mongoose Optimization (SADMO) is presented. In the replication management stage, the potential copies must first be identified based on the prior objective. The suggested SADMO model implements the optimization technique for replica placement throughout the replication management process. The outcomes of the ICTSDC technique are evaluated to other methods using a variety of metrics, like bottleneck value, migration cost, Virtual Machine (VM) load, improved correlation, as well as replication efficiency. A lower mean value of 0.324 is gained with the ICTSDC scheme for fitness. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Virtual machine

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WGSDMM+GA: A genetic algorithm-based service clustering methodology assimilating dirichlet multinomial mixture model with word embedding

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Future Generation computer Systems 2023年 145卷 254-266页

作者： Agarwal, Neha Sikka, Geeta Awasthi, Lalit Kumar Computer Science and Engineering Department Indian Institute of Information Technology Karnataka Raichur584135 India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi110036 India 246174 India

Advancement of web 2.0 results in the expeditious growth of services in repositories and service portals, which raises the demand for service management. With the clusters of services, different processes like discovery, selection, ranking, etc., can be expedited efficiently. K-Means clustering is highly recommended for service clustering but it can be stuck in the local optimum values. Developers generally use short text to describe the functionality of Web services. For effective service clustering, it is required that relevant features of services are mapped in vector space based on semantic relations. Gibbs Sampling algorithm for Dirichlet Multinomial Mixture model (GSDMM) model is able to provide good performance on short text because it works on the assumption that there will be one topic related to one document. In this paper, a novel technique for web service clustering, i.e., WGSDMM+GA, is proposed in which relevant features, semantic relations, and their mappings in vector space are analyzed based on an assimilated approach of Word2Vec and GSDMM models. As K-means clustering cannot provide effective results due to local optima problem, GA-based clustering is applied in which fitness function is based on Manhattan distance. The proposed model is applied to five real-time datasets, and the performance is evaluated based on standard evaluation measures. Experimental results illustrate that WGSDMM+GA yields better results, and the accuracy score is increased by 23%, 17%,17%, 21%, and 25% on five real-time datasets. © 2023 Elsevier B.V.

关键词： Genetic algorithms

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Exploring Personalized Internet of Things(PIoT),social connectivity,and Artificial Social Intelligence(ASI):A survey

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High-Confidence Computing 2024年第3期4卷 136-162页

作者： Bisma Gulzar Shabir Ahmad Sofi Sahil Sholla Department of Information Technology National Institute of TechnologySrinagar 190001India Department of Computer Science and Engineering Islamic University of Science and TechnologyGazipur 1700India

Pervasive Computing has become more personal with the widespread adoption of the Internet of Things(IoT)in our day-to-day *** emerging domain that encompasses devices,sensors,storage,and computing of personal use and surroundings leads to Personal IoT(PIoT).PIoT offers users high levels of personalization,automation,and *** proliferation of PIoT technology has extended into society,social engagement,and the interconnectivity of PIoT objects,resulting in the emergence of the Social Internet of Things(SIoT).The combination of PIoT and SIoT has spurred the need for autonomous learning,comprehension,and understanding of both the physical and social *** research on PIoT is dedicated to enabling seamless communication among devices,striking a balance between observation,sensing,and perceiving the extended physical and social environment,and facilitating information ***,the virtualization of independent learning from the social environment has given rise to Artificial Social Intelligence(ASI)in PIoT ***,autonomous data communication between different nodes within a social setup presents various resource management challenges that require careful *** paper provides a comprehensive review of the evolving domains of PIoT,SIoT,and ***,the paper offers insightful modeling and a case study exploring the role of PIoT in post-COVID *** study contributes to a deeper understanding of the intricacies of PIoT and its various dimensions,paving the way for further advancements in this transformative field.

关键词： Personal Internet of Things(PIoT) Artificial Social Intelligence(ASI) Hyperpersonalization

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Utilization of Two Cross Two Switch for Bitonic Sorting Circuit

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Micro and Nanosystems 2025年第1期17卷 45-57页

作者： Bhattacharjee, Subhasree Sarkar, Soumyadip Das, Kunal Sadhu, Arindam Sarkar, Bikramjit Department of Computer Application Narula Institute of Technology Kolkata India Department of Computer Science Acharya Prafulla Chandra College Kolkata India Department of Electronics & Communication Engineering Greater Kolkata College of Engineering and Management West Bengal India Department of Computer Science & Engineering JIS College of Engineering West Bengal India

Background: The synthesis of reversible logic has gained prominence as a crucial research area, particularly in the context of post-CMOS computing devices, notably quantum computing. Objective: To implement the bitonic sorting circuit, 2x2 switch, is used. The overarching objective of implementing a reversible sorting circuit is to mitigate power consumption while promising secure communication. The successful implementation of the bitonic sorting circuit utilizing the proposed 2x2 switch is validated through simulations conducted in IBM Qiskit. Methods: This study begins by employing the control swap gate to implement a 2x2 switch, aimed at enabling concurrent computing operations within multiprocessor systems to enhance throughput. Subsequently, following the successful implementation of the 2x2 switch, the study proceeds to design a bitonic sorting circuit. Results: Bitonic sort using a 2x2 switch on the IBM Quantum Experience (IBMQ) platform via the QISKit SDK in Python 3.6. Specifically, Qiskit v0.29.0 is utilized, with OpenQASM. OpenQASM serves as the quantum assembly and instruction language. Conclusion: This work presents a methodology for constructing a 2x2 switch, utilizing a reversible control swap gate as its core component. Additionally, it demonstrates the intricate development and implementation of a bitonic sorting circuit, capitalizing on this innovative switch architecture. © 2025 Bentham science Publishers.

关键词： Quantum computers

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ConciseNet: A Robust Model for Enhancing Signal Reconstruction in Wireless Sensor Networks 4

ConciseNet: A Robust Model for Enhancing Signal Reconstructi...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kumari, P. Meena Sekar, K. Jagadeesh, P. Dheepa, T. Avn Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India National Institute of Technology Silchar Department of Computer Science and Engineering Assam Silchar India Barath University Department of Computer Science and Engineering Tamilnadu India Kongunadu College of Engineering and Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350343670

This paper introduces a novel data collection model called ConciseNet that leverages a cutting-edge data reconstruction method to significantly enhance signal reconstruction accuracy in wireless sensor networks (WSNs). In traditional WSNs, data collection is prone to errors and losses, especially in long-range scenarios. The proposed model addresses this challenge by employing ConciseNet, an attentive sequential model that effectively compresses sensor signals without compromising information fidelity. The unique strength of ConciseNet lies in its ability to capture essential features from raw sensor data, allowing for efficient and precise reconstruction over extended periods. The result is a more robust and accurate representation of collected data, thereby improving the quality of information gathered by the WSN and maximizing the lifetime of sensors and WSNs. © 2024 IEEE.

关键词： Compressive sensing Data Reconstruction Time dependency Transformer Neural Network Wireless Sensor Network

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Enhancing Mental Health Support: An LLM-Based Prompt engineering Method 8th

Enhancing Mental Health Support: An LLM-Based Prompt Enginee...

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Kaysar, Md Nadim Shiramatsu, Shun Computer Science & Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (纸本)9789819793266

Mental health disorders are conditions of the mind that cause changes in emotion, thought, and behavior. It can be connected with distress and problems functioning in social, professional, or family activities. This study tries to address this issue and improve the user’s mental health situation by using the proposed LLM-based Agent system. Moreover, this research study tries to determine the user’s current situation through text-based conversation and provide suggestions to improve his mental health problems. Therefore, we developed this system by using LLM-based nCoT (nested Chain of Thought) prompt engineering. In the result section, we sequentially obtained the following: a patient goal achievement score (Average: 5.42), patient Satisfaction score (Mean: 5.57), system response Correctness score (Mean: 6.09), and system Engagement Score (Mean: 6.16). In addition, we analyzed the based CTRS and we obtained all item’s averages (Mean: 5.70), and the standard deviation (Mean: 0.91). Moreover, a system usability scale framework was used for the analysis of the usability (Mean Score: 3.60) of our proposed system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Chatbot CoT Dialogue system LLM Mental Health nCoT

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An approach to on-stream DDoS blitz detection using machine learning algorithms

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Materials Today: Proceedings 2023年 80卷 3492-3499页

作者： Manjula, H.T. Neha Mangla Atria Institute Of Technology Bengaluru India Department Of Computer Science & Engineering CMRIT Bengaluru India Department of Information Science & Engineering Atria Institute of Technology Bengaluru India

Distributed Denial of service (DDoS) attacks is an enormous threat to today's cyber world, cyber networks are compromised by the attackers to distribute attacks in a large volume by denying the service to legitimate users. The toughest and challenging task in today's network and network security engineers is to identify compromised traffic (attacked) and legitimate (normal) traffic. The main goal of the paper is to detect DDos attacks using classification algorithms. To achieve the goal the proposed system uses attacking tool to initiate attacks using Loic attacking tool with the data set extracted from open source tool Wireshark and transferring the dataset to apache Spark for detection analysis. The system also uses Apache spark machine learning algorithms (MLib), classification algorithms to classify the dataset. We use Naive Bayes, KNN and Random forest classification algorithms to classify normal traffic and attacked traffic. Our system is capable of detecting attacks with respect to any traffic protocols ICMP, TCP, or UDP. The accuracy of detection is compared on three classification algorithms and noted that random forest gives the accuracy of 96.75%. © 2021

关键词： Denial-of-service attack

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Deep Learning Strategies for Identifying Machine-Generated Text 19

Deep Learning Strategies for Identifying Machine-Generated T...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Yadagiri, Annepaka Pakray, Partha Computer Science & Engineering National Institute of Technology Silchar India

ISBN: (纸本)9798331507817

Generative AIs like LLMs are now accessible to the general public. For example, students can utilize these tools to create essays or complete theses. However, how is a teacher supposed to determine if a text was composed by the student or an AI? Using deep learning techniques, we investigate novel and classic approaches for detecting text created by artificial intelligence. We also study the more complex instance when the AI is asked to write the text in a way that a human would not recognize as AI-generated, as we discovered that categorization is more challenging in this scenario. For our studies, we used llm-detect-ai-generated text from the Kaggle competition dataset, which included texts written by students and texts produced using different LLMs. Our top systems achieve an accuracy of 0.98% and F1 scores of more than 0.98% in classifying simple and complex texts produced by humans and AI-Genereted. The systems combine features such as TF-IDF vectorization, word2Vec, and word embedding properties. Our findings demonstrate that these additional characteristics significantly enhance the performance of several classifiers. Compared to deep learning models, our best-performing model for detecting AI-generated text outperforms even the fine-tuned ROBERTA-Open-AI classifier, achieving an accuracy of 0.98%. This underscores the efficacy of our proposed approach in distinguishing between human and AI-generated content. © 2025 IEEE.

关键词： Adversarial machine learning

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