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2025 International Conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Pradeep Kumar, B.P. Bhagya, D.J. Gagana, G. Mani, N. Dept. of Computer Science and Engineering Atria Institute of Technology Karnataka Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538538

By examining patterns in the text data and metadata linked to job adverts, The use of learning algorithms has grown in popularity in the detection of fraudulent job postings. For this, Research Machine emphasises the application of a number of algorithms, such as Naive Bayes and Random Forest, Together with models for deep learning, like CNN and Bidirectional LSTM, and AdaBoost. To extract useful features These techniques depend on text preparation techniques such as lemmatisation, tokenisation, and TF-IDF vectorisation. To differentiate between authentic and fraudulent posts, models are instructed in datasets and evaluated using measures such as accuracy, accuracy, memory, and F1-score. Advanced classifiers and group techniques have proven to be quite successful in enhancing detection performance while lowering false positives and negatives. © 2025 IEEE.

关键词： Random forests

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A Fertilizer Recommendation System for Rice Leaf Plant 9

A Fertilizer Recommendation System for Rice Leaf Plant

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Murugan, Subhiksha Manikandan, A. Hariprasad, S. College of Engineering and Technology Srm Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur Chennai603203 India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Chennai603203 India

ISBN: (纸本)9798350377972

The secret to increasing crop productivity and sustainability is precision farming. Optimizing fertilizer use is essential in rice cultivation to satisfy the unique nutrient requirements of crops while reducing environmental effect. Conventional techniques focus on general principles, which frequently result in reduced yields, deteriorated soil, and inefficient nutrient usage. The AI-based fertilizer recommendation system RiceFert, created for rice leaf plants, is presented in this work. The system examines crop-specific information, soil characteristics (such as type and nutrient levels), and environmental data (temperature, humidity, and wetness) using a Random Forest (RF) model and deep learning techniques. Data preparation, feature extraction, and model training are all part of the process, which compares classifiers such as Random Forest, SVM, Gradient Boosting, and Naive Bayes. The Random Forest model is superior at recommending fertilizer based on performance criteria like accuracy, precision, recall, and F1-score. This focused strategy reduces excessive or inadequate fertilizer use, increasing rice output and promoting sustainable farming. RiceFert demonstrates the potential of AI in precision agriculture by offering farmers personalized guidance that improves crop output, lowers costs, encourages environmental conservation, and helps farmers make better decisions. © 2024 IEEE.

关键词： Deep Learning Fertilizer Recommendation Precision Agriculture Rice Crop

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Secure and Energy Concise Route Revamp Technique in Wireless Sensor Networks

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Intelligent Automation & Soft computing 2023年第2期35卷 2337-2351页

作者： S.M.Udhaya Sankar Mary Subaja Christo P.S.Uma Priyadarsini Department of Information Technology Velammal Institute of TechnologyChennai601204India Department of Networking and Communication SRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600124India

Energy conservation has become a signiﬁcant consideration in wireless sensor networks(WSN).In the sensor network,the sensor nodes have internal batteries,and as a result,they expire after a certain *** a result,expanding the life duration of sensing devices by improving data depletion in an effective and sustainable energy-efﬁcient way remains a ***,the clustering strategy employs to enhance or extend the life cycle of *** identify the supervisory head node(SH)or cluster head(CH)in every grouping considered the feasible strategy for power-saving route discovery in the clustering model,which diminishes the communication overhead in the ***,the critical issue was determining the best SH for ensuring timely communication *** secure and energy concise route revamp technology(SECRET)protocol involves selecting an energy-concise cluster head(ECH)and route revamping to optimize *** sensors transmit information over the ECH,which delivers the information to the base station via the determined optimal path using our strategy for effective data *** modeled our methods to accom-plish power-efﬁcient multi-hop ***,protected navigation helps to preserve energy when *** suggested solution improves energy savings,packet delivery ratio(PDR),route latency(RL),network lifetime(NL),and scalability.

关键词： Wireless sensor network wireless security wireless routing clustering ad hoc network

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CNLP-NITS-PP at GenAI Detection Task 1: AI-Generated Text Using Transformer-Based Approaches 1

CNLP-NITS-PP at GenAI Detection Task 1: AI-Generated Text Us...

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1st Workshop on GenAI Content Detection, GenAIDetect 2025

作者： Yadagiri, Annepaka Krishna, Reddi Mohana Teja, L.D.M.S. Sai Vardhan, M. Srikar Pakray, Partha Department of Computer Science & Engineering National Institute of Technology Assam Silchar788010 India

ISBN: (纸本)9798891762053

In the current digital landscape, distinguishing between text generated by humans and that created by large language models has become increasingly complex. This challenge is exacerbated by advanced LLMs such as the Gemini, ChatGPT, GPT-4, and LLaMa, which can produce highly sophisticated, human-like text. This indistinguishability introduces a range of challenges across different sectors. Cybersecurity increases the risk of social engineering and misinformation, while social media aids the spread of biased or false content. The educational sector faces issues of academic integrity, and within large, multi-team environments, these models add complexity to managing interactions between human and AI agents. To address these challenges, we approached the problem as a binary classification task using an English-language benchmark COLING dataset. We employed transformer-based neural network models, including BERT, DistilBERT, and RoBERTa, fine-tuning each model with optimized hyperparameters to maximize classification accuracy. Our team, CNLP-NITS-PP has achieved the 23rd rank in subtask 1 at Coling-2025 for machine-generated text detection in English with a Main Score (F1 Macro) of 0.6502 and micro-F1 score of 0.6876. © 2025 International Conference on Computational Linguistics.

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Bank Customer Churn and Extra Benefits Prediction using Machine Learning Model 2

Bank Customer Churn and Extra Benefits Prediction using Mach...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Jothi Kumar, C. Anumala, Akkash Siva, E.P. Bhuvanesh, E.S. Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Mathematics SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Bank customer churn, which results in lost revenue and decreased customer loyalty, is a major concern for banks. Retention tactics that work and precise churn prediction are crucial for resolving this problem. In order to predict customer churn and suggest additional benefits, this study presents the Extra Benefit Classifier, a cutting-edge machine learning model that combines the best features of the Random Forest Classifier and Extra Tree Classifier. A dataset with customer data, including demographics, transaction history, and interaction details, is used to train the model. Results from the experiment show how well the Extra Benefit Classifier predicts churn and suggests tailored benefits. The Extra Benefit Classifier performs better than conventional models in terms of efficiency and accuracy. This study offers insights and advances the field of customer churn prediction. This study contributes to the field of customer churn prediction and provides insights for developing proactive customer retention strategies in the banking sector. © 2024 IEEE.

关键词： Prediction models

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MediHealth: An AI-Driven Mobile Solution for Enhanced Health Care Decision-Making and Accessibility 3

MediHealth: An AI-Driven Mobile Solution for Enhanced Health...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Raj, Sukrutha Shekhar, Shashank Baskaran, P. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India

ISBN: (纸本)9798331527549

Mobile health (mHealth) applications have revolutionized healthcare by enhancing patient engagement, providing tools for self-management, and supporting better health outcomes. This paper explores the development of an innovative mHealth application designed to empower patients in making informed healthcare decisions. The application leverages advanced features such as AI powered chatbots, personalized healthcare recommendations, and a report and prescription summarizer to simplify complex medical information. By allowing users to easily find appropriate healthcare professionals, access detailed prescription data, and receive symptom-based specialist recommendations, the app facilitates effective self-management of chronic conditions. Furthermore, it offers proactive care prompts, personalized advice, and health behavior reminders to promote better overall health outcomes. Despite challenges in data security, privacy, and equitable access, the study emphasizes the importance of a user-centered design approach in ensuring the widespread adoption and success of mHealth applications. Ultimately, the goal is to enhance healthcare delivery by improving patient engagement, facilitating seamless access to healthcare resources, and enabling well-informed decision-making. This application presents a holistic solution for advancing patient care across diverse healthcare settings. © 2025 IEEE.

关键词： Electronic health record

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Food Supply Chain Management Using Hyperledger 7

Food Supply Chain Management Using Hyperledger

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Mali, Ajit S. Jagtap, Amol M. Katekar, Shubham Shinde, Shubham Ashtekar, Kunal Rajarambapu Institute of Technology Department of Computer Science and Engineering Islampur India Rajarambapu Institute of Technology Computer Science & Engineering Maharashtra Islampur India

ISBN: (纸本)9798350397284

This research study investigates the potential of blockchain technology, specifically the Hyperledger platform, in addressing challenges in food supply chain management, The aim of this study is to propose a comprehensive framework for the implementation of Hyperledger in food supply chain management. The paper begins by highlighting the issues faced in food supply chain management, including food safety, traceability, and transparency problems. It then provides an overview of the Hyperledger platform and its features, and explores how it can be leveraged to address these challenges, The study focuses on the implementation of Hyperledger in food supply chain management, examining its potential benefits, challenges, and limitations. It also discusses the key stakeholders involved in the food supply chain, and how the adoption of Hyperledger can benefit each of them. Furthermore, the paper presents a case study of a pilot project that used Hyperledger in food supply chain management, The research findings demonstrate that Hyperledger can be an effective solution for addressing challenges in food supply chain management, particularly in ensuring food safety, traceability, and transparency. However, the study also highlights some challenges and limitations of implementing Hyperledger in this context, such as the need for standardized data formats and the high cost of implementation. Overall, this paper provides valuable insights for organizations and policymakers considering the adoption of blockchain technology in food supply chain management. © 2023 IEEE.

关键词： Supply chain management

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Performance Analysis of Multipath TCP Congestion Control Algorithms 14

Performance Analysis of Multipath TCP Congestion Control Alg...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Biswal, Subhra Priyadarshini Chatterjee, Akash Alanam, Khair Patel, Sanjeev Department of Computer Science and Engineering National Institute of Technology Rourkela Odisha 769008 India

ISBN: (纸本)9798331531935

In modern high-speed networks, providing reliable and high-quality Internet services is essential. However, maintaining consistent network performance in dynamic environments with varying scenarios presents significant challenges. To address these, Multipath TCP (Transmission Control Protocol) offers a viable solution by aggregating multiple network paths, resulting in improved bandwidth utilization, higher throughput, and reduced delay. This approach overcomes the limitations of traditional single-path TCP by enhancing responsiveness, friendliness, and reducing window oscillation. Moreover, MPTCP incorporates congestion control mechanisms that couple several subflows, ensuring efficient traffic distribution across multiple paths, thereby improving overall network performance and fairness. In this paper, we analyze the performance of LIA (Linked Increases Algorithm), OLIA (Opportunistic Linked Increases Algorithm), and BALIA (Balanced Linked Adaptation) congestion control algorithms using the ns-3 network simulator. The results show that BALIA achieves 21% higher throughput compared to LIA. © 2025 IEEE.

关键词： Transmission control protocol

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Blockchain Approach for Analyzing the Issues of IoT Systems 5th

Blockchain Approach for Analyzing the Issues of IoT Systems

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Rawat, Abhishek Verma, Rajat Suryavanshi, Raghuraj Singh Department of Computer Science and Engineering Pranveer Singh Institute of Technology UttarPradesh Kanpur India

ISBN: (纸本)9789819771776

The Internet of Things combined with blockchain technology has emerged as an innovative platform to better safeguard data, save costs, and pale, achieve strength. Combining blockchain and IoT means that the distributed ledger with sophisticated cryptographic mechanisms, consensus mechanisms, and some other processes may transmit data and information in a secure and open approach among devices across long distances. This paper begins with an introduction to the IoT and blockchain technology platforms before conducting a study of how their merger can help improve the platforms. The paper will additionally emphasize critical criteria before integrating the blockchain in IoT systems. Finally, the paper offers practical examples from blockchain-supported IoT project. Overall, combining blockchain technology into IoT might transform, how perceive and communicate technical advancements, paving the path for a more secure, connected, but decentralized future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed ledger

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An autoencoder based unsupervised clustering approach to analyze the effect of E-learning on the mental health of Indian students during the Covid-19 pandemic

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Multimedia Tools and Applications 2024年 1-21页

作者： Banerjee, Pritha Jana, Chandan Saha, Jayita Chowdhury, Chandreyee Computer Application and Science Institute of Engineering and Management West Bengal Kolkata700091 India Computer Science and Engineering Jadavpur University West Bengal Kolkata700032 India Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka Bengaluru560064 India

Due to the Covid-19 pandemic, the education system in India has changed to remote that is, online study mode. Though there are works on the effect of teaching learning on Indian students, the effect of online mode and associated mental state, particularly when the entire country is going through a crisis could not be found in the literature. Our goal is to analyze data and find some pattern through which we can understand the effectiveness of the online study and also try to figure out the stress level. The dataset we collected from 500 undergraduate college students during April-May, 2021 is in questionnaire format. Our contribution in this paper are - (i) publishing a dataset of student feedbacks, and (ii) designing a data processing pipeline involving autoencoders followed by clustering approach. The dataset is in text format so for our analysis we have converted the dataset into a numerical format using the concept of a binary bag of words. Dimensionality reduction is applied through autoencoder for an effective latent space representation. Finally, for finding patterns out of this dimensionally reduced feature space, we have applied unsupervised learning algorithms - kMeans and DBSCAN. A thorough analysis of the clustering process reveals that the absence of social communication in purely online education provokes isolation irrespective of the urban or rural background of the students. However, it could supplement offline classes as a substantial number of students welcomed the concept of online learning as reported in the data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Students

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