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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Sindhu, T.N. Sujatha, S.R. Sri Siddhartha Institute of Technology Department of Computer Science and Engineering Tumakuru India

ISBN: (纸本)9798331529246

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects children from childhood to adulthood. The symptoms of ADHD include attention deficit, impulsive behaviour, hyperactivity, and early diagnosis and treatment are imperative, especially for children as it continues into adulthood. ADHD detection is generally carried out using the existing methods such as Machine Learning (ML) and Deep Learning (DL) methods. The ML methods such as Linear Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Linear Discriminative Analysis (LDA), Linear Regression (LR), Random Forest (RF), and Least Square SVM (LS-SVM) are used for the detection and analysis of ADHD. The existing DL methods such as Deep Convolutional Neural Network (DCNN), Graph Recurrent Unit (GRU), Convolutional Long Short-Term Memory (ConvLSTM), and Bidirectional LSTM are also employed for the detection of ADHD. The ML and DL algorithms enhance ADHD detection and diagnosis by automatically determining large patterns in data, thereby offering higher accuracy. The performance of the existing techniques is evaluated in terms of evaluation metrics: accuracy, precision, sensitivity, and F1-score to assess their effectiveness. © 2025 IEEE.

关键词： Adversarial machine learning

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Machine Learning based Effort Estimation Models for Software Development Projects related Datasets with diverse features 2

Machine Learning based Effort Estimation Models for Software...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Thakur, Vaishali Dutta, Kamlesh Computer Science & Engineering Department National Institute of Technology HP Hamirpur India

ISBN: (纸本)9798331530389

This research evaluates the performance of machine learning models for the estimation of software effort using diverse datasets with distinct feature sets and varying training-test splits. To address the risks of under or overestimation in project management, the study employs robust data preprocessing, feature engineering, and selection techniques. Through extensive experimentation, the study analyzes model accuracy using multiple metrics such as Mean Absolute Error, R-squared, precision, recall, Fl-score, and Receiver Operating Characteristic Area Under the Curve. Preprocessing techniques, including feature scaling and outlier removal, were applied to reduce overfitting and improve generalization. Performance was assessed using various indicators, with results showing that the stacked ensemble model consistently outperformed other models like Linear Regression, Support Vector Regression, and Gradient Boosting in generating accurate predictions across datasets. The analysis revealed that no single data split yielded optimal performance for all datasets;instead, different datasets performed best under customized splits. Larger datasets performed well with splits like 70-30, while smaller ones exhibited overfitting even after preprocessing. Models like Random Forest, XGBoost, and Gradient Boosting demonstrated robust performance, especially on larger datasets, while smaller datasets highlighted challenges with models like K-Nearest Neighbors and Naive Bayes due to overfitting and poor generalization. These findings underscore the importance of adapting data-splitting strategies to the characteristics of individual datasets, offering a pathway to improve estimation accuracy in diverse software development scenarios. The research highlights that larger datasets enable better generalization, while smaller datasets require careful model and preprocessing choices to mitigate overfitting and achieve reliable results. © 2025 IEEE.

关键词： Adversarial machine learning

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Retinal Disease Detection for Low Resource Devices Through Pruned and Quantized CNNs

Retinal Disease Detection for Low Resource Devices Through P...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Gavel, Harish Azad, Chandrashekhar National Institute of Technology Jamshedpur Department of Computer Science and Engineering Jamshedpur India

ISBN: (纸本)9798331529833

Retinal disease diagnosis is one of the crucial step for preventing vision loss, especially in conditions such as Diabetic Retinopathy(DR), Glaucoma, and Age-related Macular Degeneration(AMD). In this paper, we propose an efficient approach for retinal disease classification using lightweight convolutional neural networks (CNNs) deployed on a low-resource device, the Raspberry Pi 4. To address the limitations of resource-constrained hardware, we employ model optimization techniques, including pruning and quantization, to reduce computational requirements while maintaining classification accuracy. The proposed system is evaluated on the Messidor datasets. Our optimized MobileNet and EfficientNetLite models decreased the accuracy scores by 1.5% to 2%, while reducing inference time by over 50% and memory usage by 45%. These results show the feasibility of deploying real-time retinal disease models on edge devices for telemedicine and mobile health in resource-limited settings. © 2025 IEEE.

关键词： Constrained optimization

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Development of a Naive Bayes-based Framework for Optimizing Crop Recommendation System and Enhancing Agricultural Yield Prediction 4

Development of a Naive Bayes-based Framework for Optimizing ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swetha, P. Senthilkumar, J. Kalaignar Karunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

This research study proposes a comprehensive Crop Recommendation System that uses advanced machine learning to boost agricultural productivity. By analyzing key environmental and soil factors, including soil composition, pH, temperature, humidity, and rainfall, the system offers customized, data-driven crop suggestions to optimize yield and promote sustainable farming. The system evaluates several algorithms, including Naive Bayes, Decision Tree, Logistic Regression, and Random Forest, and identifies Naive Bayes as the top performer for accurately predicting crop suitability. This multi-algorithm approach represents a significant advancement in precision agriculture, empowering farmers and policymakers with actionable insights to drive improvements in the agricultural sector. Leveraging historical data, the Crop Recommendation System supports farmers, consultants, and policymakers in making more informed and strategic crop selection decisions. The system's precision and adaptability make it a valuable resource for stakeholders focused on improving resource efficiency and fostering sustainable agriculture, ultimately enhancing food security and minimizing environmental impact. © 2025 IEEE.

关键词： Agriculture Crop Recommendation System Decision Tree Logistic Regression Machine Learning Naive Bayes Random Forest

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Tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features 3rd

Tracking of Ship and Plane in Satellite Videos Using a Convo...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Sharma, Devendra Srivastava, Rajeev Computer Science and Engineering Department Indian Institute of Technology BHU Varanasi India

ISBN: (纸本)9789819743582

Object tracking is one of the latest research topic in various applications of computer vison. Satellite videos are provided by the commercial satellite makes it possible to extend into earth observation. Moving objects are occupy the small region of pixel and they are confused with neighboring regions also with objects. Due to the resolution constraints, the nearest similar objects can rarely be distinguished by appearance features. Conventional tracking techniques in satellite videos with hand-crafted features do not give better results. Deep learning-based techniques have proven to be superior in visual tracking benchmarks, but this is not fully explored for the object tracking in the satellite videos. Regression network is used to integrate the regression model with convolutional layers. Both motion feature and appearance feature are taken from pre-trained deep neural networks VGG16, VGG19, and ResNet50. When tracker faces obscure appearance information, motion features of object are used for discrimination and complementary information to improve the performances of model. Experimental analysis on ship and plane represents the ResNet50 feature extractor gives better tracking result. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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Why Smart Contracts Reported as Vulnerable Were Not Exploited?

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2579-2596页

作者： Hu, Tianyuan Li, Jingyue Li, Bixin Storhaug, André Southeast University School of Computer Science and Engineering Nanjing211189 China Norwegian University of Science and Technology Department of Computer Science Trondheim7034 Norway

Smart contract security is crucial for blockchain applications. While studies suggest that only a small fraction of reported vulnerabilities are exploited, no follow-up research has investigated the reasons behind this. Our goal is to understand the factors contributing to the low exploitation rate to improve vulnerability detection and defense mechanisms. We collected 136,969 real-world smart contracts and analyzed them using seven vulnerability detectors. We applied Strauss’ grounded theory to gain insights into exploitability and analyzed transaction logs to trace the historic exploitations. Among the 4,364 smart contracts flagged as vulnerable, a significant 75.25% were found to be unexploitable, meaning they were either false positives or posed no security risk. We identified ten reasons for reporting unexploitable vulnerabilities. Furthermore, we found that only 66 out of 1,080 (6%) exploitable contracts had been exploited. We compared the characteristics of exploited versus non-exploited vulnerabilities and identified five factors that may reduce the likelihood of exploitation. Our findings highlight the importance of not treating smart contracts as conventional object-oriented (OO) applications. Researchers must account for the unique features of Solidity, smart contract design principles, and execution environments. Based on these insights, we propose six recommendations to improve smart contract vulnerability detection, prioritization, and mitigation. © 2004-2012 IEEE.

关键词： Smart contract

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Optimizing Plant Disease Detection Using Convolutional Neural Networks and Docker Environment

Optimizing Plant Disease Detection Using Convolutional Neura...

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

作者： Rachana, C.V. Jai Arul Jose, G. Bms Institute of Technology and Management Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538538

In numerous nations, agriculture serves as the primary and most significant source of income generated domestically. Plant illnesses caused by bacteria, viruses, and fungi are among the many, and they can be quite expensive for agricultural companies worldwide. Crop security both in terms of quantity and quality is crucial for monitoring plant disease. Therefore, it is essential to recognize plant diseases. There are certain plant parts where the disease state is clearly visible. However, usually, the infection is seen in specific plant leaves. Many researchers use computing techniques to automatically detect plant illness using photos of their leaves. Farmers might also profit fromthese strategies by obtaining quick and suitable measures to do in both agricultural yield and quality. Furthermore, some molecular methods have been developed to reduce and even eliminate the harm posed by pathogens. Thus, this evaluationaids the researcher to identify automatically the plant illness. Also enhancing the learning and provide specific methods for disease identification in order to prevent illness. © 2025 IEEE.

关键词： Crops

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Breast Cancer Segmentation Using UNet and Global Convolutional Networks 27th

Breast Cancer Segmentation Using UNet and Global Convolutio...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Thyagachandran, Anand Ahmed, Yeruru Asrar Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India

ISBN: (纸本)9783031782008

Breast ultrasound (BUS) imaging techniques have become efficient tools for cancer diagnosis. Convolutional neural network (CNN) based encoder-decoder architectures have been widely used for the automated segmentation of tumours in BUS images, assisting in breast cancer diagnoses. However, these models have limitations in capturing long-range dependencies. To overcome this limitation, various deep learning techniques, such as atrous convolution, attention mechanisms, and transformer encoder-based models, have been introduced to capture long-range dependencies in feature maps, improving segmentation accuracy by considering larger receptive fields and global context. As modelling techniques evolve, there is a shift towards more complex and intricate designs. This study proposes a simple yet effective model that combines UNet and Global Convolutional Network (GCN) architectures for breast lesion segmentation. By leveraging the GCN block, our model captures broader receptive fields with a simpler design strategy. We have demonstrated the efficacy of our approach through various experiments, including kernel size analysis, model component evaluation, and data preprocessing assessment. The proposed model has been evaluated using four-fold cross-validation with BUSI and Dataset-B datasets. Additionally, models trained on both datasets have been validated with a blind test dataset, where our model demonstrates better performance compared to state-of-the-art methods, achieving a 4.9% and 6.7% improvement in Intersection over Union (IoU) score, respectively. The robustness analysis and external validation experiments underscore the superior generalization performance of our model in breast lesion segmentation tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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SmartAudit: Smart Contract Vulnerability Detection Using Transfer Learning 10th

SmartAudit: Smart Contract Vulnerability Detection Using Tra...

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10th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2024

作者： Jain, Ankur Tripathy, Somanath Department of Computer Science and Engineering Indian Institute of Technology Patna Patna India

ISBN: (纸本)9789819637737

Ethereum being a dominant player in decentralized finance, invites a vast number of smart contracts annually. Though smart contracts play a crucial role in developing decentralized applications that are safe, secure, and efficient, Ethereum lost a huge amount due to various attacks caused by different vulnerabilities within the smart contracts. Therefore, it is crucial to audit the reliability of smart contracts before their deployment. This paper proposes SmartAudit which identifies whether the smart contract is vulnerable. SmartAudit employs BERT (Bidirectional Encoder Representations from Transformers) to identify vulnerabilities in unseen smart contracts. It utilizes optimized opcode sequences derived from a pruned control flow graph as input. The results show that SmartAudit significantly improves accuracy, precision, recall and F1-score compared to the state-of-the-art models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart contract

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Exploring Transformer-Based Approaches for Hyperspectral Image Classification: A Comparative Analysis 3rd

Exploring Transformer-Based Approaches for Hyperspectral Ima...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Arya, Rajat Kumar Srivastava, Rajeev Department of Computer Science and Engineering Indian Institute of Technology-BHU Varanasi India

ISBN: (纸本)9789819743582

Hyperspectral imaging (HSI) is a technique that captures and analyzes multiple spectral bands for each pixel in an image. Convolutional neural networks (CNNs) effectively extract local features for HSI classification but struggle with capturing sequential spectral signatures. To address this, we conducted a comparative study to assess the performance of transformers, which can handle sequential information, in HSI classification. We reviewed a few models in the literature, analyzing their architecture and performance. Using the Indian Pines dataset, we evaluated models like ViT, SSRN-ViT, SpectralFormer, and Double-ViT. Our results show that Double-ViT models outperform transformers for HSI classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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