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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Balakumar, C. Sujatha, T. Wilfred Blessing, N.R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350365337

The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the Internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field. © 2024 IEEE.

关键词： Internet of things

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Secure Smart Contracts: Enhancing Vulnerability Detection With Graph and Tokenization Analysis

Secure Smart Contracts: Enhancing Vulnerability Detection Wi...

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

作者： Arun Kumar, B.R. Lakshmi, B.N. Prasad, Mahadeshwara Sangolli, Dharshan B. Gowda, Akshith Likhith, Y.V. Bms Institute of Technology & Management Department of Information Science Bengaluru India Bms Institute of Technology & Management Department of Computer Science Bengaluru India

ISBN: (纸本)9798331538538

Blockchain smart contracts can be easily integrated into any DApp because they function automatically and cannot be altered. But the existence of important weaknesses, like reentrancy attacks, integer overflow, underflows, and access control problems, puts these contracts at a considerable risk in terms of reliability and security. To effectively detect the vulnerabilities, smart contract's code needs to be preprocessed into a strong representation that will be properly analyzed by the deep learning models. In this study, we offer a hybrid deep learning model combining the use of Graph Neural Networks (GNNs) and CodeBERT that can effectively be ensured through a dedicated preprocessing pipeline. Our model involves: (1) First, smart contract code is graphically converted by employing Recursive Depth First Traversal (RDFT) and Iterative Depth First Traversal (IDFT) algorithms to allow code structure along with relationships to be preserved;(2) The WordPiece tokenizer is used in smart contracts to comprehend semantics better;and (3) The Slither static analysis tool is incorporated to facilitate the marking of weaknesses. Finally, we examine different graph construction algorithms focusing on accuracy and computational ability while concluding that IDFT is most helpful in completing this task. The proposed preprocessing pipeline enables precise identifi- cation of vulnerable functions, variables, and line numbers while preparing the data for hybrid model training. This work lays a foundation for advancing automated vulnerability detection, contributing to a more secure blockchain ecosystem. © 2025 IEEE.

关键词： Graph neural networks

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Smart IoT System for Cardiopulmonary Resuscitation Training: Embedded Sensor Integration and Android Application 2

Smart IoT System for Cardiopulmonary Resuscitation Training:...

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2nd International Symposium on information technology and Digital Innovation, ISITDI 2024

作者： Aisuwarya, Ratna Erlina, Tati Mitzi, Richo Attir Universitas Andalas Faculty of Information Technology Computer Engineering Department Padang Indonesia

ISBN: (纸本)9798350389241

Lack of awareness and experience in CPR often discourages individuals from initiating rescue actions of Out-of-hospital cardiac arrest (OHCA). This research presents a new approach to CPR training using embedded system-based devices. This system, which resembles the human body's torso and head sections, monitors and calculates key parameters critical to successful CPR, including compression, airway management, and breathing techniques. Sensor nodes are placed to measure pressure during compression and monitor airway and respiratory processes. The collected data is transmitted and processed using Internet of Things (IoT) technology with a multi-user Android application, facilitating collaborative training sessions. Experimental testing validated the system's functional capabilities with successful network connection, real-time data transmission, and effective execution of CPR procedures. Hardware implementations use microcontrollers and sensors, with Arduino and NodeMcu ESP8266 enabling data processing and transmission. Software components developed within the Arduino IDE and Android Studio ensure efficient data handling and visualization. The results highlight the system's effectiveness in improving the accessibility and affordability of CPR training, potentially improving intervention by witnesses, and improving OHCA survival rates. © 2024 IEEE.

关键词： Resuscitation

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Supervised Learning-Driven Emotion Recognition through Facial Landmark Trajectories Enhanced by SMOTE Methodology

Supervised Learning-Driven Emotion Recognition through Facia...

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2025 AI-Driven Smart Healthcare for Society 5.0, AdSoc5.0 2025

作者： Kar, Ishita Chatterjee, Mehuli Manna, Sukanya Das, Akash Dutta, Stobak Department of Computer Science and Engineering St Thomas’ College of Engineering and Technology Kolkata India

ISBN: (纸本)9798331536336

The study highlights the importance of facial landmark trajectories in understanding mental health by investigating the application of supervised learning systems to identify emotions. Diagnoses and treatment plans are impacted by the difficulty in recognizing and controlling emotions, which are intrinsically tied to mental health. Although physiological signals like ECG and EEG are accurate, they have drawbacks including expense and user discomfort. Because they are noninvasive, facial landmarks present a viable substitute for practical uses. Using information from the ASCERTAIN dataset, this study divided emotions into four valence-arousal plane quadrants: High Arousal High Valence (HAHV), High Arousal Low Valence (HALV), Low Arousal High Valence (LAHV), and Low Arousal Low Valence (LALV). Algorithms including Random Forest, K-Nearest Neighbors (KNN), Decision Tree, and Support Vector Machine (SVM) were used to solve a 4-class classification problem. A common problem, class imbalance, was lessened by employing the Synthetic Minority Oversampling Technique (SMOTE). The study showed that SMOTE greatly enhanced predictions for minority classes, particularly for LALV, whereas classifiers strongly preferred the majority class in the absence of balance. Despite possible overfitting, Decision Tree has the greatest true positive rate among classifiers for LALV after SMOTE application. The findings demonstrate developments in emotional computing and provide information for applications in mental health support and human-computer interaction. © 2025 IEEE.

关键词： k-nearest neighbors

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Early Detection of Alzheimer's Disease based on the State-Of-The-Art Deep Learning Approach 7

Early Detection of Alzheimer's Disease based on the State-Of...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Bagade, Virendra Godse, S.P. Vishwakarma Institute of Information Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798331527822

In this paper, we introduce a deep convolutional neural network architecture, code-named Inception, which sets a new benchmark for classification and detection tasks. Early detection of Alzheimer's disease (AD) is critical, as the brain is the primary organ affected, and early intervention can help slow disease progression. Traditionally, AD diagnosis relies heavily on MRI scans, a process that is both time-consuming and resource-intensive. However, recent advancements in artificial intelligence (AI) present promising solutions for more efficient detection. This study examines the application of various Convolutional Neural Network (CNN) models for detecting AD in MRI images. A comprehensive comparison was performed on four distinct CNN architectures - Inception-V3, ResNet-152, VGG16, and VGG19 - using MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The images were categorized into four classes: Non-Demented, Very Mild Demented, Mild Demented, and Moderate *** convolutional neural networks (CNNs) excel at identifying advanced dementia stages (Stages 5-7), accurately classifying the earliest, most elusive stages (Stages 2-4) remains a significant challenge. This paper tackles this crucial gap by systematically comparing various CNN models to achieve promising classification accuracy for these crucial early stages of dementia. Through rigorous analysis, we aim to advance the field of dementia detection by enabling earlier diagnosis and intervention, ultimately improving patient outcomes. Therefore, the application of machine learning technology can assist healthcare professionals in enhancing their efforts to combat Alzheimer's, a serious medical condition. © 2024 IEEE.

关键词： Convolutional neural networks

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Lung Cancer Detection Using Machine Learning and Deep Learning Models 21

Lung Cancer Detection Using Machine Learning and Deep Learni...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Alheeti, Khattab M. Ali Al-Shouka, Tabreer T. Majeed, Saleem Hamad Ahmed, Amjed Abbas College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq College of Computer and Information Technology University of Anbar Computer Sciences Department Anbar Iraq Department of Computer Techniques Engineering Baghdad Iraq

ISBN: (纸本)9798350374131

technology and artificial intelligence play a significant role in improving healthcare and enable tasks to be automated. In addition, the diseases can be better understood and diagnosed faster, saving time and reducing costs. This study examines the impact of transfer learning models on the effectiveness of deep learning models in classifying lung cancer through the analysis of CT scan images. Additionally, it investigates the relative performance of various machine learning and deep learning models, encompassing Support Vector Machine (SVM) and convolutional neural net-works (CNN) such as InceptionV3, VGG16, Xception, ResNet50, and MobileNetV2, in the early detection of lung cancer based on CT scan images. The SVM model achieved an overall accuracy of 89% after preprocessing, the proposed approach was applied to five pre-trained models (ResNet50, In-ceptionV3, VGG16, Xception, MobileNetV2) using the dataset: Chest CT-Scan;Among the pre-trained CNN models, the Mo-bileNetV2 model achieved the highest accuracy of 98% and the lowest test loss, indicating it performed the best. The Xception model achieved the second-highest accuracy of 97%. The image pre-processing phase plays a significant role in im-proving system performance in terms of improving image contrast and increasing processing speed. © 2024 IEEE.

关键词： Support vector machines

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A Comparative Analysis of YOLOv8 and YOLO-NAS for Identifying Fetal Intracranial Structures in Pregnancy 15th

A Comparative Analysis of YOLOv8 and YOLO-NAS for Identif...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Singh Gill, Navtegh Wadhwa, Stuti Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab147004 India

ISBN: (纸本)9783031810794

Standard/non-standard (S-NS) plane classification is a key step for Nuchal Translucency measurement and early Down Syndrome assessment in fetus during pregnancy weeks 11–14. To classify S-NS planes, fetal intracranial structures have to be identified with high precision and low latency. This paper presents a comparative study between YOLOv8 and YOLO-NAS for the identification of fetal intracranial structures during pregnancy weeks 11–14. Using a diverse dataset comprising 1528 2D sagittal-view ultrasound images from 1519 females, we evaluate the performance of these state-of-the-art object detection models. The dataset includes meticulously labelled instances of crucial fetal intracranial structures such as thalami, nasal bone, palate, 4th ventricle, cisterna magna, NT (nuchal translucency), nasal tip, nasal skin and midbrain. Through comprehensive experiments and analysis, this research sheds light on the suitability of these YOLO variants for accurate and efficient detection of these structures in early-stage pregnancies, providing valuable insights for improving prenatal healthcare and diagnosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

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Statistical assessment of Indian classical music’s role in diabetes, thyroid, hypertension management

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International Journal of information technology (Singapore) 2025年第1期17卷 423-429页

作者： Pingle, Yogesh Prabhakar Ragha, Lakshmappa K. Research Scholar at Department of Computer Engineering Terna Engineering College Assistant Professor at Department of Computer Science and Engineering (Data Science) Vidyavardhinis College of Engineering and Technology University of Mumbai Maharashtra Mumbai India Department of Computer Engineering Terna Engineering College University of Mumbai Maharashtra Mumbai India

This multi-center randomized controlled trial explores the therapeutic benefits of Indian classical music, specifically “Raga Therapy,” for managing diabetes, thyroid disorders, and hypertension—prevalent global health issues. The study examines how specific Ragas can enhance traditional treatments: Bhimpalas and Chhanayat for hypertension, Malkauns and Bhupal Todi for diabetes, and Shyam Kalyan and Yaman for thyroid disorders. Results show significant health improvements in patients integrating Raga Therapy into their care, highlighting its holistic benefits. The study underscores the profound connection between music and healing, positioning Indian classical music as a valuable complementary therapy. By addressing a critical gap in the literature, it advocates for further research to understand the mechanisms involved and refine Raga selection for optimal therapeutic outcomes. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Paired t-test Patient satisfaction survey Raga Therapy Wilcoxon signed-rank test

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Analysis of Semiconductor Chip Performance using Medallion Architecture 7

Analysis of Semiconductor Chip Performance using Medallion A...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Shabu, S.L. Jany Nirmal Kumar, P. Rohith Kumar, S. Maheswari, M. Refonaa, J. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

This research presents a meticulous investigation into semiconductor chip performance using a data engineering methodology. Employing the Medallion Architecture, the study comprehensively analyses various CPU performance metrics, including Integer Math, Floating Point Math, Random String Sorting, Data Encryption, Data Compression, Physics, Extended Instructions, and Single Thread operations. The project, consummated via Microsoft Fabric and Python integration, aims to decipher intricate CPU behaviours across diverse computational workloads. This in-depth exploration not only elucidates these metrics but also aims to derive critical insights essential for advancing chip design and optimizing computational efficiency. The findings of this rigorous analysis contribute significantly to the understanding and utilization of semiconductor chip performance within contemporary computing paradigms. © 2024 IEEE.

关键词： Program processors

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Soil Image Classification Using Transfer Learning Approach: MobileNetV2 with CNN

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SN computer science 2024年第1期5卷 199页

作者： Banoth, Ravi Kumar Murthy, B. V. Ramana Department of Computer Science and Engineering University College of Engineering Osmania University Telangana Hyderabad India Department of Computer Science and Engineering Stanley College of Engineering and Technology for Women Abids Telangana Hyderabad India

This paper presents a novel study on soil image classification, leveraging the synergistic potential of transfer learning and convolutional neural networks (CNNs). The proposed approach combines the strengths of the MobileNetV2 architecture with a customized CNN model for accurate and efficient soil type recognition. The pre-trained MobileNetV2 is used to capture generic features before fine-tuning it with a dedicated soil image dataset comprising four distinct classes: red, clay, black, and yellow soils. To enhance the model’s capacity for discerning intricate soil textures, a specially designed CNN architecture is incorporated. The model’s performance is rigorously evaluated on a dataset of 108 images, each sized at 256 × 256 pixels, achieving an exceptional accuracy rate of 100% on the test dataset. The promising results demonstrate the efficacy of the proposed methodology in soil image classification tasks, offering potential applications in precision agriculture, environmental monitoring, and land management. While these findings showcase remarkable accuracy, further investigations are recommended to assess the model’s generalization across diverse environmental conditions and an expanded range of soil image datasets. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Black soil Clay soil Convolutional neural networks (CNNs) MobileNetV2 Red soil Soil image classification Yellow soil

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