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Voice AI-Intelligence Based Voice Assistant 3

Voice AI-Intelligence Based Voice Assistant

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Garai, Pooja Nikam, Aditya Kerkar, Suyash Deshmukh, Samruddhi Mane, Atharva Bhise, Suvarna Vasantdada Patil College Of Engineering & Visual Arts Dept. of Artificial Intelligence & Data Science Mumbai India

ISBN: (纸本)9798331515911

Voice assistant applications have become integral parts of modern technology ecosystems, offering users convenient and efficient ways to interact with their devices. This paper introduces Voice Ai, a versatile voice assistant application developed using Android Studio and Google's Machine learning Kit Machine learning(MLKit) for Optical Character Recognition (OCR) functionalities. The app includes features like setting alarms, making calls, opening applications, and conducting question-answer sessions powered by G4F. A comparative analysis between G4F and Google's backend is performed, assessing their performance and user satisfaction during question-answer interactions. Voice Ai simplifies daily tasks and boosts productivity, providing insights for developers, researchers, and end-users interested in AI-based voice assistant applications. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

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Soil Analysis for Crop Recommendation using Machine learning Algorithms 3

Soil Analysis for Crop Recommendation using Machine Learning...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sathya, J. Asha, V. Kalaivani, A. Hanchinal, Sanjeev Sangappa Fathima, Sarana Sachin, K.S. Department of MCA New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798331527549

In the area of farming, parts of the soil are of special note as it has to do with growing crops and provision of food for consumption. Soil analysis is also important in crop rating since it reflects some important Indices of the soils such as nutrients, pH, and texture which all affect plants’ growing condition. Farmers are able to utilize this strategy in order to plant the right sorts of crops on their area of land, hence gaining scope in the yield and resources consumed. This paper showcases the application of advanced machine learning algorithms to optimize crop recommendations based on essential climatic, geographic, and eco-spatial parameters. In such techniques, depending on humidity, temperature and rainfall, certain patterns and relations are established and the best crops are recommended. For the purpose of generating predictive models that recommend the growth of specific crops in specific regions with the aim of increasing agricultural productivity, the system deploys the algorithms of Gradient Boosting, XGBoost, and LightGBM. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

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An Evaluation of Image Preprocessing in Skin Lesions Detection 3rd

An Evaluation of Image Preprocessing in Skin Lesions Detecti...

引用

3rd International conference on optimization, learning Algorithms and Applications (OL2A)

作者： Silva, Giuliana M. Lazzaretti, Andre E. Monteiro, Fernando C. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Lab Sustentabilidade & Tecnol Reg Mt SusTEC Campus Santa Apolonia P-5300253 Braganca Portugal Fed Univ Technol BR-80230901 Curitiba Parana Brazil

ISBN: (纸本)9783031530357;9783031530364

This study aims to evaluate the impact of image preprocessing techniques on the performance of Convolutional Neural Networks (CNNs) in the task of skin lesion classification. The study is made on the ISIC 2017 dataset, a widely used resource in skin cancer diagnosis research. Thirteen popular CNN models were trained using transfer learning. An ensemble strategy was also employed to generate a final diagnosis based on the classifications of different models. The results indicate that image preprocessing can significantly enhance the performance of CNN models in skin lesion classification tasks. Our best model obtained a balanced accuracy of 0.7879.

关键词： Skin Lesion Classification Convolutional Neural Networks Deep learning Image Preprocessing

来源：评论

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Deep learning based Brain Tumor Detection using YOLO11n and Segmentation using SAM2 Model 3

Deep Learning based Brain Tumor Detection using YOLO11n and ...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pasunoori, Krupa Chary Rajendra Prasad, Ch. Raj Kumar, K. Dept. of ECE SR University Warangal506371 India

ISBN: (纸本)9798331527549

The central nervous system (CNS) contains the brain, spinal cord, and it governs all essential functions. These functions encompass cognition, verbal communication, and locomotion. When a tumor develops in the CNS, it can disrupt an individual's cognitive functions, speech, and motor skills. A brain tumor is a condition resulting from the proliferation of atypical cells inside the brain. Any disorder in the brain might be life-threatening. This paper presents deep learning based YOLO11n algorithm for tumour prediction. Then the Segment Any Model2 is employed for tumor segmentation. The proposed model significantly improve that segmentation Dice Score (96.3%) and IOU Score(93.03%) compared with previous models. © 2025 IEEE.

关键词： Deep learning

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optimization and Automatic Generation Algorithm of Mechanical Design Parameters Based on Deep learning 3

Optimization and Automatic Generation Algorithm of Mechanica...

引用

3rd International conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Gao, Siming Jiang, Rongheng Harbin Huade University Heilongjiang Harbin150025 China

ISBN: (纸本)9798350376173

The purpose of this paper is to study the optimization and automatic generation algorithm of mechanical design parameters based on deep learning, and to explore its application in engineering design field. Firstly, the paper introduces the potential application of deep learning technology in mechanical design, and reviews the related research work. Then, a parameter optimization method based on Generative Adversarial Network (GAN) is proposed. By learning the complex design space characteristics, efficient design parameter optimization is realized. Then, the automatic generation algorithm based on the Variable Automatic Encoder (VAE) is discussed, which can generate diverse and innovative design schemes. Through the evaluation and analysis of the experimental results, the effectiveness and performance advantages of the proposed method are verified and compared with the traditional methods. Finally, the future development direction of GAN-based mechanical design optimization and VAE-based generation algorithm is prospected, and its application prospect in engineering practice is discussed. This study provides new ideas and methods for the application of deep learning technology in the field of mechanical design, and is of great significance for promoting the intelligence and automation of engineering design. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

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An intelligent approach for Speed Bump Detection 3

An intelligent approach for Speed Bump Detection

引用

3rd International conference on Computing and Machine Intelligence (ICMI)

作者： Hussein, Abrar Ahmed Omar, Mariam Mohamed Altamimi, Jawaher Suliman Ead, Waleed M. Beni Suef Univ Comp & Artificial Intelligence Bani Suwayf Egypt Najran Univ Appl Coll Dept Comp Najran Saudi Arabia

ISBN: (纸本)9798350372977;9798350372984

In this paper, we propose an intelligent approach that uses YOLO algorithms to detect marked and unmarked bumps. The proposed approach for speed bumps based on real data collected from the Upper Egypt region in Egypt. Several data augmentations were performed on it. The proposed approach reached a mAP of 81% in the model while also calculating the distance from the car to the bump. We have used the Jetson Nano and the Xbox Kinect camera hardware to achieve this objective. Based on road preview using a camera, this work can be used to inform and alert the driver via a mobile application to make the ride more safe, prevent accidents, and reduce vehicle damage.

关键词： Speed bumps intelligence systems safe roads detection Deep learning Kinect camera

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Development of Image Classification System to Detect Skin Lesion using Deep learning Techniques 3

Development of Image Classification System to Detect Skin Le...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Hariprasath, V. Rangkeshwaran, R. Dhanush, K. Prema, D. Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (纸本)9798331527549

Skin lesions are abnormal growths or appearances of the skin and may be an indication of several diseases, both benign and malignant. To achieve the correct diagnosis and proper treatment of these lesions, need to be discovered early and diagnosed accurately. Conventional methods of diagnosis, including biopsies and examinations of the dermatologist's eye, are often invasive and expensive, thus limiting access to them in underdeveloped regions. This work proposes a deep learning-based image classification system for the identification of skin lesions. The categorization of dermoscopic images was done using CNN architectures such as ResNet50, DenseNet201, VGG 19 and Xception. ResNet50 performed better than the others in identifying and categorizing skin lesions, achieving the highest accuracy of 87.72%. This non-invasive method and accessibility could greatly improve the diagnostic capabilities while reducing reliance on conventional techniques. © 2025 IEEE.

关键词： Deep learning

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Enhanced YOLOv5 Algorithm for Defect Detection in Solar Cells 3

Enhanced YOLOv5 Algorithm for Defect Detection in Solar Cell...

引用

3rd International conference on Robotics, Artificial Intelligence and intelligent Control (RAIIC)

作者： Ma, Mingyuan Zhang, Chaozhu Liao, Xinyu Han, Yongli Qilu Univ Technol Jinan Peoples R China

ISBN: (纸本)9798350352634;9798350352627

Photovoltaic cells play a critical role in solar power generation, with defects in these cells significantly impacting energy conversion efficiency. To address challenges in detecting defects of varying scales in solar cells, an enhanced YOLOv5 algorithm is proposed. This algorithm integrates the Convolutional Block Attention Module (CBAM) to improve feature extraction, incorporates the Bi-directional Feature Pyramid Network (BiFPN) for refined feature fusion, and introduces the FasterNet architecture to enhance detection speed without sacrificing performance. Experimental results show a 1.6% increase in mean Average Precision (mAP) compared to the original network, and the effectiveness of the proposed method is further validated against mainstream models.

关键词： Deep learning YOLOv5 solar cell defect detection EL image

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Leveraging Sequential Deep learning Models for Robust Compiler Bug Detection in Software Logs 3

Leveraging Sequential Deep Learning Models for Robust Compil...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mishra, Sahil Tiwari, Utkarsh Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Compiler bugs critically impact the correctness of software applications, making their detection and resolution vital for improving software quality. This paper proposes a novel approach leveraging sequential deep learning models, including Long short-term memory, Gated recurrent unit, Recurrent neural network, and a stacked ensemble model, to enhance compiler bug identification. By combining the strengths of these architectures, the proposed method improves predictive accuracy and generalization. To ensure model interpretability, we employ LIME (Local Interpretable Model-agnostic Explanations) to identify the features influencing bug detection decisions. Experimental results demonstrate that the stacked ensemble outperforms individual models in terms of precision, recall, F1-score, and ROC-AUC. This work advances the state of compiler log analysis and contributes significantly to software quality assurance by integrating robust deep learning techniques with explainable AI. © 2025 IEEE.

关键词： Application programs

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Optimized Feature Selection and Classification Framework for Cardiovascular Disease Using Statistical Normalization and Bio-Inspired Algorithms 3

Optimized Feature Selection and Classification Framework for...

引用

3rd International conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Muthumani, N. Akilandeswari, K. Ppg College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798331528201

Heart disease, which includes various circulatory illnesses that largely impact the structure and function of the heart, is a prevalent primary cause of morbidity and mortality. This long-term illness significantly lowers people's quality of life and raises healthcare costs. This illness will cause the body to react severely, including heart attacks and cardiac arrest, which may outcome in excruciating losses or even death. Early diagnosis is crucial for survival and treatment effectiveness in order to prevent severe consequences. Some medical diagnostic techniques, such as ECG, ECO, and stress tests, are available for the identification of cardiovascular illness;however, these techniques have certain drawbacks and restrictions caused by challenges with cost, personnel, and accessibility. Remedies are thus required to get over these drawbacks, which are satisfied by computer algorithms. Machine learning algorithms meet this requirement by combining various data types, handling metadata, and identifying medical risk factors and patterns that traditional methods are unable to identify. The Ant-Hybrid Support Vector Machine learning approach, which employs the hybrid kernel support vector machine learning algorithm for disease classification and Ant colony optimization for feature extraction, is used in this research work to enhance the capabilities of feature selection and classification. This technique will improve feature extraction and classification capabilities by identifying intricate patterns in the dataset that are overlooked by current approaches. The performance of the algorithms for the prediction of cardiovascular disease is compared using the different performance metrics such as Accuracy, Precision, Recall and this comparison clearly shows that the proposed model outperforms the existing algorithms with the highest accuracy of 98%. To increase model accuracy, the z-Score Normalization technique is employed during the preprocessing step to normalize the d

关键词： Ant colony optimization

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