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International Conference on ent Computing and Control Systems (ICICCS)

作者： Venkata Srinivasulu B K.Sai Dixith Keerthi Shivaram Ch.Karthik Reddy Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Hyderabad Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Precision agriculture is a fast growing field that may contemporary issues surrounding agricultural sustainability are increasingly being addressed through innovative technologies. Machine learning, a cutting-edge tool that enhances precision agriculture, enables sophisticated methods for detecting and classifying plant diseases. This paper offers a comprehensive review of how ml and DL techniques are applied in precision agriculture, focusing on disease identification and categorization. A novel classification framework is introduced, organizing relevant studies into distinct categories—Method-wise Classification Based Approaches. First, we categorize the studies method wise into classification (e.,g, image, and video-based studies) or object detection (e. g., bounding box-based studies) algorithms. Moreover, we provide existing archives for the classification and tracking of plant infections. By presenting a systematic framework that groups machine learning techniques according to their methodology, this study seeks to close the gap between the current plant disease classification techniques. We offer a comparative study to identify the best methods for disease identification by assessing the effectiveness of cutting-edge classification and object recognition algorithms on the PlantDoc dataset. In addition to streamlining precision agriculture research, the suggested architecture provides a basis for creating plant disease detection models that are more precise and scalable.

关键词： Precision agriculture Deep learning Plant diseases Machine learning algorithms Cameras Real-time systems Classification algorithms Computational efficiency Object recognition Sustainable development

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Image Based Identification of Precious Stones Using Edge Computing Devices with YOLOv8 on Raspberry Pi 5 5th

Image Based Identification of Precious Stones Using Edge Com...

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Vrindavanam, Jayavrinda Kumar, Pradeep Bharadwaj, Abhilash S. Kamath, Gaurav Chandrashekar, N. Narayanan, Anirudh Department of Computer Science & Engineering (AI & ML) Dayananda Sagar University Bengaluru India

ISBN: (纸本)9789819788644

This research paper addresses the tausk of image-based identification of precious stones, focusing on the integration of edge computing devices, specifically the Raspberry Pi 5, and utilizing the YOLOv8 instance segmentation model for gemstone identification. The study explores the challenges associated with classifying diverse gemstones and leverages the computational capabilities of the Raspberry Pi 5 for efficient edge processing. We present a detailed investigation into the design and implementation of the YOLOv8 instance segmentation model on the Raspberry Pi 5, highlighting the model’s efficacy in accurately identifying and classifying various precious stones based on image input. The experimental results demonstrate the feasibility of deploying sophisticated image-based classification systems on edge devices, with a particular emphasis on gemstone recognition. The combination of edge computing and advanced instance segmentation techniques contributes to the development of a portable and efficient solution for gemstone identification, presenting opportunities for real-world applications in industries such as jewelry authentication and gemology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Edge devices Gemstone identification Jewelry authentication Raspberry Pi 5 YOLOv8 instance segmentation

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Transfer Learning based Analysis of Chest X-rays for Accurate Lung Disease Detection and Interpretation 3

Transfer Learning based Analysis of Chest X-rays for Accurat...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Perla, Someswari Veledendi, Sudhakar Jaiswal, Pooja Muppidi, Satish Mandala, Jyothi Maram, Balajee Gmr Institute of Technology Department of Cse - Ai&ml Andhra Pradesh Rajam India Sr University School of Computer Science and Artificial Intelligence Warangal India Galgotias University India Gmr Institute of Technology Departnent of Computer Science and Engineering Rajam India University Department of Computer Science and Engineering Banglore India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798331515911

This is a research paper based on a transfer learning approach with a primary aim at the analysis of chest Xrays for accurate detection and interpretation of lung diseases. The proposed method relies heavily on the use of pretrained deep learning models to enhance diagnostic accuracy and reduce the time and computational resources taken during training. Applying transfer learning to a large chest X-ray dataset, the model successfully detects key patterns associated with common lung diseases, such as pneumonia and tuberculosis. The manuscript encompasses data preprocessing, model finetuning, and performance evaluation and demonstrates huge improvements over the traditional methods both in terms of accuracy and interpretability. It has been experimentally proven that the model is competent enough to provide localization of disease areas, as it can be visualized through heatmaps obtained from predictions, which might further help the radiologists perform their diagnosis tasks. This work advocates for medical imaging automation for the early and efficient detection of lung disease. © 2025 IEEE.

关键词： Lung cancer

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Optimized Image Compression Using Multiple Compressed Sensing Techniques

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SN computer science 2025年第4期6卷 1-18页

作者： Puttegowda, Kiran Mohan, B.A. Veeraprathap, V. Vijay, C.P. Sudheesh, K.V. Kumar, D. S. Sunil Department of ECE Vidyavardhaka College of Engineering Karnataka Mysuru India Department of ISE B.M.S. Institute of Technology and Management Yelahanka Karnataka Bengaluru India Department of ECE ATME college of Engineering Karnataka Mysuru India Department of Computer Science Engineering (AI & ML) Vidyavardhaka College of Engineering Mysuru Karnataka India Department of Computer Applications East Point College of Higher Education Banglore India

In the digital era, the demand for efficient image compression techniques is ever-increasing due to the exponential growth of visual data. Compressed sensing (CS), a groundbreaking theory in signal processing, offers a promising solution by exploiting the inherent sparsity of images. The proposed work introduces a novel approach to image compression, leveraging compressed sensing techniques to achieve optimized results. Our method works by expressing image data in a higher-order non-raw and non-dense format hence dramatically decreasing the size of the entire data. This significantly reduces storage requirements while preserving image quality. By combining advanced signal processing with innovative image analysis techniques, our approach maintains low computational complexity, enabling real-time applications without sacrificing the fidelity of reconstructed images, even after compression. This makes our approach especially valuable for applications where storage space is limited but image integrity remains crucial. We present experimental results demonstrating the effectiveness of our technique across various image types and compression ratios. Our findings underscore the potential of compressed sensing as a transformative paradigm in the realm of image compression, paving the way for more efficient storage and transmission of visual content in diverse applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Compressed Sensing Compression Compression ratio Images Reconstruction

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Smart System for Missing Child Identification

Smart System for Missing Child Identification

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Vineela Madireddy Jonnalagadda Varsha K. Ram Prasad T. Sai Bhargava Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Identifying and recovering missing children is a significant challenge faced by law enforcement agencies and social organizations worldwide. Every year, many individuals go missing, and a considerable number of them are never found. The complexity of these cases increases when missing individuals are located far from their original location, requiring substantial time and effort to confirm their identities. Traditional identification methods, which depend heavily on manual efforts and police resources, are often slow and inefficient. Delays in these processes can lead to serious consequences, reducing the chances of finding a missing child promptly. To overcome these challenges, a System is introduced that uses Convolutional Neural Networks (CNN) for extracting facial features and the K-Nearest Neighbors (KNN) algorithm for matching images. This approach aims to enhance the identification process by comparing photos of found children with a centralized database of missing children. The system allows the public to upload images of children who might be missing, which are then automatically analyzed and matched against the stored images. Additionally, users can provide relevant information such as location details, landmarks, and contact information. Designed to adapt to variations in age and image quality, the CNN model ensures reliable matching. Upon receiving an image, the system performs an automated comparison—either identifying a match or storing the image for future reference. If a match is found, the system promptly provides relevant recovery details to assist in reuniting the child with their family. By leveraging automation and machine learning, this approach significantly reduces identification time, increases efficiency, and enhances the success rate of locating missing children. Optimizations like FaiSS indexing, and dimensionality reduction enhance efficiency, ensuring real-time identification with reduced computational overhead and improved scalability.

关键词： Law enforcement Scalability Organizations Manuals Nearest neighbor methods Smart systems Reliability engineering Real-time systems Convolutional neural networks Optimization

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Hybrid CNN and RNN Variant Model for Indian Music Genre Classification

Hybrid CNN and RNN Variant Model for Indian Music Genre Clas...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Swathi Surapally Siva Ramakrishna Jeevakala Sriya Pranathi Aluri Computer Science and Engineering (AI & ML) Institute of Aeronautical Engineering Hyderabad India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Music genre classification contributes largely to information retrieval for organizing growing collections of music. The process of classification of music is very difficult and unreliable. Many methods utilized handcrafted features to identify unique patterns but were still unable to determine original music characteristics. In comparison, music classification by applying deep learning models exhibits a dynamic and effective potential. Although there are a large number of neural networks, the combination of CNN and variants of RNN has not received adequate consideration. Moreover, on the shortcomings of the specific neural network classification model, we would like to propose a hybrid architecture of CNN and variant of RNN, such as a GRU. In addition, we compare the performances based on Mel-spectrogram and MFCC features.

关键词： Analytical models Time-frequency analysis Tagging Smart systems Spatial databases Robustness Convolutional neural networks Tuning Spectrogram Recommender systems

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Ensemble Learning-based Image Captioning with NLP for Context-Aware Descriptions

Ensemble Learning-based Image Captioning with NLP for Contex...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： P. M. Naidu S. Dhanesh Sai M. Naveen Ch. Anil Kumar Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Image captioning is a challenging task that requires an appropriate combination of computer vision and natural language processing to describe an image accurately and with context relevance. This paper presents an advanced method for captioning images using an ensemble learning setup along with an NLP-based fusion layer. The proposed system analyzes images from various perspectives by using multiple pre-trained models to capture diverse features, and the generated captions are then combined into a single coherent description using the fusion layer. The system’s efficiency is demonstrated using popular evaluation metrics such as BLEU, ROUGE, and METEOR, showing high accuracy and computational efficiency. This system is designed to work efficiently for a variety of image types, ensuring scalability and real-time application compatibility. The proposed method addresses common challenges in image captioning, including ambiguity, context preservation, and ensuring relevance across different datasets. Qualitative evaluation confirms the system’s ability to produce human-like captions, making it suitable for a wide range of applications such as assistive technologies, autonomous systems, and content generation.

关键词： Measurement Deep learning Scalability Feature extraction Control systems Natural language processing Real-time systems Meteors Ensemble learning Convolutional neural networks

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Adaptive residual convolutional neural network for moiré image restoration

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International Journal of Information Technology (Singapore) 2025年第2期17卷 783-791页

作者： Malagi, Vindhya P. Naresh, E. Mithra, C. Suresh, B. V. N. V. Krishna Department of AI & amp ML Dayananda Sagar College of Engineering Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai 600062 India Computer Science & amp Engineering KLEF KL University Andhra Pradesh Vaddeswaram India

Restoring Moiré images presents significant challenges due to the complex interference patterns that obscure image details. These patterns often degrade the quality of images, making accurate restoration crucial for various applications. Effective Moiré image restoration requires advanced techniques to overcome the difficulties posed by these intricate artifacts. This study introduces an Adaptive Residual Convolutional Neural Network (RCNN) for Moiré image restoration, augmented with the Zebra Optimization Algorithm (ZOA) to enhance both feature extraction and restoration accuracy. The hybrid model leverages ZOA's capability to balance exploration and exploitation, optimizing network parameters to improve learning efficiency. Tested on the DIV2K dataset, this approach demonstrates significant improvements in handling complex Moiré patterns, as evidenced by enhanced Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values. © The Author(s) 2024.

关键词： Adaptive residual convolutional neural network Moiré images Peak signal-to-noise ratio Structural similarity index measure Zebra optimization algorithm

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Comparative Analysis of Sorting Algorithms: Time Complexity and Practical Applications

Comparative Analysis of Sorting Algorithms: Time Complexity ...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Manish Rai Hemlata Parmar Suhani Sharma Department of Computer Science and Engineering(AI&ML) Manipal University Jaipur Jaipur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In the field of computer science, sorting algorithms are crucial because they facilitate the effective processing and arrangement of data in a variety of scenarios, including data analysis, searching, and optimal system operation. The objective of this study is to look at and compare different sorting algorithms to see how well they work and how useful they are in different situations. The study tests popular sorting algorithms like Quick Sort, Merge Sort, and Bubble Sort on different input sizes, types of data (sorted, reversed, and random), and how they are implemented in the C++ programming language. The study shows how they work in terms of time complexity, resource use, and real applications through a systematic analysis. The most important results show that algorithms work differently depending on the inputs. For example, Quick Sort does better in most situations, while Merge Sort stays stable in the worst ones. This work also finds situations where simpler algorithms, like Bubble Sort, are good for small data sets. This paper is unique because it gives real-life cases, suggestions for choosing the best algorithm based on specific needs, and suggestions for how to do that. The goal of these results is to help programmers, teachers, and experts pick the right sorting algorithms for their computer tasks.

关键词： Machine learning algorithms Data analysis Systematics Systems operation Instruction sets Machine learning Internet of Things Time complexity Optimization Sorting

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CbcErDL: Classification of breast cancer from mammograms using enhance image reduction and deep learning framework

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Multimedia Tools and Applications 2025年第15期84卷 15501-15526页

作者： Agrawal, Rohit Singh, Navneet Pratap Shelke, Nitin Arvind Tripathi, Kuldeep Narayan Singh, Ranjeet Kumar Department of Computer Science and Engineering Madhav Institute of Technology & Science Race Course Road MP Gwalior474005 India School of AI Bennett University Uttar Pradesh Greater Noida201310 India School of Computer Science and Engineering Bennett University TechZone 2 UP Greater Noida201310 India

Breast cancer is a major health concern for women worldwide, and early detection is vital to improve treatment outcomes. While existing techniques in mammogram classification have demonstrated promising results, their limitations become apparent when applied to larger datasets. The decline in performance with increased dataset size highlights the need for further research and advancements in the field to enhance the scalability and generalizability of these techniques. In this study, we propose a framework to classify breast cancer from mammograms using techniques such as mammogram enhancement, discrete cosine transform (DCT) dimensionality reduction, and deep convolutional neural network (DCNN). The first step is to improve the mammogram display to improve the visibility of key features and reduce noise. For this, we use 2-stage Contrast Limited Adaptive Histogram Equalization (CLAHE). DCT is then used to enhance mammograms to reduce residual data. It can provide effective reduction while preserving important diagnostic information. In this way, we reduce the computational complexity and increase the results of subsequent classification algorithms. Finally, DCNN is used on size-reduced DCT coefficients to learn feature discrimination and classification of mammograms. DCNN architectures have been optimized with various techniques to improve their performance, including regularization and hyperparameter tuning. We perform experiments on the DDSM dataset, a large dataset containing approximately 55,000 mammogram images, and demonstrate the effectiveness of the proposed method. We assess the proposed model’s performance by computing the precision, recall, accuracy, F1-Score, and area under the receiver operating characteristic curve (AUC). We achieve Precision and Recall values of 0.929 and 0.963, respectively. The classification accuracy of the proposed models is 0.963. Moreover, the F1-Score and AUC values are 0.962 and 0.987, respectively. These results are better a

关键词： Discrete cosine transforms

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