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Tailoring Content with Keyword-Based Recommendation Engine Using Machine learning 3

Tailoring Content with Keyword-Based Recommendation Engine U...

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

作者： Prabha, M. Vijay Ananth, K.S. Vinoth Kannan, S. Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

It is the information age thus;it is very important to understand how to personalize content in accordance with what appeals to a user. This work is conceptualized to be labeled 'Tailoring Content with Keyword-Based Recommendation Engine Using Machine learning'. This work provides the applicative system catering for the experience of users by proposing personalization in terms of content. The core of the work is keyword-based recommendation engine chosen by the user to recommend posts as well as other contents believed to be of the user's interest. A recommendation system uses various types of algorithms related to machine learning and a combination of data analysis technologies while considering the interaction of the user with posts, created, liked, or participated in. From this, it will then understand keen interests by a user and make recommendations based on those grounds. This will keep users interesting about the relevant content and help develop skills in areas they are interested in. Some scope includes designing a recommendation engine, developing it into an application, and measuring performance. This would be in the ability of doing so, with accuracies at all high degrees, when recommending this content, scalability, and also privacy for the users. It will be used to prove how machine learning techniques can be applied towards developing personalized user experience and, as a by-product, contribute to this field known as content recommendation systems. © 2025 IEEE.

关键词： Convolutional neural networks

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An Efficient Deep learning based Approach for Pneumonia Detection 3

An Efficient Deep Learning based Approach for Pneumonia Dete...

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

作者： Shirisha, M. Sri Sai Durga, M. Bhanu Keerthan Reddy, G. Shanmugasundaram, Hariharan Vaishnavi, Bandar Natarajan, Karuppiah Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9798331527549

Pneumonia is a major global cause of death, hence early detection and precise diagnosis are essential for successful management. Conventional techniques for detecting pneumonia, like radiological interpretation and clinical assessment, are frequently laborious and prone to variation amongst observers. Deep learning models have transformed medical imaging by offering automated, reliable, and accurate diagnostic instruments. The goal of this paper is to use data (image) pre-processing techniques to increase the accuracy of the deep learning model, EfficientNetB0 for pneumonia identification. The dataset utilized in this paper consists of annotated chest X-ray images from the publically accessible source "Chest X-Ray Images (Pneumonia)";Paul Mooney submitted 5,863 photos in 2 categories on Kaggle. To enhance the model’s resilience and ability to generalize, the images undergo preprocessing using techniques such as normalization, gray scaling, median filtering, CLAHE, and data augmentation. A standardized evaluation framework is used to evaluate each model’s performance, and measures like F1 score and confusion matrix analysis are used. The results of this paper demonstrate how pre-processing the data can increase the accuracy of the deep learning model and can enhance medical imaging diagnostic accuracy and dependability. © 2025 IEEE.

关键词： Median filters

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Efficient Classification of Sugarcane Leaf Disease using Fine-Tuned MobileNetV2 3

Efficient Classification of Sugarcane Leaf Disease using Fin...

引用

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

作者： Archana Reddy, R. Singla, Seerat Gupta, Rupesh School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

In this work, we propose a deep learning approach to classify the health of sugarcane leaves using the transfer learning on MobileNetV2 architecture. This research aims to improve crop disease detection through the exploitation of a pre-trained model on ImageNet tailored to the specific task of classification into diseased or healthy sugarcane leaves. The dataset was split into three sets namely training, validation, and test with data augmentation and caching strategies to improve the model efficiency and performance. We froze some of the layers of the model MobileNetV2. This will allow selective extraction of features while other layers are open and adaptable to the dataset of the sugarcane leaf. After 50 epochs of training, the model performed very well. Loss and accuracy were very low, indicating minimal overfitting on the test set. A final confusion matrix was then derived in order to measure the reliability of the predictions across classes. The approach will thus validate the application efficacy of transfer learning for crop disease detection and its promotion of accurate, scalable classification in crop disease management. © 2025 IEEE.

关键词： Deep learning Image Classification MobileNetV2 Real-world Applications Sugarcane Disease Classification

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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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A Comprehensive Study on optimization Algorithms for Controller Placement in Software-Defined Networking 3

A Comprehensive Study on Optimization Algorithms for Control...

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

作者： Kolli, Maheswari Prabu, U. Karre, Meenakshi Geetha, V. Department of CSE Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Department of Information Technology Puducherry Technological University Puducherry India

ISBN: (纸本)9798331527549

In today’s context, the use of the Internet has significantly increased, and new technologies keep evolving. Traditional networks are struggling to keep up with the growing demand. The advent of Software Defined Networking (SDN) has become essential in data centers and also large scale area networks, playing a pivotal role in modern network architectures. SDN is characterized by its separation of the logically centralized design, along with the control plane and data planes. In SDN, the controller is a critical component that ensures smooth operations and consistent behavior across network. Determining optimal placement, quantity of controllers is vital for enhancing network performance. Identifying the best locations and the most efficient number of controllers is crucial to achieving seamless and resilient network operations. This study explores various advanced techniques for controller placement, focusing on minimizing latency. It emphasizes the development of optimization strategies that dynamically adapt to network conditions and traffic demands, presenting an approach to improving SDN controller placement. © 2025 IEEE.

关键词： Average Latency Controller Placement Problem Software Defined Networks

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Pavement Crack Segmentation using Incomplete Supervision Network 24

Pavement Crack Segmentation using Incomplete Supervision Net...

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3rd International conference on Artificial Intelligence and intelligent Information Processing, AIIIP 2024

作者： Wu, Wenxiu Jin, Yihui Fang, Zhihua Zhang, Jianfeng Fan, Xiaqi Song, Xiaoliang Port and Transportation Management Center Zhejiang Jinhua China Lanxi Transportation Construction Investment Group Co. Ltd. Zhejiang Jinhua China Lanxi Tongda Highway Maintenance Engineering Co. Ltd. Zhejiang Jinhua China Lanxi SHUNDA Road and Bridge Engineering Co. Ltd. Zhejiang Jinhua China

ISBN: (纸本)9798400707308

Challenges such as the lack of baseline pavement disease databases and limited segmentation accuracy and speed impact the performance of pavement disease segmentation and its practical engineering applications. To explore the feasibility of incomplete supervised learning, an experimental study generated inaccurate labels in batches by combining traditional image segmentation algorithms with manual methods, and the Mean Teacher model was tested. For imprecise supervised learning, the test F1 accuracy reached 0.572 after adding imprecise labels to the training set of self-collected data, although external imprecise labels were not feasible. In incomplete supervised learning, the Mean Teacher model showed significant advantages, achieving a test accuracy (MIoU) of 0.796 and an F1 score of 0.596 on self-collected data. Using unlabeled external images can enhance network accuracy in semantic segmentation tasks. With a small number of accurately labeled images, incomplete supervised learning can achieve substantial prediction accuracy. © 2024 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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Plant Leaf Disease Identification based on Machine and Deep learning Methods 3

Plant Leaf Disease Identification based on Machine and Deep ...

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

作者： Kalaiarasi, P. Ramalakshmi, R. Kannan, V. Bhuvanesh, A. Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Agriculture Kalasalingam Academy of Research and Education Krishnankoil India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (纸本)9798331527549

The substantial effects of plant diseases on agriculture, which result in losses in production of crops, financial losses, and food insecurity, highlight the necessity for plant leaf disease detection and categorization. In order to effectively control illnesses, stop them from spreading to other plants, and develop tailored treatment plans, early and precise disease identification is essential. Various strategies are used for plant leaf disease diagnosis, such as manual symptom assessment, computer image processing, and visual examination by skilled professionals. Traditional methods rely on human expertise to visually identify disease symptoms based on characteristic patterns, discolorations, or lesions on plant leaves. These techniques, however, are labor-intensive, time-consuming, and sensitive to bias and mistakes. The development of automated and computer-based techniques for the identification of plant leaf disease through the use of ML (machine learning) algorithms and digital imaging technology has garnered increasing attention in recent years. There are still a number of issues with the DL (deep learning) and ML algorithms. In order to identify research gaps and address them in the future while developing a unique framework, this study presents an examination of ML and DL models. © 2025 IEEE.

关键词： Deep learning

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Enhancing Adaptive Visual Impedance Control Through Proximal Policy optimization 3rd

Enhancing Adaptive Visual Impedance Control Through Proximal...

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3rd World conference on intelligent and 3D Technologies, WCI3DT 2024

作者： Huang, Yichao Zhao, Yuhan Guo, Jingzhen Lu, Yang Jiang, Yangmin Liu, Liteng Zhang, Sen Zhao, Wenlong University of Shanghai for Science and Technology Shanghai200093 China Centre for Collaborative Research Shanghai University of Medicine and Health Sciences Shanghai201318 China Center for Medicine Intelligent and Development China Hospital Development Institute Shanghai Jiao Tong University Shanghai200025 China Zhoupu Hospital Affiliated to Shanghai University of Medicine and Health Sciences Shanghai201204 China

ISBN: (纸本)9789819791279

Currently, the most robot arm controllers rely solely on either force information or visual information. However, controllers utilizing only one type of information often fail to meet the requirements of most tasks. A visual impedance control method is proposed, which integrates information from vision and end-effector force sensors. This method utilizes a Multi-modal synergistic approach to combine force and visual feedback. The impedance parameters in the visual impedance system are adaptive, and the PPO (Proximal Policy optimization) algorithm in deep reinforcement learning is used to find the optimal impedance parameters. The experiment results showed that the adaptive visual impedance control method adjusts impedance parameters through deep reinforcement learning, resulting in a 200% increase in force control accuracy compared to visual servo control. The Adaptive control method improves the reliability of the robotic system in interacting with task environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： End effectors

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Towards Sustainable Agriculture: Mango Leaf Disease Classification with Deep learning Models 3

Towards Sustainable Agriculture: Mango Leaf Disease Classifi...

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

作者： Aluvala, Srinivas Chauhan, Shanvi School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

This work proposes a deep learning-based method using the EfficientNetB5 model for the classification of mango leaf images into eight categories, including six disease classes (Powdery Mildew, Cutting Weevil, Anthracnose, Bacterial Canker, Sooty Mould, and Gall Midge), a healthy class, and Die Back. Training, validation, and test sets were formed from a publicly available collection comprising 4,000 labeled images. Using Adamax optimizer with hyperparameters including a batch size of 32, a learning rate of 0.001, and a dropout rate of 0.2, the model was fine-tuned. Supported by high precision, recall, and F1-scores across all classes, data augmentation techniques and an input image size of (224,224,3) helped the model, to achieve an amazing test accuracy of 97%. Minimal misclassification revealed by the confusion matrix analysis confirmed the model's consistency in disease diagnosis. This work conforms with the objectives of sustainable agriculture and responsible production through exact disease identification, so enhancing food security and supporting economic development in agricultural communities. For more general relevance in agricultural activities, future developments could be real-time deployment using mobile apps. © 2025 IEEE.

关键词： Fruits

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Transforming Agriculture with Cloud Computing: Data Processing and Analysis Innovations 3

Transforming Agriculture with Cloud Computing: Data Processi...

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

作者： Mohammed, Shehu Singh, Arun Kaur, Harpreet Lovely Professional University Department of Computer Application Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Lovely Professional University School of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798331515911

Cloud computing is a game-changer in modern agriculture, providing new forms to access enormous data analysis. In this paper, we investigate how cloud computing solutions are being integrated with agricultural practices to improve the efficiency, scalability and availability in managing data pertaining to agriculture. Farming operations generate massive amounts of data from a multitude of sources like sensors, drones, and satellite imagery., the performance bottleneck becomes the traditional methods with which the data are being processed. So the common factor in all these problems lies with Cloud computing itself, it has a scalable infrastructure that provides reliable analysis and Real-time processing of large data sets throwing light on an array of questions people have about various aspects. In this paper, we explore the major innovations in cloud computing that power precise agriculture and examine how machine learning algorithms, data fusion methods, and distributed processing platforms are utilized. It also discusses cloud-based solutions, which can reduce costs of the use of resources, crop yield prediction, and risk management to weather/climate and pests. Cloud computing opens up a world of monitoring, analysis, and optimization to help provide higher yields more efficiently while maintaining a commitment to sustainable agriculture. Challenges and considerations in deploying cloud computing in agriculture, e.g., data security risks, privacy concerns, and the need for stable internet connection for rural environments are also articulated in the paper. The results of this project underscore the vital importance of cloud technology in redefining the agri-future and providing a robust platform for smarter and more sustainable agriculture. © 2025 IEEE.

关键词： agricultural data processing Cloud computing data analysis machine learning precision agriculture sustainable farming

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