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

作者： Jayakumar, K. Muthupandi, G. Dhayanidhi, R. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

Tuberculosis (TB) is one of the leading causes of deaths globally, mainly in low- and middle-income countries. Early and accurate detection is crucial for effective treatment and disease control. In this paper, models like CNNs-VGG16, ResNet, and DenseNet, to name a few, are taken into an exhaustive study. optimization techniques, including transfer learning and ensemble methods, which can increase accuracy in the challenging diverse clinical settings, will be presented. Equitable healthcare outcome is achieved in terms of future research directions which include representative datasets and interpretability of the model. © 2025 IEEE.

关键词： Disease control

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Weed Detection in Farmlands using RCNN 3

Weed Detection in Farmlands using RCNN

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

作者： Sivakumar, V. Lokit Sushruth, T. Jayaa Suthersen, A.R. Gugan, S.K. Vellore Institute of Technology Vellore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Detection and classification of weeds are important for the optimization of farming applications as it allows to identify species so that they can be removed, competing with others who need nutrients to grow their crop. A variety of methods have been used in different research studies but there still exists a research gap for a comprehensive comparative analysis of multiple machine learning models. Here, we attempt to provide a comparative study for classic/not ensemble (Support Vector Machines (SVM), Random Forest etc.) machine learning models with Region-Based CNNs (RCNN). Last, we stack LSTM (a type of RNN) onto the model to making it more powerful with respect to time learning and performance.1300 images of sesame crops and different types of weeds, serving as a proxy for diverse agricultural scenarios. The proposed weed detection and classification technique demonstrates strong potential for practical applications, particularly when integrated with autonomous robots for real-time field deployment. The study contributes to the field of precision agriculture by providing comparative insights into machine learning and deep learning models, emphasizing the importance of temporal learning for weed management. © 2025 IEEE.

关键词： Support vector machines Precision agriculture Crops Nutrients Real-time systems Internet of Things Long short term memory Random forests optimization Farming

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Deep learning-Driven Computational Intelligence for Leaf Disease Identification for Smart Agriculture 3

Deep Learning-Driven Computational Intelligence for Leaf Dis...

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

作者： Phani Prasad, J. Jadala, Vijaya Chandra Department of CS&AI School of Computer Science and Artificial Intelligence SR University Warangal506371 India

ISBN: (纸本)9798331527549

In the context of smart farming, Artificial Intelligence (AI) plays a crucial role in enhancing agricultural practices, from soil management to crop yield optimization. By leveraging deep learning and image processing techniques, AI can address various challenges across different agricultural sectors, particularly in detecting and mitigating plant diseases. This paper focuses on the use of these technologies for leaf disease detection to minimize crop loss. Deep learning and image processing are key components in identifying, classifying, and monitoring plant diseases. The study reviews various types of leaf diseases and examines the effectiveness of existing algorithms for detection. To improve performance, an algorithm based on ensemble learning methods is proposed. Experimental investigation is conducted on tomato leaf diseases, followed by a comparative analysis of the results. The proposed AI-driven system enables early disease detection, facilitating timely monitoring and intervention, ultimately reducing crop loss and improving overall crop health through proactive management and precautionary measures. © 2025 IEEE.

关键词： Contrastive learning

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Advancing Deep learning on Edge Devices: Fine-Tuning and Deployment of YOLOv7 Model for Efficient Object Detection in AI based Computer Vision Applications 3

Advancing Deep Learning on Edge Devices: Fine-Tuning and Dep...

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

作者： Shekhar, Sudhanshu Sathwik, T.S. Pritwani, Mayank Mohana Ramakanth Kumar, P. Sreelakshmi, K. RV College of Engineering® Bengaluru India

ISBN: (纸本)9798331527549

This paper investigates the optimization and deployment of YOLOv7 deep learning model on NVIDIA Jetson Nano, an AI-focused edge computing platform for object detection in various computer vision applications. The work leverages TensorRT and quantization techniques for model acceleration for good detection accuracy. Further it examines performance metrics such as speed, accuracy, and resource utilization for image dataset. The model is trained using 80 different classes of objects and demonstrates the use of 6 classes. The average detection accuracy obtained 92.35% and the average processing time is 117.8ms. This work supports AI by demonstrating the feasibility of running deep learning models on edge devices and provides insight into the challenges and opportunities of optimizing AI models for energy-efficient, real-time operations on edge devices for various computer vision applications. © 2025 IEEE.

关键词： Adversarial machine learning

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Real-Time Traffic Prediction And optimization using Machine learning 3

Real-Time Traffic Prediction And Optimization using Machine ...

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

作者： Vineetha, Kundrapu Tharun Reddy, M. Jaffer Ali, Md. Prathima, Narisetty Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The Real-Time Traffic Prediction and optimization System may be described as enriched, spacious, algorithm-oriented, and designed to help contribute to the anti-symptomatic deprievement of increasingly congested urban traffic streams. The system with the help of integrating real time data of numerous sources like GPS tracker, traffic sensor as well as weather API, the system using advanced machine learning algorithm in order to predict traffic accurately and optimize routes based on it. This predictive capability then greatly improves the flow of vehicles, the time taken, and the occurrence of choke points. The structure of the system is fully based in the cloud, which makes it easy to scale and very compatible with other systems to amalgamate a vast array of data inputs with high dependability. Fully customizable and intuitive control panel gives live view of traffic, offers actionable stats, and instant control mechanisms to traffic operators to make correct decisions quickly. Congestion in urban areas has an influence on time taken to cover a certain distance, fuel consumption and emission to the atmosphere. On the basis of the above analysis, this work extends a Real-Time Traffic Prediction and optimization System (RTPOPS) using machine learning approach to predict the traffic density and to further optimize traffic signals and routes. The used system demonstrated successful management and reduction of vehicle density by 30%, and increasing speed limits by 18% and proved that intelligent Urban Mobility has the potential to be achieved. © 2025 IEEE.

关键词： Traffic congestion

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Guidelines for enhancing the energy performance regarding accessory dwelling units in Southern California

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JOURNAL OF BUILDING ENGINEERING 2025年 99卷

作者： Tao, Guanyu Jiang, Qingrui Minyag Huang, Chenyu Southern Calif Inst Architecture SCI Arc 960 E 3rd St Los Angeles CA 90013 USA Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst 1239 Siping Rd Shanghai Peoples R China Tongji Univ Coll Architecture & Urban Planning 1239 Siping Rd Shanghai Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

Considering California's severe housing shortage and the state's ambitious sustainability goals, accessory dwelling units (ADUs) emerge as a critical solution to increase housing density and address the issue. This paper addresses the residential energy performance and optimization in Southern California under the adoption of ADUs, finding energy-efficient design configurations and urban planning policy. The paper fills the gaps that most studies regarding ADUs are economic orientation, lacking energy and urban form study. The study proposed to set roof photovoltaics generation and energy use ratio, peak load, and average window sky view factors as optimization objectives. The optimization in the paper achieves the potential of increasing 34.7 %, reducing 13.2 %, and increasing 5.0 % of three objectives, respectively, in different schemes, with an average of 30.2 %, 10.7 %, and 3.8 %, respectively, in Pareto front results. In the meantime, the research establishes the framework of using machine learning, specifically extreme gradient boosting (XGBoost), which can largely boost the speed of generating data points by 142 times instead of going through data from traditional energy simulation. As a result, suggestions are being made to urban planners and policymakers, such as suggesting floor area ratio to approximately 0.45, limiting multi-story buildings, and promoting ADUs construction with area ratio of ADU and main building from 0.16 to 0.32 in three types of community. This study established a framework, providing tools for policymakers aiming to meet sustainability goals within Southern California's climatic and socioeconomic context and a ready-to-use ADUs urban planning guideline in Los Angeles County.

关键词： Building energy performance Accessory Dwelling Units Multi-objective optimization Machine learning Urban planning strategies

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Automatic Traffic Surveillance using Deep learning Model Under Extreme Climatic Conditions 3

Automatic Traffic Surveillance using Deep Learning Model Und...

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

作者： Venkataramani, Saravana Ganesh ICON Clinical Research Dublin Ireland

ISBN: (纸本)9798331527549

This research makes use of deep learning for creating an automatic traffic surveillance model to detect the various vehicles under extreme climatic weather conditions. The model proposed is an extension of the standard VGG16-Net model. The model has been tested using real time videos taken from online database repository. The dataset is available in the form of images of vehicles running under three conditions namely normal, snowy and rainy conditions. The model detects the conditions of weather in the traffic when vehicle move on road. Weather conditions namely rainy and snowy are predicted in this research. The model has been developed using Python programing language. Algorithms adopted in this research are Adam optimization Algorithm (AOA) and Stochastic Gradient Descent (SGD) for optimizing the model. Performance is estimated through f1-score, precision, accuracy and recall parameters. It was found that model for predicting weather condition showed 92 percent accuracy. © 2025 IEEE.

关键词： Stochastic systems

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Detection of Maize Plant Insects using Lightweight Deep-learning Scheme 3

Detection of Maize Plant Insects using Lightweight Deep-Lear...

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

作者： Vickram, A.S. Bhavani Sowndharya, B. Saveetha School of Engineering Department of Biosciences TN Chennai602105 India Simats Saveetha School of Engineering Department of Biosciences TN Chennai602105 India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Automatic plant condition monitoring using a chosen computer algorithm is one of the recent advancement in the agriculture domain. To ensure the plant health, it is necessary to monitor the pest which causes the considerable damage to the plant. This work considered the monitoring of maize (corn) from the study and the common pests which causes the severe damage to maize, like the grasshopper and beetle are analyzed automatically using a chosen Lightweight Deep-learning (LD) methodology. This work considered the MobileNet (MN) variants for the study and this proposed technique consist the following phases;digital image collection and converting it to 224x224 pixels, feature extraction using chosen MN-model, feature optimization using Butterfly-Algorithm (BA) and fused-features generation, and binary classification with 5-fold cross validation. This work considered the MN-variant model to examine the pest images and the achieved result confirms that this scheme presents a detection accuracy up to 96% when SoftMax based classification is executed. © 2025 IEEE.

关键词： Smart agriculture Condition monitoring Accuracy Databases Plants (biology) Digital images Insects Feature extraction Monitoring optimization

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An intelligent Bio-AI for Optimized Resource Allocation in 5G Networks 3

An Intelligent Bio-AI for Optimized Resource Allocation in 5...

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

作者： Nimmala, Satyanarayana Chilukuri, Ravindrareddy Janbhasha, Shaik Manasa, Pinnapureddy Mahendar, Maragoni Manoranjini, J. CVR College of Engineering Hyderabad India Department of CSE Neil Gogte Institute of Technology Hyderabad India Department of AI & DS Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798331527549

The rapid expansion of 5G networks necessitates sophisticated resource allocation algorithms to tackle the problems posed by fluctuating traffic conditions, varied device requirements, and rigorous Quality of Service (QoS) standards. This work presents the Bio-AI Allocator, a hybrid model that combines Deep Reinforcement learning (DRL) with Ant Colony optimization (ACO) for effective and adaptable resource management. The DRL model is trained via the publicly accessible 5G Quality of Service Dataset from Kaggle, which includes essential parameters like as signal strength, bandwidth utilization, latency, and user mobility patterns. The training utilizes Q-learning with episodic incentives to formulate optimal resource allocation strategies. Experimental findings indicate that the Bio-AI Allocator realizes a 20% decrease in latency, a 25% increase in throughput, and a 15% gain in energy economy relative to traditional approaches such as Round-Robin and Max-Min Fairness scheduling. The comparative analysis demonstrates the superiority of the proposed hybrid model compared to standalone AI and bio-inspired methods, proving the Bio-AI Allocator is a scalable and intelligent solution for next-generation 5G networks. © 2025 IEEE.

关键词： Ant colony optimization

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Application of Artificial Intelligence in Mobile Communication Networks: A Revolutionary Impact on Network optimization 3rd

Application of Artificial Intelligence in Mobile Communicati...

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

作者： Tian, Tony Co. Ltd. Beijing Branch Beijing100088 China

ISBN: (纸本)9789819791231

This study comprehensively explores the application of artificial intelligence (AI) in mobile communication networks and its revolutionary impact on network optimization. With the evolution of mobile communication technology from 2 to 5G and future 6G, the complexity of networks and user demands continue to increase, making traditional network management methods insufficient for modern communication systems. The introduction of AI technology, particularly machine learning and deep learning algorithms, plays a pivotal role in intelligent network optimization, fault diagnosis and prediction, personalized services, among others. The study also establishes an intelligent optimization framework, including data collection and analysis, performance metrics and network configuration, and dynamic learning with adaptive adjustments, aiming to continually enhance network performance and maintain its optimal state. In conclusion, AI technology is becoming a key driving force in propelling mobile communication networks towards higher efficiency and intelligence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Application: big data Artificial intelligence Mobile communication networks optimization framework

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