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4th International Conference on Expert Clouds and applications, ICOECA 2024

作者： Hebri, Dheeraj Kirubakaran, S. Jasmine, R. Rajitha Poosamooper, Anitha Devi, S. Rukmani Joseph, D Raja Srinivas Institute of Technology Department of Master of Computer Applications Karnataka Valachil Mangalore574143 India CMR College of Engineering and Technology Department of Computer Science Engineering Telangana Hyderabad501401 India R.M.K. Engineering College Department of Information Technology Tamil Nadu Kavaraipettai Thiruvallur601206 India Vel Tech Ranga Sanku Arts College Department of Computer Applications Tamil Nadu Chennai600062 India Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Department of Computer Science Tamil Nadu Chennai600124 India School of Aeronautical Sciences Hindustan Institute of Technology and Science Padur Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350385793

Remote sensing is concerned with high-resolution geographical data. Its applications include farming, water resources, ecology, forests, deserts, marine applications, soil types, disaster alerts, and management. To extract the necessary information from satellite images, considerable image processing is required. Image segmentation and categorization into land cover categories is an important stage in satellite image processing. This article describes a method for categorizing and detecting different forms of land cover from satellite data. In this study, we examine satellite images for historical map classification using machine learning algorithms. We acquire Landsat images to complete this tedious task. The images are carefully labeled using the ArcMap software. We categorize areas into five types: forest, building, water, barren, and agricultural land. The images are then sent to the Convolutional Neural Network (CNN) Model for feature extraction. The retrieved features were then exposed to three Machine Learning (ML) models: Support Vector Machine (SVM), Naive Bayes (NB), and Decision Tree (DT). Standard classification measures are used to analyze the outcomes of all three models. When compared to the other two models in satellite image classification, CNN feature extraction with SVM classifier achieves the greatest accuracy of 95%. © 2024 IEEE.

关键词： Convolutional neural networks

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A Grading of PDN Based CC Networks 4

A Grading of PDN Based CC Networks

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Singh, Suresh Al-Farouni, Mohammed Singh, Devendra Varshney, Neeraj Arvind, Sudha Krishnaraj, N.S. Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India The Islamic University Najaf Iraq Uttaranchal University Uttaranchal Institute of Technology Department of Computer Science & Engineering Dehradun India Gla University Department of Computer Engineering and Applications Mathura India Cmr Technical Campus Department of Ece Telangana Hyderabad India Department of Information Science and Engineering Nitte India

ISBN: (纸本)9798350360165

Network systems function based on rules that are intrinsically dynamic, subject to temporal circumstances set by outside events like host utilisation, bandwidth measurements, intrusion detection, or time-specific events. Software-defined networking (SDN) presents an opportunity to streamline network configuration through the provision of more sophisticated configuration tools. In an attempt to lower network monitoring costs and traffic overheads, we offer a software-defined cloud resource management solution that customises network resource distribution using a Flexible Analytical Hierarchy Procedure (Fuzzy-AHP). Through an Application Programme Interface, this framework may be easily integrated into cloud infrastructures that support SDN (API). We demonstrate how our system improves network resource management and efficiently responds to growing traffic requests using real-time data. Moreover, we confirm the performance of our framework using simulations. © 2024 IEEE.

关键词： and fuzzy analytical hierarchy process (fuzzy-AHP) Cloud computing network management resource management scheduling software-defined networking

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An Adaptive Deep Learning Framework for Prediction of Agricultural Yield 2

An Adaptive Deep Learning Framework for Prediction of Agricu...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Rao, K Rama Swapna Kumari, Manthena Eklarker, Ravindra Shaker Reddy, Pundru Chandra Muley, Kirti Burugari, Vijay Kumar Geethanjali College of Engineering and Technology Electronics and Communication Engineering Telangana Hyderabad India Hyderabad India Vardhaman College of Engineering Department of Ai &ds Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Bharati Vidyapeeth's Institute of Management and Information Technology Master of Computer Applications Navi Mumbai India Koneru Lakshmaiah Education Foundation Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350317558

Agriculture and its large yields are vital to human survival. Recently, machine learning and deep learning have been applied to determine agricultural output. This study estimated apple production utilizing a unique deep-learning(DL)-based technique. A study-designed convolutional neural network (CNN) classified golden and royal gala apple photos. Classifying CNN-extracted pictures with traditional machine-learning(ML) methods was a hybrid application. Datasets A is used to evaluate the performance of the proposed model. Based on Dataset A accuracy findings, the CNN-SVM hybrid model performed best. CNN-SVM and CNN-Gradient Boosting hybrid models performed best and accurately for Dataset A. The more balanced distribution of train, test, and validation sizes, the suggested hybrid CNN model, and the model's assessment made dataset C the best choice. The hybrid model was 99.70% accurate for Dataset A. Precision, recall, f-measure, and Cohen kappa were 99%. The study found that hybrid models could accurately predict apple fruit yield using golden and royal gala photos. © 2024 IEEE.

关键词： DL Ensemble-Methods ML Smart-farms Yield-Prediction

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Sentiment analysis of reviews using Nature Inspired Algorithm 4

Sentiment analysis of reviews using Nature Inspired Algorith...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Tripathy, Abinash De, Utpal Chandra Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Pattanayak, Binod Kumar Pandey, Trilok Nath School of Computer Applications KIIT Deemed to be University Odisha Bhubaneswar India Department of Computer Sc. & Engg ITER SOA Deemed to be University Odisha Bhubaneswar India School of Computer Science & Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350305258

Social media now plays an important role is sharing the views or comments about a topic. These comments are available to the users for their processing and future decision making process. As the reviews contains mostly text or emojis, thus, these need to be processed properly to get meaningful information. Nature is always a source for information and knowledge. Many activities happens in the words, that help to classify the reviews or comments. In this paper, Genetic Algorithm (GA) is considered for processing the reviews. GA is based on the process of genetic evaluation. Different GA operations are implemented to get a better classification result. The reviews are processed using the proposed algorithms and finally, the confusion matrix is generated, which helps to generate the evaluation parameters to evaluate the results. © 2023 IEEE.

关键词： Confusion matrix Firefly algorithm Particle Swarm Optimization Performance evaluation parameter Sentiment analysis

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A Systematic Review of Deep Learning Approaches for Vessel Segmentation in Retinal Fundus Images 2

A Systematic Review of Deep Learning Approaches for Vessel S...

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2nd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2023

作者： Hegde, Govardhan Prabhu, Srikanth Gupta, Shourya Prabhu, Gautham Manuru Palorkar, Anshita Srujan, Metta Venkata Bhandary, Sulatha V Department of Computer Science and Engineering India Department of Data Science and Computer Applications India Department of Information and Communication Technology Manipal Institute of Technology Karnataka Manipal576104 India Department of Ophthalmology Kasturba Medical College Karnataka Manipal576104 India

Retinal vessel segmentation (RVS) is important to accurately differentiate retinal vasculature for diagnosing and monitoring various ocular and systemic diseases. The traditional methods for RVS have mostly involved supervised learning, although semi-supervised and unsupervised techniques are on the rise. This paper reviews the increase in complexity of developments in RVS primarily after 2020. The methods were chosen to cover both the gradual transition over time and a variety of unorthodox or combinatorial approaches. This includes convolutional neural networks, encoder-decoder models, generative models, and other multi-modal or hybrid techniques. CNN approaches discussed employ Zero Phase Component Analysis, Global Contrast Normalization, and reinforcement learning. Encoder-decoder models include approaches such as the use of skip and residual connections, spatial attention, and atrous enhancement U-Net. Generative models propose short link connections, recurrent residual blocks, and multi-scale features to refine convolutional blocks. Hybrid methods involve the use of connectivity features, the MISODATA Algorithm, cross-domain adaptation, and multiple filters (such as morphological, match, and Gabor). All the frameworks are compared based on their performance on the benchmark dataset DRIVE to provide a comprehensive understanding of the current state of RVS. © Published under licence by IOP Publishing Ltd.

关键词： Diagnosis

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A Wearable EEG-Based Hybrid CNN-RNN Framework for Psychiatric Disorder Diagnosis: Leveraging XAI for Enhanced Clinical Insights 6

A Wearable EEG-Based Hybrid CNN-RNN Framework for Psychiatri...

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6th International Conference on Devices for Integrated Circuit, DevIC 2025

作者： Siddiqui, Shams Tabrez Syed, Ziauddin Hashmi, Syed Ghyasuddin Ahmad, Jorair Singha, Anjani Kumar Qidwai, Khalid Ali Jazan University College of Engineering and Computer Science Department of Computer Science Jazan Saudi Arabia SRM Institute of Science and Technology Department of Computer Applications UP Ghaziabad India

ISBN: (纸本)9798350391107

Mental health disorders like schizophrenia, anxiety, and depression are major causes of disability worldwide, yet diagnosis remains difficult due to the lack of objective biomarkers. This study introduces a hybrid CNN-RNN framework leveraging wearable EEG sensors and deep learning for more accurate diagnostics. CNNs extract spatial features, while RNNs model temporal patterns, improving performance over traditional and standalone deep learning models. Evaluated on public and custom datasets, the model achieves superior accuracy, precision, recall, F1-score, and ROC-AUC. Explainable AI techniques enhance interpretability, identifying clinically relevant EEG patterns. Cross-Dataset validation confirms robustness, though challenges like dataset size, real-time processing, and ethical concerns remain. This approach advances objective, data-driven mental health diagnostics, promoting more effective and accessible care. © 2025 IEEE.

关键词： mHealth

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An end-to-end smart IoT-Driven navigation for social distance monitoring framework for Covid-19 using deep convolutional neural networks in Deep Learning

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Measurement: Sensors 2024年 32卷

作者： Banothu, Balaji Ramdas, Vankdothu Department of Computer Applications National Institute of Technology Trichy India Department of Computer Science and Engineering Balaji Institute of Technology & Science Laknepally Warangal India

The extraordinary worldwide spread of the COVID-19 coronavirus pandemic has considerably boosted the development of new Internet-of-things (IoT)-based strategies to stop, stop, monitor, or foresee virus propagation among people. These technologies are being used more often for a variety of practical purposes that include enhanced safety, discipline, and control. The novel coronavirus creates a global pandemic and affects a human, besides a huge death rate. As there is no proper medication, at least the spread should be stopped or it should be minimized. One way is to avoid physical contact. People should maintain a minimum distance to avoid direct contact. This paper presents detecting the social distance between pedestrians in public places. The idea is to take live videos from the camera we set up in a public place and create bounding boxes for each person in the video. If two or more people are close to each other violating social distance, then we put a red box around them otherwise we put a green box. The proposed framework's experimental results exhibit good results in monitoring the distance among humans in public areas compared to the existing methods. © 2024 The Authors

关键词： Coronavirus

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Deep Reinforcement Learning for Optimizing Route Planning in Urban Traffic

Deep Reinforcement Learning for Optimizing Route Planning in...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Mittal, Mudit Sehgal, Archana Varshney, Neeraj Kumar, Sunil Prashanth Boob, Nandini Shirish Reddy, R. Akhilesh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

Urban traffic networks are efficient route design problems due to the intricacy and dynamic character of contemporary cities. Conventional methods hamper real time adjustment to continually changing traffic conditions, thereby providing suboptimal routes, a greater burden on the roadways, and greater air pollution. This thesis presents a novel deep reinforcement learning (DRL) method to optimize the route planning for urban traffic networks. With this rendering of real time traffic data, we use DRL to make autonomous decisions to reduce travel time and congestion of routes. One advantage of using sophisticated neural networks for feature extraction and policy learning is that, since the model is so adaptable to changing traffic patterns, the model guarantees that. And, in comparison to common routing algorithms, the results of simulations using real world traffic data show that this method is able to greatly enhance route efficiency and overall traffic flow management. The suggested DRL based system is feasible as a smart city program based on intelligent transportation system which can scale to grand metropolitan regions. The research presents that DRL can enhance the commuter run experience, reduce the environmental effect, and alter urban transportation. Future study will also investigate how this system can be better optimized when integrated with traffic infrastructure enabled by the Internet of Things. © 2025 IEEE.

关键词： Deep reinforcement learning

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Harnessing Tech Innovations for Diversified Multigenerational Work Force and Work Life Balance

Harnessing Tech Innovations for Diversified Multigenerationa...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Mittal, Mudit Sharma, Vishal Varshney, Neeraj Kumar, Sunil Prashanth Boob, Nandini Shirish Reddy, R. Akhilesh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

This research aims to examine how technologically innovative breakthroughs may help a multigenerational heterogeneous workforce to maintain a good work and life balance. We investigate how technology can be used to strategically raise up the rate of participation and align with the particular needs of different age groups. The research findings show how AI powered personal learning, collaborative digital communication tools and flexible remote working solutions promote a healthy and mature culture in the workplace. The findings explain how key it is to utilize technology in a refined manner such that all workers can thrive and maintain a sustainable balance between their work and home life. © 2025 IEEE.

关键词： Collaborative learning

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Smartening Energy Consumption: A Comparative Study of Energy Optimization Strategies in IoT Environments 8th

Smartening Energy Consumption: A Comparative Study of Energy...

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8th International Conference on Information System Design and Intelligent applications, ISDIA 2024

作者： Biswal, Sudhansu Mohan Mohapatra, Ambarish G. Panigrahi, Sunil K. Mishra, Sunil K. Mohanty, Jnyana Ranjan Bajaj, Mohit Barik, Rabindra K. Department of Electronics Silicon Institute of Technology Bhubaneswar India Department of Computer Science and Engineering Einstein Academy of Technology and Management Bhubaneswar India School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India School of Computer Applications KIIT Deemed to be University Bhubaneswar India Dehradun India Graphic Era Hill University Dehradun248002 India

ISBN: (纸本)9789819748945

Nowadays, the Internet of Things (IoT) platform is being utilized extensively to integrate the real world with cyber-physical systems. The IoT is being used to share data among small and resource-constrained devices. Thus, optimization has the key to providing energy efficiency as well as the suitability of network architectures of IoTs. This article portrays a brief introduction to optimization strategies applied in different energy-efficient IoT and M2M technologies. In this article, a basic energy optimization strategy for IoT architecture is demonstrated. The simulation focuses on minimizing energy consumption by implementing a simple optimization algorithm. The optimization algorithm iterates through each device, selectively turning off devices that fall below their energy thresholds. The simulation results reveal the total energy consumption before and after optimization, highlighting the percentage reduction achieved. While this simulation provides a basic framework, real-world energy optimization in IoT systems involves more complex algorithms that consider various factors such as data processing requirements, network conditions, and communication protocols. Nevertheless, the presented result serves as a starting point for researchers and practitioners to explore energy optimization strategies in IoT networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Energy efficiency Energy optimization IoT IoT architecture M2M

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