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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Jain, Prateek Joshi, Amit M. Mohanty, Saraju P. Nirma University Electronics & Instrumentation Engineering Ahmedabad India MNIT Electronics & Communication Engineering Jaipur India University of North Texas Computer Science and Engineering Denton United States

ISBN: (纸本)9798331533229

Nowadays, diabetes is a critical health issue for human beings. With the concern of being diabetic, people are more conscious of continuous glucose monitoring and medication. For this purpose, people are completely focused on smart monitoring devices, which can instantly measure glucose value without pain. However, the continuous glucose monitoring is not enough for diabetes prediction. Physiological parameters and other conditions are key factors that increase the likelihood of diabetes. Hence, it is required to propose a model, which predicts diabetes based upon the physiological parameters. In this paper, a machine learning-based diabetes prediction framework has been proposed, which evaluates the likelihood of diabetes based on diastolic blood pressure (DBP), body mass index (BMI), insulin intake, blood glucose value and skin thickness. The proposed framework has been calibrated using the DNN model. 543 subject observations have been taken to calibrate, validate and test the proposed DNN model. Overall 82% accuracy has been reported as a true prediction of diabetes. 0.0705 mean absolute error (MAE) has been observed in the predicted likelihood of diabetes using an optimized DNN model. The proposed iGLU 4.1 diabetes prediction paradigm helps the doctor diagnose and prescribe medication at remote locations. © 2024 IEEE.

关键词： Prediction models

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Designing of Smart Pothole Detection System 9

Designing of Smart Pothole Detection System

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9th International Conference on communication and electronics Systems, ICCES 2024

作者： Maji, Anushka Sinha, Sagnik Banerjee, Subhabrata Das, Amit Kumar Gangopadhyay, Malay Rateria, Roshan Institute of Engineering & Management Department of Computer Science and Engineering Kolkata India Institute of Engineering & Management University of Engineering and Management Department of Electronics & Communication Engineering Kolkata India Institute of Engineering & Management University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350377972

Recently, Artificial Intelligence (AI) algorithms have been used to detect the potholes. In this article, a machine learning algorithm has been designed to solve the problem efficiently. A software model has also been created for drivers, enabling them to detect and recognize potholes in advance through a mobile application. This mobile app utilizes various sensors to detect potholes and provides timely alerts to the driver. Moreover, the system is capable of responding to newly detected potholes and alerts the driver about them. The proactive notification system improves road safety by ensuring drivers are informed about upcoming road hazards. © 2024 IEEE.

关键词： Mobile applications

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Performance analysis of free space optical system incorporating circular polarization shift keying and mode division multiplexing

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Journal of Optical communications 2024年第2期45卷 347-354页

作者： Meenu Sharma, Anshu Malhotra, Rahul Department of Electronics and Communication Engineering CT Institute of Engineering Research and Technology Greater Kailash Maqsudan Punjab Jalandhar144008 India Computer Science and Engineering CT Institute of Technology & Research Greater Kailash Maqsudan Punjab Jalandhar144008 India Computer Science and Engineering CT Institute of Technology and Management Shahpur Near Lambra Punjab Jalandhar144020 India

In this work, a free-space optical (FSO) communication system with the integration of mode division multiplexing and circular polarization shift keying (CpolSK) is proposed at 2 × 40 Gbps using LG00 and LG01 modes. Effects of diverse weather conditions such as clear weather, light rain, moderate rain, heavy rain, thin fog, thick fog, and heavy fog are studied on system performance. Further, a detailed comparison of CpolSK and polarization shift keying (PolSK) is performed at different FSO lengths in terms of log bit error rate. For implementation, analysis, and comparison, Optiwave Optisystem software is used and results show that CpolSK covers 100 km link distance and PolSK limits to 90 km only. Also, LG00 mode performs better than LG01 mode under all weather instabilities in the proposed system. © 2024 Walter de Gruyter GmbH. All rights reserved.

关键词： Fog

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Optimizing City Transit: IoT and Gradient Boosting Algorithms for Accurate Bus Arrival Predictions 2

Optimizing City Transit: IoT and Gradient Boosting Algorithm...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and electronics, ICIITCEE 2024

作者： Shanthi, S. Maruthu Kannan, B. Giriprasad, S. Babuji, R. Sivakumar, S. Malathi, N. Care College of Engineering Department of Electronics and Communication Engineering Tamilnadu Tiruchirappalli India Sphoorthy Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350306415

Sustainable cities and people's well-being depend on efficient and reliable public transportation networks. To improve the reliability of bus arrival forecasts, we offer a new method that uses Internet of Things (IoT) data and advanced Gradient Boosting Machine learning algorithms to combine the two. Using real-time information from IoT sensors installed on buses and at strategic nodes along their routes, it constructs a comprehensive dataset that accounts for many variables contributing to bus transit delays, such as traffic, weather, and passenger load. Then, it creates superior prediction models than conventional approaches. It uses Gradient Boosting, an effective ensemble learning methodology. The findings will lead to a more dependable and efficient public transportation system by improving the accuracy of predicted bus arrival times. It helps in the continuous struggle over urban problems with mobility and sets the path for more data-driven, efficient improvement of public transportation. © 2024 IEEE.

关键词： Bus Arrival Predictions Public Transit Efficiency Public Transportation Real-time Data Smart Cities

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Deep Learning Models for Acoustic Monitoring of the Himalayan Monal and Other Elusive Bird 4

Deep Learning Models for Acoustic Monitoring of the Himalaya...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Nikshya, J. Ebens Raj, J. Relin Francis Krishnan, R. Santhana Rajkumar, G.Vinoth Settu, Imran Javeed Kavitha, S. Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India J. P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798331523923

Acoustic monitoring of rare and elusive bird species presents significant challenges due to the complexity of environmental noise and the variability in bird vocalizations. This study proposes a novel deep learning framework, BioAudio VisionNet (BAVNet), specifically designed for the acoustic identification and monitoring of the Himalayan Monal and other rare bird species. The performance of the proposed algorithm was evaluated against three established algorithms: Support Vector Machines with Acoustic Features (SVM-AF), Audio Spectrogram Transformer (ASF), and Deep Belief Networks (DBN). Simulation experiments were conducted using metrics such as accuracy versus the number of epochs, processing time versus accuracy, and spectrogram classification accuracy versus spectrogram resolution. The results demonstrated that BAVNet achieved a notable improvement in classification accuracy, computational efficiency, and adaptability to varying spectrogram resolutions when compared to existing methods. These findings highlight the potential of BAVNet for real-time biodiversity monitoring in challenging environments. Future work can explore the integration of additional contextual data, such as environmental factors, to further enhance system performance. © 2025 IEEE.

关键词： Birds

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Prediction of Insulin-Dependent T2DM in Patients Using Machine Learning Model 2

Prediction of Insulin-Dependent T2DM in Patients Using Machi...

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2nd International Conference on Intelligent Data communication Technologies and Internet of Things, IDCIoT 2024

作者： Gowthami, S. Venkata Siva Reddy, R. Ahmed, Mohammed Riyaz School of Electronics and Communication Engineering REVA University Bangalore560064 India HKBK College of Engineering Department of Computer Science and Engineering Bangalore560045 India

ISBN: (纸本)9798350327533

Diabetes, a widespread and enduring chronic ailment, exerts its impact on millions of people across the globe. Individuals with diabetes need regular insulin injections to sustain optimal blood sugar levels. Insulin is indispensable for persons with type 1 diabetes, although persons with type 2 diabetes may require insulin at a later stage. However, the current approaches for suggesting insulin dosage frequently prove inadequate, exhibiting either imprecision or complex procedures. Certain approaches require individuals to bear the responsibility, necessitating the manual calculation of insulin dosage using blood sugar levels and other factors - a complex and prone-to-error undertaking. Some individuals require closed-loop insulin administration devices, which are expensive and include wearing a continuous glucose monitoring (CGM) system. Recent estimates indicate that over 40% of patients who are suffering from (T2DM) in India and Gulf countries are utilizing insulin either on its own or in conjunction with Oral Antidiabetic Drugs (OADs) at any one moment. Lack of sufficient understanding regarding the use of insulin is expected to impact the acceptance, adherence, and effectiveness of therapy. This highlights the urgent requirement to examine the knowledge, and attitude, and suggest the usage of insulin to patients with both juvenile and type 2 diabetes. Insulin therapy is a crucial component of diabetes treatment;individuals with juvenile diabetes and the majority of those with type 2 diabetes will need insulin at some point. Proper injection technique is crucial for achieving glycemic control when using injectable medications such as human insulin, insulin analog, and glucagon-like peptide-l receptor agonists to manage diabetes. The proposed study focuses on determining the subset of individuals with diabetes who are taking both insulin and Oral Antidiabetic Drugs (OAD), and to assess the influence of insulin on glycemic control, ultimately resulting in improved

关键词： Insulin pens Machine learning algorithms Oral Antidiabetic drugs Precision medicine Type 2 Diabetes Mellitus

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Deep Fish: An Approach to Fish Species Identification Through Deep Learning Techniques 3rd

Deep Fish: An Approach to Fish Species Identification Throug...

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3rd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Reddy, Penumalli Jithya Malathi, M. Julaiha, AG. Noorul Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department Computer Science Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9789819739905

This analysis introduces a system for automated identification of fish species and classification based on deep learning. This system can provide valuable insights to marine biologists, with better understanding on fish species habitats. The developed system relies on deep learning neural networks (DNNs) to extract features from underwater images and classify fish into different species. Classifying underwater images of fish species is crucial for conducting fish surveys, maintaining ecological balance, monitoring populations, and conserving endangered species. Yet, it proves challenging due to optical obstacles like smattering and raptness in ocean water, leading to dark and low images. This research delves into the application of deep learning in diverse aquatic tasks, encompassing fish identification, classification, feeding decisions and behavior analysis, estimation of its size, and quality of water prediction. Using a sizable labeled dataset from Kaggle, the proposed system employs a hierarchical CNN classification approach, achieving notable recognition rates and accuracy. The study highlights the efficacy of deep learning in fish species classification, emphasizing its automatic feature extraction capability, and removing the necessity for labor-intensive manual work when engineering features. Furthermore, the advantage of deep learning models lies in their capacity to attain high accuracy with extensive datasets, exemplified by the successful training on the Kaggle dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Enhancement of Photovoltaic Dust Detection Using Deep Learning Technique

Enhancement of Photovoltaic Dust Detection Using Deep Learni...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Gracia, R. Lillian Priyadharsini, C. Caroline, B.Elizabeth Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India Ifet College Of Engineering Division Of Electronics And Communication Engineering Villupuram India

ISBN: (纸本)9798350371567

The dust collection on solar panels situated in solar parks is a common problem faced by PV system operators, particularly in arid and dusty regions. Dust deposition reduces the transparency of the panel surface, leading to decreased sunlight absorption and thus reduced energy conversion efficiency. Traditional methods for detecting dust accumulation involve manual inspection or periodic cleaning schedules, both of which are labor-intensive, time-consuming, and often inefficient. Recent developments in deep learning and machine vision have made autonomous dust detection techniquesfeasible. Numerous techniques have been employed to identify and measure dust on solar panels, such as machine learning models and image processing algorithms. In this paper, the dataset of clean and dusty images of the solar panels utilized wasapplied to four state of the art models and then the EfficientnetB0 model was proposed to detect the dust over the solar panels. On comparing the EfficientnetB0 model with the other state of the art models, EfficientnetB0 yielded an impressive accuracy rate of approximately 85.92%. Further research and development in this field hold the potential to revolutionize the maintenance and operation of solar power plants, contributing to a greener and more sustainable future. © 2024 IEEE.

关键词： Solar panels

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Machine Learning-Based Crop Recommendation System for Sustainable Agriculture 4

Machine Learning-Based Crop Recommendation System for Sustai...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sriharsha, Kantheti Chandan Joshi, Kotapuri Rahul Reddy, Vummadi Ravi Sankar Chowdary, Vadlapudi Kalyan Hanuman Hajarathaiah, Koduru Rajak, Shaik School of Computer Science and Engineering VIT-AP University Amaravati India Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Amaravati India

ISBN: (纸本)9798331523923

This research paper addresses the critical challenges in agricultural crop recommendation systems through machine learning approaches. Current systems face significant limitations including limited integration of real-time environmental data, inconsistent prediction accuracy across diverse geographical regions, difficulty in handling multiple conflicting parameters simultaneously, and lack of interpretability in recommendation decisions. Recent techniques like Random Forest, Support Vector Machines, and Deep Neural Networks have attempted to address these issues but with varying degrees of success. This paper proposes an enhanced crop recommendation system that leverages multiple machine learning algorithms to overcome these limitations. The system implements real-time soil and weather parameter integration, while providing interpretable recommendations to farmers. Our approach achieves 99.54% accuracy in crop recommendations, significantly improving upon existing systems. The proposed model serves as a comprehensive solution for sustainable agriculture, contributing to improved crop yield and farmer decision-making processes. The system's effectiveness is validated across different agricultural zones, demonstrating its practical applicability and scalability in diverse farming conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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A Modified Hexagonal Shaped Sub-6 GHz 5G Antenna in MIMO Configuration

A Modified Hexagonal Shaped Sub-6 GHz 5G Antenna in MIMO Con...

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2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Porwal, Rahul Garg, Rahul Kumar Raj, Nikhil Nair, M.V. Deepak Department of Electronics and Communication Engineering The LNM Institute of Information Technology Jaipur India Department of Computer Science and Engineering University of Engineering and Management Jaipur India

ISBN: (纸本)9798350379693

A line-fed Modified Hexagon shaped 2-port planar antenna is presented for 5G application. The MIMO antenna is designed through combining the techniques of a modifying the radiator and defected ground structure. In the MIMO configuration, two replicas of the MIMO element are arranged orthogonal to ensure the pattern diversity. A cross shaped structure is integrated with the ground to enhance interelement isolation. The size and operating bandwidth of the MIMO antenna are 28×56 mm2 and 2.54 GHz to 5.46 GHz, respectively. This design advantageously offers a wide -15 dB bandwidth from 3.3 GHz to 5.0 GHz with a percentage bandwidth of 40.96% and a peak realized gain of 3.1 dBi at 3.3 GHz. Maximum isolation value of 33.55 dB is achieved while it is never less than 16.85 dB for the frequency of interest. The envelope correlation coefficient (ECC) © 2024 IEEE.

关键词： 5G mobile communication systems

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