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

作者： Laxmikant, Kumar Arthi, A. Vinodhini, V. Natarajan, B. Bhuvaneswari, R. Selvam, Prabu Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Karpagam College of Engineering Computer Science and Technology Coimbatore India University College of Engineering Department of Electronics and Communication Engineering Ariyalur India VIT University School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350317558

Lung and colon cancers are significant health issues across the world, prompting the need for inventive methods of diagnosis. This study takes the lead in introducing advanced Deep ConvNets (CNNs) to enhance the accuracy of early detection. The model is trained on extensive datasets, resulting in impressive outcomes. During training, it achieves an accuracy of 92.54% and a loss value of 0.0161. These results are mirrored in testing, where the accuracy reaches 96.59% and the loss value is 0.0441. Beyond just numerical achievements, this research revolutionizes cancer diagnostics by providing a sophisticated tool for personalized and early detection. This study surpasses the mere influence on individuals and extends its reach to bring hope for society and the economy by tackling the pervasive weight of cancer. It distinguishes itself in the realm of advanced medical investigation, affirming the efficacy of the model and inspiring additional inquiry, encompassing the utilization of more extensive sets of data and implementation in diverse medical domains. © 2024 IEEE.

关键词： Colon Cancer Convolutional Neural Network Lung Cancer

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Efficient Mobility in Next Generation Heterogeneous Wireless Networks Based on Enhanced Vertical Handoff Approach

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第6期19卷 128-139页

作者： Kaliappan, Vishnu Kumar Padmapriya, T. Manikanthan, S.V. Ilavarasi, S. Suresh Kumar, B. Department of Computer Science and Engineering KPR Institute of Engineering and Technology Arasur Tamil Nadu Coimbatore India Melange Publications Puducherry India Melange Academic Research Associates Puducherry India Tamil Nadu Tiruchengode India Department of Electronics and Communication Engineering The Kavery Engineering College Mecheri Tamil Nadu Salem India

The progression of mobile technologies from first generation (1G) to fifth generation (5G) networks has brought about noteworthy advancement. The present network environment is characterized by the coexistence of various mobile networks and technologies, leading to overlapping or non-overlapping coverage areas, creating a next-generation heterogeneous landscape. Handoffs within the same network (i.e., GSM to GSM or vice versa) are known as horizontal or homogeneous handoffs. Conversely, when handoffs entail a transition to a different network type, such as moving from GSM to CDMA or vice versa, they are categorized as vertical or heterogeneous handoffs. Multiple link triggers, such as link down (LD), link going down (LGD), link detected, and link up, are employed to initiate and execute vertical handoff (VHO) when needed. We have determined the thresholds for vehicular speed and link triggers through extensive simulations, explicitly focusing on high-demand traffic types like video. This paper proposed an enhanced VHO approach based on IEEE 802.21, commonly called media independent handover (MIH), to improve Quality of Service (QoS) parameters, specifically mitigating packet loss (PL), and handoff delay and enhancing the user experience. © 2025 by the authors of this article.

关键词： 5G mobile communication systems

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Optimization of Traffic System using Adaptive Response First Come First Serve

Optimization of Traffic System using Adaptive Response First...

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2024 International Conference on Signal Processing, Computation, electronics, Power and Telecommunication, IConSCEPT 2024

作者： Amutha, S. Singh, Dhruv Singh, Shashank Shekhar Jhandial, Anant Gupta Krishna, Kunal School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics and Communication Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331540685

In this period of urbanization and adding vehicular traffic, the optimization of business operation systems is consummate to insure both the effectiveness of transportation networks and the safety of commuters. This exploration paper explores the operation of deep literacy ways to revise the field of business operation. By employing the power of deep learning, sensors and timers, our study aims to develop innovative results that ameliorate the delicacy, rigidity, and real- time responsiveness of business control systems. The primary focus of this exploration is to design a comprehensive deep literacy-grounded business operation system that incorporates colorful data sources. By assaying vast quantities of miscellaneous data, the system will acclimatize to dynamic business conditions and offer real- time perceptivity for effective business inflow operation, accident discovery, and traffic relief. The exploration will also explore the integration of machine literacy algorithms for intelligent business signal control, prioritizing green light allocation grounded on real- time demand and business viscosity. The proposed deep literacy result aims to significantly reduce business traffic, minimize trip times, and enhance overall road safety by furnishing optimized business operation strategies. The exploration will concentrate on the practical perpetration of the system in real- world business scripts, with the eventuality to gauge and acclimatize to colorful civic surroundings. This exploration bid seeks to lay the foundation for a future in which business systems seamlessly acclimatize to evolving business patterns, creating safer, more effective transportation networks, and contributing to a sustainable and intelligent civic ecosystem. The proposed dynamic signal control logic is 83.33 % more efficient in utilizing green time compared to the traditional fixed-time system. Estimated reduction in wasted green time is 25%. © 2024 IEEE.

关键词： Traffic signals

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A Comparative Analysis of ISLRS Using CNN and ViT 4th

A Comparative Analysis of ISLRS Using CNN and ViT

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4th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2023

作者： Renjith, S. Manazhy, Rashmi Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819965854

Indian Sign Language Recognition System (ISLRS) aims at recognizing and interpreting the hand gestures and movements in Indian Sign Language (ISL), in order to facilitate smooth communication between the hearing-impaired individuals and the normal population. This research aims at comparing ISLR System using a custom convolutional neural network (CNN) architecture as well as Vision Transformer (ViT). From the ISL alphabet dataset consisting of 36 classes, 26 classes corresponding to the English alphabets are considered in this analysis. The analysis showed that for the dataset, ViT outperforms CNN in terms of performance metrics considered. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Enhanced Lung Cancer Detection Using Advanced ROI Extraction and Deep ANN-CNN Hybrid Classifiers 5

Enhanced Lung Cancer Detection Using Advanced ROI Extraction...

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5th International Conference on Sustainable communication Networks and Application, ICSCNA 2024

作者： Anto Sagaya Priscilla, A. Balamanigandan, R. Department of Electronics and Communication Engineering Tamil Nadu Chennai602105 India Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331530013

Lung Cancer is a leading cause of death and can be cured if diagnosed early. For the automated detection, the system employing the image processing, artificial neural networks and convulational neural networks (ANNs & CNNs) have been developed. First the images are preprocessed using filter techniques such as noise elimination, image sharpening and segmentation which zooms into region of interests (ROI) where tumours are expected. Following preprocessing these segments of images are further analyzed in layers by CNNs to recognize significant features in layers and ANNs are used to classify the tumors as benign or malignant. This makes the approach more accurate, sensitive and specific compared to the traditional approaches of detection. These systems demonstrate higher performance using more extensive lung cancer data sets and contribute to increasing the availability of accurate early diagnosis. © 2024 IEEE.

关键词： Lung cancer

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Modulated advancements in semiconductor-based nanomaterials for environmental solutions

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Nanotechnology for Environmental engineering 2024年第4期9卷 525-537页

作者： Tirmare, Aarti Hemant Gowda V, Dankan Dhabarde, Rupali J Tirmare, Hemant Appa Kale, Satish Bapuso Suryawanshi, Varsha Amol Kumar N, Anil Department of Electronics and Telecommunications Engineering Bharati Vidyapeeth’s college of Engineering Maharashtra Kolhapur416013 India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka560064 India Department of Computer Science and Technology Department of Technology Shivaji University Kolhapur Maharashtra Kolhapur416008 India Department of Computer Science and Technology Department of Technology Shivaji University Kolhapur Maharashtra Kolhapur416004 India Department of Mechanical Engineering Department of Technology Shivaji University Kolhapur Maharashtra Kolhapur416004 India Maharashtra Kolhapur India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India

This research explores significant advancements in semiconductor-based nanomaterials and their crucial applications in addressing environmental challenges. Highlighted for their expansive surface area, chemical stability, and adaptable electrical and optical properties, these nanomaterials present innovative solutions for pressing environmental issues. The paper details novel synthesis methods enhancing these materials’ effectiveness in environmental applications, including pollution remediation, sustainable energy production, and the development of environmental monitoring tools. Key findings include the discovery of advanced techniques for fabricating nanoscale semiconductor components, improving the efficiency of photocatalytic degradation of pollutants, energy conversion, and sensor sensitivity. Additionally, this study investigates the integration of semiconductor nanomaterials with biocompatible polymers and metals, aiming to enhance their operational stability and longevity in environmental applications. The outcomes contribute to the advancement of nanotechnology in environmental conservation, offering insights into the development of eco-friendly materials and innovative strategies for environmental protection. This work underscores the potential of semiconductor-based nanomaterials to revolutionize the field of environmental solutions, presenting a promising avenue for future research and application in sustainable technologies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Nanostructured materials

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Role of Machine Learning in the Prediction of Thyroid Disease

Role of Machine Learning in the Prediction of Thyroid Diseas...

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2024 International Conference on Electrical, electronics and Computing Technologies, ICEECT 2024

作者： Kumari, Priyanka Kaur, Baljinder Rawat, Anil Kumar Rakhra, Manik Lovely Professional University School of Computer Science and Engineering Phagwara India Lovely Professional University School of Electronics & Communication Engineering Phagwara India

ISBN: (纸本)9798350378092

Numerous thyroid disorders impact people throughout the world. Numerous conditions can affect the thyroid gland, such as hyperthyroidism, hypothyroidism, and thyroid cancer. Thyroid illness patients may have excruciating symptoms. Effective classification and machine learning are highly useful in making timely thyroid illness diagnoses. This categorization will affect the patient's timely treatment. An endocrine ailment or condition known as hypothyroidism occurs when the thyroid gland does not generate enough thyroid hormone. Experts in the present system suspect hypothyroidism based on symptoms;it is a scientific investigation. The diagnosis of hypothyroidism can only come from a series of diagnostic tests. To ascertain their thyroid health, some groups need more precise tests. In India, 42 million individuals are estimated to have thyroid illness. The incidence of thyroid disorders has increased in recent years. Hyperactivity and hypothermia are two of the less common symptoms caused by thyroid gland disorders. Many people are diagnosed with thyroid diseases including hyperthyroidism and hypothyroidism each year. A shortage of T3 and T4 hormones can result in both hyperthyroidism and hypothyroidism. A strategic forecast of thyroid illness is required to ensure that the patient receives appropriate care at the appropriate time, save needless deaths, and minimize medical costs. Using state-of-the-art technology to enhance medical processes for the detection and prevention of thyroid issues is extremely desirable. The information from the UCI repository, Kaggle, and GitHub is used to categorize thyroid illnesses. similar to KNN, Logistic Regression, and Naïve Bayes, these algorithms also yielded results with up to 96% accuracy. © 2024 IEEE.

关键词： Logistic regression

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A Comparative Analysis of Deep Learning based Classifiers for the Identification of Maize Rot Diseases 1

A Comparative Analysis of Deep Learning based Classifiers fo...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Dash, Arabinda Sethy, Prabira Kumar Sambalpur University Department of Computer Science and Engineering Odisha Sambalpur India Sambalpur University Department of Electronics and Communication Engineering Odisha Sambalpur India

ISBN: (纸本)9798350361780

One of the main factors causing production deprivation in specific provinces of India, is infections with maize stalk rot, however large-scale identification of maize illnesses is challenging due to limitations in standard image-recognition technology and a lack of human competency. Thankfully, advances in computer intelligence technology allow for automatic disease identification, which can assist farmers in rapid and dependable disease detection. In the current analysis, we compared the success rate of multiple deep learning-based classifiers for identifying two of the dominant varieties of maize stem diseases in India. In order to do this study, we collected 1364 photos of maize, which fall into three different categories: healthy, Charcoal rot, and Fusarium stalk rot. Furthermore, all these images undergo analysis using 13 distinct pre-trained CNN models including AlexNet, Densenet-201, Googlenet, Inceptionresnet-V2, Inception-V3, Mobilenet-V2, VGG 16 & 19, Resnet 18, 50 & 101, XceptionNet, and ShuffleNet for both training and testing purposes. The performance metrics, namely accuracy and False Positive Rate, are meticulously recorded for each CNN model. Subsequently, the efficacy of every individual model is assessed via multilabel analytical investigation conducted using IBM SPSS tool, aiming to identify the utmost image categorization model among the aforementioned models. © 2024 IEEE.

关键词： Deep learning

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Design of Approximate Multiplier using Highly Compressed 5_2 Counter 6

Design of Approximate Multiplier using Highly Compressed 5_2...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Nelmin, Bathlin N. Shaji, R.S. Hariprasad, S. Shettygari, Sudeshna Vasan, J.K. Vijayan, V. Department of Electronics and Communication Engineering University College of Engineering Nagercoil India Department of Computer Science and Engineering St. Xavier’s Catholic College of Engineering Nagercoil India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798331522667

Approximate computing effectively impacts low-power VLSI circuits by reducing circuit overhead. It introduces minimal error in the output, which does not significantly affect the application. This paper proposes an approximate multiplier constructed using 5_3 and 5_2 counters. The proposed 5_2 counter delivers two outputs instead of three, thereby achieving 60% compression. The overhead of the 5_2 counter circuit is reduced, achieving a power delay product (PDP) improvement of 18%. This reduction comes with an addition of an 6% error rate. The newly designed multiplier has less delay, where the Mean error distance (MED) is kept as low as possible. The complexity of the 5_2 counter is also reduced since the counter uses only a full adder and three gates. This is achieved by applying various error optimization mechanisms. The error parameters, (i) Error Distance (ED), and (ii) Mean Error Distance (MED) are analyzed and compared with existing works. The proposed multipliers are involved in image contrast application with better Peak Signal to Noise Ratio (PSNR) value. © 2025 IEEE.

关键词： Delay circuits

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Detection of Plant Diseases and Recommendation of Plant Cycle using Machine Learning 4

Detection of Plant Diseases and Recommendation of Plant Cycl...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Divya, B. Alivn Ancy, A. Vivekanandan, G. Swapna, A. Kaniha, P. Indhu Priyadharshini, S. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Chennai Institute of Technology Dept of Electronics and Communication Engineering India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Plant diseases create significant challenges to productivity in agriculture, food security, and ecological stability. Pathogens and environmental variables interact to cause plant diseases. Pathogens include various microorganisms like bacteria, fungi, and viruses, each with unique modes of spread. Environmental conditions such as temperature, humidity, and soil quality impact disease development and severity. The impacts of plant diseases are far-reaching. It affects crop yields and quality. Outbreaks of plant diseases can lead to economic losses, and food shortages which ultimately lead to hunger and poverty. Effective management of plant diseases requires an integrated approach that combines detection, preventive measures, cultural practices, and chemical interventions. The solution has two modules. The first module focuses on detection of plant diseases and its management. The second module focuses on recommendations of the plant cycle. Our proposed system utilizes CNN algorithms trained on a large amount of datasets. Using image analysis the features are extracted and patterns are recognized. Therefore, the system can quickly identify the disorders.. The system also includes suggestions for preventive measures. The system integrates modules for recommending optimal plant cycles to the farmers and gardeners. It generates personalized recommendations for planting schedules and crop varieties. This system increases crop yields and decreases the risk of disease outbreaks. Our solution offers a holistic approach to plant health monitoring and management. © 2024 IEEE.

关键词： Productivity Plant diseases Pathogens Schedules Soil Feature extraction Stability analysis Pattern recognition Crop yield Thermal stability

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