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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Venkateswarulu Naik, B. Unhelkar, Bhuvan Chakrabarti, Prasun Ghantasala, G.S. Pradeep Vidyullatha, Pellakuri Pyla, Meghana Department of Information Systems and Decision Sciences Muma College of Business University of South Florida South Florida United States Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (纸本)9798350383867

Pneumonia diagnosis using chest X-ray (CXR) images shows a decisive part in early detection and handling of the disease. Within this research, proposes a convolutional neural network (ConvNet) model for automated pneumonia diagnosis based on CXR images. Our model leverages the deep learning capabilities of ConvNets to analyze features in CXR images and categorize them as pneumonia-positive or pneumonia-negative. We conducted experimentations on a comprehensive dataset of CXR images, achieving auspicious outcomes in terms of efficiency and accuracy. The proposed ConvNet model demonstrates prospective for an accurate and appropriate pneumonia diagnosis, contributing to improved healthcare outcomes and resource optimization in medical settings. ConvNet model is implemented using tensorflow keras with preprocessing using Image data generator. The models are deployed like sequential, Conv2D with tuning parameters of Relu, dropout, max pool 2d and adam optimizer. For training the images with 150, 150 pixels, train sample as are 1000 and input shape is 3, epochs are 50, batch size is 20. Applications of the categorical cross-entropy loss function for multi-class division with Adam optimizer employed for parameter optimization. The Model trained for a stable amount of epochs through early based on validation loss toward prevent overfitting and learning rate, batch size, and dropout rate optimized through hyperparameter tuning. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimizing Railway Traffic Management Using Fuzzy Logic-Based Decision-Making Systems 2

Optimizing Railway Traffic Management Using Fuzzy Logic-Base...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Kumari, Nalli Vinaya Pradeep Ghantasala, G.S. Sharma R, Rajesh Ananthanagu, U. Vidyullatha, Pellakuri Pandiya, Dileep Kumar Jntuh Malla Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Zoominfo Technologies Principl Software Engineer Boston United States

ISBN: (纸本)9798350364699

Railway web page traffic manipulate is a complex and dynamic method that includes making real-time picks based mostly on various uncertain and difficult to understand factors, collectively with fluctuating passenger demand, diverse train schedules, and unpredictable weather conditions. Traditional decision-making strategies often battle to cope with the ones uncertainties, foremost to inefficiencies and potential safety risks. This paper proposes the software program of fuzzy properly judgment-based completely decision-making systems to optimize railway web page traffic manipulate. Fuzzy properly judgment, with its potential to model uncertainty and control difficult to understand data, offers a robust framework for boosting decision-making approaches in railway operations. By simulating various conditions and integrating real-time data, the proposed gadget can dynamically regulate train schedules, manipulate delays, and enhance everyday operational efficiency. The paper may present case studies demonstrating the effectiveness of fuzzy properly judgment in dealing with railway web page traffic, reducing delays, and improving passenger satisfaction. The findings advice that incorporating fuzzy properly judgment into railway web page traffic manipulates systems can purpose greater adaptive and resilient railway operations, ultimately contributing to greater steady and additional reliable rail networks. © 2024 IEEE.

关键词： Efficiency

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An Efficient Method For Skin Cancer Detection Using Convolutional Neural Network

An Efficient Method For Skin Cancer Detection Using Convolut...

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International Conference on Advances in Information technology (ICAIT)

作者： R Rajagopal Karthik Kumar G V Senbagavalli M P. Divya Sai Priya Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bangalore India Department of Computer Science & Information Technology Alliance College of Engineering and Design Alliance University Bangalore India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Skin cancer poses a significant health risk, often overlooked despite its prevalence. Manual detection by clinicians may sometimes be inadequate, leading to the necessity of automated systems utilizing deep learning. In this study, we propose a computerized solution employing Convolutional Neural Networks (CNNs) to detect skin diseases from imaging data. By leveraging the Kera's Sequential API, we design and train various neural network architectures, analyzing their performances in skin cancer detection. Our custom CNN model achieves an accuracy of approximately 80%. Subsequently, we explore the efficiency of transfer learning models such as VGG11 to improve accuracy and for comparative analysis. Notably, the vgg16 architecture achieves the highest accuracy of 86% making it a competitive solution for skin cancer detection.

关键词： Accuracy Transfer learning Neural networks computer architecture Manuals Skin Convolutional neural networks Lesions Information technology Skin cancer

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Area and Energy Efficient Method Using AI for Noise Cancellation in Ear Phones

Area and Energy Efficient Method Using AI for Noise Cancella...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Manikandan, R. Ramkumar, S. Prabhakaran, M. Department of Lean Operations and Systems Karnataka Bengaluru India Department of Computer Science Karnataka Bengaluru India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350300857

Adaptive filters are suitable for most of the Digital Signal Processing (DSP) applications such as channel equalization, noise cancellation, echo cancellation, channel estimation and system identification. Nowadays due to the advancement in semiconductor technology, the need for Active Noise Cancellation (ANC) headphones in compact devices is increased. The major idea behind this proposed work is to design an area and energy efficient novel adaptive filter suitable for in-ear headphones by combining Normalized Least Mean Square (NLMS) and Block LMS (BLMS). The proposed filter is designed and simulated using Xilinx ISE 13.2. The simulation results shows that the proposed design mitigates the unwanted noises in various frequency bands. © 2023 IEEE.

关键词： Adaptive filters

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Predicting Employee Under Stress Using Machine Learning

Predicting Employee Under Stress Using Machine Learning

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Thonduru Anjum Javeriya M Kabilan Golla Manoj Yarragonda Bhavya Sree Rongala Venkata Satya Jagadish Mahmood Hussain Mir Saswati Debnath B. M. Girish Bachelor of Technology in Computer Science and Engineering Alliance College of Engineering and Design Bengaluru Karnataka Bachelor of Technology in Information Technology Alliance College of Engineering and Design Bengaluru Karnataka Bachelor of Technology in Electrical and Electronic Engineering Alliance College of Engineering and Design Bengaluru Karnataka Department of Computer Science and Information Technology Alliance College of Engineering and Design Bengaluru Karnataka Department of Mechanical Engineering Alliance College of Engineering and Design Bengaluru Karnataka

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Because of COVID-19, businesses have had to change how they operate, like having people work from home or having fewer employees in the office. This “new normal” is sticking around for a while. Workers have had to adjust to these changes, which can make them feel tired and Pressure. Stress happens when people feel overwhelmed and uncertain. There are lots of things that can cause stress for workers, like getting paid less, having too much work to do, feeling lonely while working from home, working longer hours, not having a good place to work, not feeling like part of a team, feeling like they can’t do their job well, and worrying about getting sick. If bosses can recognize when workers are starting to feel stressed, they can help fix the problem, which can make things better for both the worker and the company.

关键词： COVID-19 Machine learning Companies Signal processing Stress Business

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An Enhancement of Object Detection Using YOLO V8 and Mobile Net in Challenging Conditions

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SN computer Science 2025年第4期6卷 1-20页

作者： Pasupuleti, Shailaja Ramalakshmi, K. Gunasekaran, Hemalatha Arokiaraj, Rex Macedo Debnath, Saswati Jebaseeli, T. Jemima Department of Computer Science and Engineering Alliance University Karnataka Anekal Bangalore India AU-Centre of Excellence Department of Computer Vision Alliance University Karnataka Anekal Bangalore India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Alliance University Karnataka Bangalore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

Autonomous drones and deep learning neural networks are becoming popular tools to revolutionize aircraft operations by partially automating visual inspection processes in aircraft maintenance. The research aims to provide a trustworthy method for locating and classifying surface flaws on aircraft, such as dents, fractures, corrosion, and a few other anomalies. To assess the model performance with and without a lightweight network integration, the implementation merges YOLOv8 and Mobile Net architectures, using dropout regularization and data augmentation approaches. To guarantee the model's performance in low-light situations, the dataset has been carefully selected to contain images replicating such settings. Annotation, environment setup, model training, and online dataset scraping are all covered by the thorough technique. A web-scraped dataset is used to train and validate two popular semantic segmentation algorithms, YOLOv8 and Mobile Net, which show enough performance for segmenting and grading aircraft surface problems in practical settings. The results show that precision and recall values are improved using a hybrid technique that ensembles trained Mobile Net and YOLOv8 models. Using COCO metrics, in the proposed approach an assessment of Ensemble WBF, Mobile Net-YOLO, and YOLOv8 outcomes on test and validation sets, guaranteed reliable object recognition and classification abilities. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aircraft Classification Mobile-Net Object detection Weighted Box Fusion YOLOv8

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A Novel Optimized AI Based Model for Traffic Prediction in VANET 5

A Novel Optimized AI Based Model for Traffic Prediction in V...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Pradeep, R. Revathi, K. Thomas, Aby K Srikanth, R. Sns College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Sns College of Engineering Department of Information Technology Coimbatore India Alliance College of Engineering and Design Alliance University Department of Electronics and Communication Engineering Bangalore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350321425

Research into a novel type of wireless network, the vehicle ad hoc network (VANET), has gained an increasing research interest in recent years. By exchanging data about things like vehicle velocity, location, and direction, VANET facilitates interaction between moving vehicles and fixed infrastructure. It's best to take an alternate route, if possible, when there's a high probability that a large number of cars will use the same one to get from point A to point B. If vehicles could precisely foresee traffic congestion, they could potentially evade it. Hence, suggested using DRL in VANET to improve traffic congestion forecasting. In addition, this deep reinforcement learning model examines and compares various neural networks, such as the CNN, MLP, and LSTM, to determine which is best with the results. The proposed strategy is evaluated based on the ATTD and the AWTD of vehicles, which serve as metrics to assess its effectiveness. Through simulation analysis, it is evident that the suggested approach enhances the average travel time delay and average waiting time delay across multiple iterations, considering the environmental factors as inputs. The simulation results demonstrate that compared to existing algorithms used for network traffic prediction, the deep reinforcement learning model provides superior performance in terms of both execution time and prediction errors. © 2023 IEEE.

关键词： Traffic congestion

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AI Based Variable Step Size Block Least Mean Square Filter for Noise Cancellation System

AI Based Variable Step Size Block Least Mean Square Filter f...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Manikandan, R. Kumar, K. Sathesh Ramkumar, S. Department of Lean Operations and Systems Karnataka Bengaluru India Alliance University Computer Science and Engineering Alliance College of Engineering and Design Karnataka Bengaluru India Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350300857

Most of the Active Noise Cancellation (ANC) systems working properly in low-frequency noises only. To make it suitable for isolating high-frequency noise, it needs an additional circuit which consumes more energy. This problem is mitigated in this study by designing a Variable Step size Block Least Mean Square (VSBLMS) filter which is suitable for an effective noise cancellation system. VSBLMS filter is designed with RCA to make a design area efficient and it is designed with a novel adder to achieve high speed as well as less energy consumption. The proposed filter is designed and simulated using Xilinx ISE 13.2. The simulation results shows that the proposed VSBLMS filter design mitigates the unwanted noises in various frequency bands. The proposed VSBLMS reduces the energy consumption by 9.32%, 27.63%, 13.53%, 11.80%, 10.71 %, 13.14% and 9.26% when compared with state of the art methods. © 2023 IEEE.

关键词： Adaptive filters

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A novel K-nearest neighbor classifier for lung cancer disease diagnosis

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Neural Computing and Applications 2024年第35期36卷 22403-22416页

作者： Sachdeva, Ravi Kumar Bathla, Priyanka Rani, Pooja Lamba, Rohit Ghantasala, G. S. Pradeep Nassar, Ibrahim F. Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Chandigarh University Punjab Gharuan Mohali India Haryana Mullana Ambala India Haryana Mullana Ambala India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Faculty of Specific Education Ain Shams University 365 Ramsis Street Abassia Cairo Egypt

One of the world's deadliest diseases is lung cancer. Based on a few features, machine learning techniques can help in the diagnosis of lung cancer. The performance of several classifiers: support vector machine (SVM), logistic regression (LR), Naïve Bayes (NB), random forest (RF), and K-nearest neighbor (KNN), was evaluated by the authors using the dataset available on Kaggle to create a systematic approach for the diagnosis of lung cancer disease based on readily observable signs and historical medical data without the requirement of CT scan images. The authors have proposed a novel approach for classification called Pearson correlation weighted KNN (PCWKNN), which is a modified version of KNN and uses Pearson correlation coefficient values to determine weights in a weighted KNN. The performance of the classifiers was evaluated using the hold-out validation method. SVM, LR, and RF were 96.77% accurate. NB obtained 95.16% accuracy. KNN achieved 91.93% accuracy. PCWKNN outperformed the employed classifiers and obtained an accuracy of 98.39%. Addressing the imperative for improved model generalization, the researchers utilized PCWKNN on an alternative, more extensive lung cancer dataset and subsequently broadened its application to diverse diseases, including the brain stroke dataset. The encouraging outcomes underscore PCWKNN's resilience and adaptability, suggesting its viability for real-world implementation. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Lung cancer

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Multilingual Exploration of the Kambaramayana: Analyzing Tamil Translations in Telugu, Malayalam, and Kannada Using mBERT and DistilBERT 1

Multilingual Exploration of the Kambaramayana: Analyzing Tam...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Swathi, K. Kamalam, G.K. Hemalatha, S. Jothimani, K. Dharshini, N. Aadhishri, A. Kongu Engineering College Department of Computer Science and Design Tamil Nadu India Kongu Engineering College Department of Information Technology Tamil Nadu India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356816

An essential and basic aspect of our existence are our emotions. Our behaviors and words reflect our feelings, even though they are not instantly obvious. This research looks at the multilingual features of the Kambaramayana, with a concentration on Tamil translates into Telugu, Malayalam, and Kannada. We study how linguistic and cultural subtleties show across translations using a varied dataset. We do a comparison study using sophisticated multilingual language models, specifically mBERT and DistilBERT, to identify semantic variances and similarities. We hope to uncover the subtle interplay among language and culture in literary text transmission by investigating vocabulary, grammar, and cultural allusions. Our findings not only improve the accuracy of cross-lingual text analysis, but also highlight the Ramayana's lasting relevance among South Indian language communities. The experiments clearly reveal that mBERT outperforms DistilBERT in emotion analysis tasks between languages. © 2024 IEEE.

关键词： Measurement Vocabulary Translation Accuracy Text analysis Semantics Robustness Multilingual Grammar Cultural differences

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