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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： V. Nivedita Kamalraj Subramaniam M. Ramya B D Parameshachari Department of Computer Science and Engineering SRM Institute Of Science And Technology Trichy India Department of Biomedical Engineering karpagam academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Excel Engineering College Namakkal India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

People may acquire skin illnesses as a consequence of hereditary factors, the natural ageing process, hormones, allergic responses, exposure to the sun or hazardous chemicals, and other environmental variables. Skin disorders are among the most prevalent and pervasive diseases in the world. The diagnosis is exceedingly challenging despite the fact that this condition is rather common since there are slight changes in skin tone, colour, and the presence of hair. When it is in its earlier phases, individuals sometimes fail to recognise the impacts of skin diseases. At the moment, dermatologists diagnose skin conditions by performing a sample, which is subsequently analysed, and then manually give drugs to patients. In this research, we offer a technique for the diagnosis of skin diseases that is based on image processing, Python, and the Yolov3 tool. Our goal is to eliminate the need for physical examination and to deliver reliable findings in a fast way. An picture of the portion of the patient's skin that is infected may be sent as an input to the system by the patient. On this picture, several image processing methods are used, and feature values are retrieved and located, in addition to the illness that is under investigation. The suggested approach is particularly helpful in geographical locations with a limited number of dermatologists. Acne, melanoma, blisters, and cold sores are the four forms of skin diseases that are discussed in this article, along with a technique for diagnosing each of them. The fundamental objective of this approach is to attain the greatest degree of accuracy in the prediction of skin diseases as is possibly conceivable.

关键词： Software algorithms Melanoma User interfaces Skin System software Reliability Sun

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Human Activity Classification using G-XGB

Human Activity Classification using G-XGB

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Data science and Network Security (ICDSNS), International Conference on

作者： Aman Kataria Vikram Puri Piyush Kumar Pareek Sita Rani Amity Institute of Defence Technology Amity University Noida India Center of Visualization and Simulation Duy Tan University Da Nang Vietnam Dept. of Artificial Intelligence and Machine Learning and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India

Wearable body sensors have recently gained popularity because they monitor user behaviors and vital indicators. These sensors are designed to monitor various physiological factors, such as motion, temperature, and heart rate, among others. Making sense of the data gathered by these sensors requires separating the dependent and independent features. The dependent parameters are those that body-worn sensors can read in this scenario. Examples of such elements include blood oxygen levels, heart rate, temperature, mobility, and airflow. These sensors capture data about a person's activity and health. The independent parameters in wearable body sensors are related to the dependent parameters, whereas the dependent parameters are those that the sensors can measure directly. Such characteristics include age, sex, weight, and environmental influences. For example, humidity and airflow might impair a temperature sensor's accuracy. The user's age or level of physical fitness may also influence the accuracy of a heart rate monitor. Activity monitors are used in this study to capture the actions of various people. Static and dynamic properties are derived from the activity monitor recording. The performance evaluations of the eight distinct machine-learning models that were created using the retrieved features are discussed in this paper. The Grid Search method is used to enhance the performance of the XGB model.

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Early Cervical Cancer Diagnostics Detection Based on RMSE and Extreme Learning Machine

Early Cervical Cancer Diagnostics Detection Based on RMSE an...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Aditya Kumar Singh Pundir Ullal Akshatha Nayak D. Gayathri Sucharitha. M Bhavana Jamalpur Ravi Rastogi Department of Electronics and Communication Engineering Arya College of Engineering and Information Technology Jaipur Rajasthan India Department of Information Science and Engineering Nitte Meenakshi institute of technology Bangalore Karnataka India Department of ECE PSNA College of Engineering and Technology Dindigul Tamilnadu India School of Electronics Engineering VIT-AP University Amaravati Andhra Pradesh India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Electronics Department NIELIT Gorakhpur Uttar Pradesh India

Among Indian women, cervical cancer continues to be the most prevalent form of the disease. The death and incidence rates from cervical cancer are dramatically higher in India than in any other country. Cervical cancer is unique among malignancies in that it can be prevented through screening by detecting and treating precancerous lesions at any point in the disease's lengthy natural history. Women can be screened for cervical precancers and malignancies using a number of different procedures, both older and more cutting-edge technologies. Given the limitations of any given screening tool, selecting the appropriate tool for a given situation is essential. Cervical precancers can be treated in a number of ways; the one chosen will rely on a number of factors, including the patient's preferences, the available budget, the quality of the biopsy specimens, etc. Cervical cancer screening guidelines for Indian women are examined. The proposed method consists of three main stages: preprocessing, feature extraction, and model training. In the preprocessing phase, image processing techniques like HE and sharpening and smoothing are applied. The Fourier Transform is used for feature extraction in GLCM. The RMSE-ELM technique is utilized for model evaluation. Better results are achieved with the proposed technique than with CNN or ELM.

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Spatial Attention Mechanism Integrated Ensemble Machine Learning for Real Time Prediction and Classification in Precision Agriculture

Spatial Attention Mechanism Integrated Ensemble Machine Lear...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Myasar Mundher Adnan Bura Vijay Kumar Madhura G K G. Mohammed Abhijeet Das Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq School of Computer Science and Artificial Intelligence SR University Warangal India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Ahl Al Bayt University Karbala Iraq Department of Civil Engineering C.V. Raman Global University (CGU) Bhubaneswar India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Generating spatial and temporal features for real time prediction and classification in precision agriculture is challenging due to inaccurate estimation of features from the remote sensing images. Using Geographical Information System (GIS) and systematic data regarding spatial and temporal data for monitoring and management of agricultural activities. The proposed research integrated GIS and used satellite image processing with Ensemble Machine Learning (EML) algorithms. Initially, the remote sensing data is pre-processed with normalization technique to remove noise. The pre-processed data is given as input to the Improved Speeded Up Robust Features (ISURF) for fast computation and identification of relevant features. The EML algorithms such as Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) are used as a prediction and classification models considering the ISURF features as input. Additionally, the Spatial Attention Mechanism (SAM) is integrated with the EML algorithm to particularly focus on the spatial features of the remote sensing data. The experimental results showed that the proposed SAM-EML achieved precise prediction and classification with 98.30% accuracy, and 97.19% precision when compared to the existing methods Random Forest Regression (RFR) and RF-SVM.

关键词： Support vector machines Machine learning algorithms Attention mechanisms Computational modeling Artificial neural networks Prediction algorithms Classification algorithms Satellite images Remote sensing Geographic information systems

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Brain Tumour Detection Using VGG-Based Feature Extraction With Modified DarkNet-53 Model

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International journal of biomedical imaging 2025年第1期2025卷 5535505页

作者： S Trisheela Roshan Fernandes Anisha P Rodrigues S Supreeth B J Ambika Piyush Kumar Pareek Rakesh Kumar Godi G Shruthi Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India. Department of Cyber Security NMAM Institute of Technology NITTE (Deemed to be University) Nitte India. Department of Computer Science and Engineering NMAM Institute of Technology NITTE (Deemed to be University) Nitte India. School of Computer Science and Engineering REVA University Bengaluru Karnataka India. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India. Department of AI&ML Nitte Meenakshi Institute of Technology Bengaluru India. Department of Computer Science Central University of Karnataka Kalaburagi India.

The objective of AI research and development is to create intelligent systems capable of performing tasks and reasoning like humans. Artificial intelligence extends beyond pattern recognition, planning, and problem-solving, particularly in the realm of machine learning, where deep learning frameworks play a pivotal role. This study focuses on enhancing brain tumour detection in MRI scans using deep learning techniques. Malignant brain tumours result from abnormal cell growth, leading to severe neurological complications and high mortality rates. Early diagnosis is essential for effective treatment, and our research aims to improve detection accuracy through advanced AI methodologies. We propose a modified DarkNet-53 architecture, optimized with invasive weed optimization (IWO), to extract critical features from preprocessed MRI images. The model's presentation is assessed using accuracy, recall, loss, and AUC, achieving a 95% success rate on a dataset of 3264 MRI scans. The results demonstrate that our approach surpasses existing methods in accurately identifying a wide range of brain tumours at an early stage, contributing to improved diagnostic precision and patient outcomes.

关键词： VGGNet brain tumour deep learning invasive weed optimization magnetic resonance imaging modified DarkNet-53

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DLT Based Smart Medical Ecosystem

DLT Based Smart Medical Ecosystem

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： Vikram Puri Aman Kataria Sita Rani Piyush Kumar Pareek Center of Visualization and Simulation Duy Tan University Da Nang Vietnam Amity Institute of Defence Technology Amity University Noida Uttar Pradesh India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India Dept. of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

The healthcare system has become more reliant on the data collection capabilities of fast evolving IoT devices. Patient healthcare records (PHR) now contain additional information as a result. However, securing identities and guaranteeing data security have emerged as critical challenges in the healthcare system. When it comes to regulating PHR, many of the centralized and decentralized methods now in use struggle difficulties such as single point of failure, high transaction fees, slow throughput, and excessive latency. To address these concerns, a distributed ledger technology (DLT)-based framework named IOTA rather than a blockchain-based platform such as Ethereum or Polygon is proposed. The proposed framework considered the example of a hospital with several patients on the same and various floors. Furthermore, the suggested approach employs a real-time experimental setup for calculating the time and power consumption of the devices.

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Deep Learning and Sentiment Analysis Improve E-commerce Sales Prediction

Deep Learning and Sentiment Analysis Improve E-commerce Sale...

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Data science and Network Security (ICDSNS), International Conference on

作者： Geetha K Swarnalatha K. Ananda Babu. J Poranki V L R N Sai Sudha Ramachandra A C Dept. of CSE GST GITAM University Bengaluru Campus Bengaluru India Dept of AI&DS MIT Thandavapura Mysore India Department of Information Science and Engineering Malnad College of Engineering Hassan India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The dataset labelled “Amazon and Yelp Reviews“ is a useful resource for sentiment analysis and opinion mining. It has roughly 1.5 million reviews from Amazon and Yelp for a wide range of products and services, as well as the relevant information for each review, which includes product ratings, review dates, and review titles. The dataset's size and diversity make it a good choice for training large-scale deep learning models to handle sentiment analysis tasks. The recommended approach for undertaking sentiment analysis using the “Amazon and Yelp Reviews“ dataset includes three steps: collecting data on a daily basis, preparing user comments, and applying a BiLSTM model for sentiment analysis. Assessing consumer sentiment entails evaluating comments, goods, and businesses to develop predictions about how people feel about various products and services. Some of the measures used in the model evaluation process include accuracy, precision, recall, and F1-score. The proposed method achieves an average accuracy of 87.3%, which is comparable to existing deep learning methods such as character-level convolutional neural networks (CNNs)- LSTM and deep transfer learning. The model's errors may be evaluated to discover where it can be improved, and additional assessment metrics such as AUC- ROC, AUC-PR, G-mean, and MCC can be used to determine how well the model is doing. The dataset and methods presented have the potential to be useful for a wide range of applications, including customer feedback analysis, product and service development, and online reputation management.

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A Personalized Music Therapy: Emotion-Based Treatment Through User-Centric Emotion Detection

A Personalized Music Therapy: Emotion-Based Treatment Throug...

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： C P Vijay K Anitha B D Parameshachari Rajeshwari Kisan Kiran Puttegowda D S Sunil Kumar Department of Computer Science Engineering (AI&ML) Vidyavardhaka College of Engineering Mysuru India Department of ECE GSSS Institute of Engineering & Technology for Women Mysuru Karnataka India Department of ECE Nitte Meenakshi Institute of Technology Bengaluru Karnataka India Department of CSE S. G. Balekundri Institute of Technology Belagavi Karnataka India Department of ECE Vidyavardhaka College of Engineering Mysuru Karnataka India Research Scientist Anatta innovations BGS B-School Bengaluru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Music serves as a tool for regulating, enhancing, and alleviating unwanted emotional states such as stress, fatigue, or anxiety in daily life. Despite the proliferation of multimedia content and the development of intuitive music players with diverse options, users still find themselves manually sifting through song lists to find ones that match their mood. This report addresses mental health issues by proposing solutions to alleviate stress and anxiety. Additionally, it seeks to streamline the process of finding mood-appropriate music by introducing a highly accurate CNN model for facial emotion recognition. Utilizing a webcam, facial expressions are analyzed to determine emotional states. The CNN classifier, a powerful deep learning model, is utilized to train and test the system for facial emotion recognition using the FER2013 dataset. This dataset, which contains a diverse collection of facial images labeled with various emotions, serves as an excellent benchmark for evaluating the performance of emotion recognition systems. The CNN classifier is implemented using OpenCV, a comprehensive open-source library for computer vision. Through rigorous training and validation, the model achieves an impressive accuracy of 97.42 %. This high level of accuracy indicates that the classifier is highly effective at correctly identifying the emotions depicted in the images. Additionally, the model maintains a low loss value of 0.09, which suggests that the predictions are not only accurate but also consistently reliable across different samples in the dataset.

关键词： Emotion recognition Accuracy Mood Webcams Computational modeling Anxiety disorders Medical treatment Training data Streaming media Convolutional neural networks

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Secure Smart Healthcare Surveillance Framework Using Fuzzy C-Means Clustering with Effective Ant Colony Optimization in Internet of Things

Secure Smart Healthcare Surveillance Framework Using Fuzzy C...

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： Jayakumar Karuppaiah C. Aarthi Girirajan M Jananai Parameshachari B. D. Biomedical Engineering Kalaigar karunanidhi Institute of Technology Coimbatore India Sengunthar Engineering College Thiruchengode India Department of ECE SR University Warangal India Computer science and Engineering KPR Instiute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The emergence of IoT device networks is not limited to a specific industry, application, or environment; instead, they are assisting in the development of the country in a number of sectors, including industries, cities, agriculture, and defense. But IoT device networks have some restrictions, including those related to bandwidth, energy resources, and other resources, as well as restrictions based on the environment. The fast growth of the Internet of Things (IoT) has brought to light significant privacy protection problems. This has a tremendous impact on the large-scale IoT applications. In this study, a ground-breaking IOT computing platform for secure and knowledgeable healthcare monitoring services is introduced. Within an IoT architecture, fully homomorphism encryption (FHE), which protects data privacy, is handled and kept. For the proposed IoT framework, a distributed method for clustering-based approaches is created with the scalability to gather and independently analyse massive amounts of hetrogeneous data in distributed IoT devices before it is transferred to the cloud. In this study, the Cluster Heads (CHs) were chosen from the available IoT devices using a brand-new Efficient Ant Colony Optimization (EACO) technique. For healthcare surveillance, fuzzy C-Means clustering (FCMC) is employed. FCM allows us to recognise regular (typical) bio signal patterns.

关键词： Cloud computing Ant colony optimization Data privacy Smart cities Surveillance Wireless networks Scalability

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Deep Learning Based Cancer Detection in Bone Marrow using Histopathological Images

Deep Learning Based Cancer Detection in Bone Marrow using Hi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Swaroopa Rani B Geetha B S G Shivaprasad Yadav Gandla Shivakanth Manjula B M Department of CSE (AI&ML) CMR Technical Campus Hyderabad India Department of Computer Science & Engineering H. K. E. Society's S. L. N. College of Engineering Raichur India Department of Electronics and Telecommunication Engineering Ramaiah Institute of Technology Bangalore India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The cancerous proliferation of abnormal white blood cells (WBC) in the bone marrow and blood is characteristic of leukemia, which has its origins in these blood-forming tissues. Blasts are immature aberrant cells that inhibit the creation of new, lineage-normal blood cells. Acute Lymphoblastic Leukemia (ALL), Acute Myeloid Leukemia (AML), Chronic Lymphocytic Leukemia (CLL), and Chronic Myelomonocytic Leukemia (CML) are the four Chronic Lymphocytic Leukemia (CLL), Chronic myeloid leukemia (CML), and Acute myeloid leukemia (AML) (CML). Patients with leukemia are often diagnosed after their symptoms have been evaluated, a complete blood count (CBC) has been run, and a peripheral blood smear has been examined under a microscope by a pathologist. As part of the process to confirm and classify leukemia, a bone marrow examination and other sophisticated laboratory testing are performed. Traditional blood and bone marrow smear examination using light microscopy is plagued by intra- and inter-observer variability. Overcoming these individual biases of doctors is possible with the use of image processing-based approaches that can automatically analyse pictures of blood and bone marrow smears to detect abnormal cells. Using an American Society of Hematology dataset, the researchers classified cases of ALL into B-cell and T-cell subtypes, as defined by the World Health Organization (ASH). The research improves classification accuracy to 94.12% and compares the effectiveness of three distinct training strategies. The research also includes subtyping AML into M3, M4, and M5 using a ResNet-50 pretrained network and traditional machine learning based classifiers based on the CNN's characteristics. Using the features of ResNet-50, we were able to get a classification accuracy of 96.43 percent, and we were able to get a hundred percent accuracy using just a small number of traditional classifiers. Using the properties of Element and an ASH dataset, the researchers also sought to clas

关键词： White blood cells PSNR Microscopy Clustering algorithms Cells (biology) Ash Bones

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