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检索条件"机构=Master of Science in Computer Science DigiPen Institute of Technology"

共 532 条记录，以下是181-190 订阅

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OHDLL:Optimized Hybrid Deep Learning Model for Classification of Leukaemia images

OHDLL:Optimized Hybrid Deep Learning Model for Classificatio...

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Confluence of Advancements in Robotics, Vision and Interdisciplinary technology Management (IC-RVITM), International Conference on th

作者： Binju Saju V Asha Neethu Tressa R A Suhas Shah Yash Jagesh V Sandhya Department of Computer Science - AI Engineering Adi Shankara Institute of Engineering & Technology Kalady Kerala Master of Computer Applications New Horizon College of Engineering Bengaluru India

A serious haematological cancer that causes mortality and morbidity in people of all ages is leukaemia. The haematologists examine bone marrow and blood to determine Leukaemia. Techniques for manual blood testing that have been in use for a long period are frequently slow and produce less reliable diagnoses. The study proposes a hybrid model to classify leukaemia cell images. Initially, images are pre-processed which includes Image resizing, Gray scale conversion and noise removal using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is compared with other studies in literature. The proposed model has obtained an F1 score of 98.98%, an accuracy of 98.20%, sensitivity of 98.9%, specificity of 98.1%, and precision of 98%.

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Designing Robust Deep Learning Models for RealTime Data Analysis in Machine Learning 3

Designing Robust Deep Learning Models for RealTime Data Anal...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Borade, Monali R. Gupta, Mukur Faiz, Ayaan Acharjya, Kalyan Rajesh, A. Nunciya, P.M. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India

ISBN: (纸本)9798350319125

The developing call for real-time statistics analysis and selection-making has been a primary driver of improving deep studying models for the device getting to know. These models permit the evaluation of massive statistics sets, the generation of predictions from the statistics, and the speedy studying of new behaviors and patterns. Deep mastering fashions have been efficaciously used for various tasks, including natural language processing, computer imagination and prescient, and reinforcement studying. However, because of their complexity and a massive variety of parameters, they can be unreliable and volatile when uncovered to real-global records. This paper outlines strategies and techniques to lay out deep mastering fashions that can be robust to actual-world records. We also provide high-quality practices for deploying and retaining deep learning models in manufacturing packages. Those strategies include regularization techniques, model architectures, facts pre-processing techniques, and model validation and monitoring techniques. We additionally speak of the numerous strategies for assessing and improving the reliability of deep getting-to-know fashions. Subsequently, we provide a case study of a product that uses deep studying fashions to stumble on and classify objects in near-actual time in a manufacturing place. © 2023 IEEE.

关键词： improving maintaining production regularization techniques

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The performance analysis of image resolution model using deep residual algorithm 15

The performance analysis of image resolution model using dee...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Chandi Priya, K.G. Savitha, P. Sujatha, V. Sabarivani, A. Dinesh, M. R.M.K College of Engineering and technology Department of General Engineering Chennai India Nandha Engineering College Department of CSE Perundurai Erode India S.A. Engineering College Master of Computer Applications Chennai India Sathyabama Institute of Science and Technology Department of Biomedical Engineering Chennai India Saveetha University Saveetha School of Engineering Department of VLSI Microelectronics Chennai India

ISBN: (纸本)9798350370249

Deep residual algorithms succeeded wildly in different computer vision problems (e.g., Image Super resolution). Deep neuraenablersraredesign and residual learning are great enablers that define how these architectures overcome limitations in traditional image resolution models. This article implements a deep residual algorithm representing the image resolution model performance research. This paper aims to synthesize HR images from their low-resolution (LR) counterparts by training the proposed model using large-scale image pairs across different resolutions. The model is tested using multiple evaluation criteria and compared with classical resolution models. Experimental results show that the deep residual model performs better in peak signal-to-noise ratio(PSNR), structural similarity index(SSIM), and visual quality metrics. It examines the influence of various network architectures, training strategies, and datasets on how well this algorithm can recognize dialog acts. Experiments demonstrate the superiority of the proposed model with a deep residual algorithm in terms of improving Resolution compared to traditional models. © 2024 IEEE.

关键词： Network architecture

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Mental Health Tracker Using Machine Learning Approaches

Mental Health Tracker Using Machine Learning Approaches

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Global Conference for Advancement in technology (GCAT)

作者： Puneeth N. Thotad Shanta Kallur Laxmi Mundaragi Swati H. Kadam Dept. of Master of Computer Applications K.L.E. Institute of Technology Hubballi India Dept. of Computer Science and Engineering K.L.E. Institute of Technology Hubballi India

Mental illness is a common condition in the 21st century. Despite the significant number of people who experience mental illness, many continue to experience stress without comprehending its damaging effects on their brain. Python and machine learning tools are adopted to create the mental health tracker system. According to the experimentation, the work focuses on developing a psychological well-being tracker. The prediction uses various models such as logistic regression, random forest, decision tree, and k-nearest neighbor. In the proposed system, the patient must answer the questions to predict their mental illness condition. Based on the responses given by the patient, the system will predict whether they need treatment or not. The system will attempt to gain an understanding of a user’s mental state, determine whether they are suffering, and then suggest steps they can take to improve their mental health situation. Mental health prediction might benefit psychological departments looking to enhance their counseling and diagnosis abilities. The model was developed by collecting the question-answer set as options related to mental health by consulting specialized doctors in the hospital. The model is trained using different ML algorithms, finding their accuracy and predicting the results. The analysis was made by considering the raw data and normalized data. Models performance was improved with normalization with the following accuracies. 71.4%, 73.5%, 80.6%, and 81.2% accuracies are obtained for Logistic regression, K-Nearest Neighbor, Decision Tree, and Random Forest, respectively.

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An Efficient Model for Plant Disease Detection in Agriculture Using Deep Learning Approaches

An Efficient Model for Plant Disease Detection in Agricultur...

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Global Conference for Advancement in technology (GCAT)

作者： Puneeth N. Thotad Shanta Kallur Anupama Nandeppanavar Dept. of Master of Computer Applications K.L.E. Institute of Technology Hubballi India Dept. of Computer Science and Engineering K.L.E. Institute of Technology Hubballi India

Detecting plant diseases is crucial as they can significantly impact plant growth. At the same time, several machine-learning methodologies have been employed to distinguish and categorize plant diseases. Deep learning, a subset of machine learning, has shown promising results in terms of accuracy. Using CNN architectures and visualization methods, scientists can develop and implement effective strategies for detecting and classifying plant disease symptoms. These methods primarily rely on analyzing leaf characteristics such as color and damage. A plant-village dataset of 54,306 images representing 14 different crop classes and 26 types of infections was used to train the models. The convolutional neural networks (CNNs) and additional machine learning models were trained on this dataset and achieved an impressive accuracy rate of 95% in identifying disease symptoms on plant leaves. Additionally, an efficient web-based application for crop disease detection was developed as part of this approach.

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Enhanced Demand-Side Management for Electric Vehicle Charging Stations Using Pyramidal Dilation Attention Neural Networks

Enhanced Demand-Side Management for Electric Vehicle Chargin...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： N. Venkateswaran Ramprabu J Ramaprabha Marimuthu Nagella Venkata Ramana Mahesh Kotha Balasubbareddy Mallala Department of Master of Business Administration Panimalar Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Management Studies Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Computer Science and Engineering (AIML) CMR Technical Campus Hyderabad Telangana India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

With the improvement in EV's popularity, controlling the load demand of EVCS's has become an essential task to keep the grid balanced and efficient. Conventional DSM techniques tend to rely on historic patterns to predict charging trends, hence cannot serve as viable solutions to peak load problems. In order to improve charging demand forecasting, this research presents a Pyramidal Dilation Attention Convolutional Neural Network (PDACNN) for the DSM of EV charging stations. By using precise forecasting and ideal charging schedule, this study aims to increase the effectiveness of DSM in EV charging stations. The PDACNN is used to predict the charging load for EV stations, allowing for optimized scheduling of charging sessions that align with grid capacity and energy availability. By then, the proposed method is implemented on MATLAB platform and evaluated with other existing methods such as Artificial Neural Network (ANN), Deep Reinforcement Learning (DRL), and Genetic Algorithm (GA). The proposed PDACNN method achieves an RMSE of 0.0753 and an accuracy of 98.9%, demonstrating its effectiveness in EV charging load forecasting and demand-side management.

关键词： Training Accuracy Demand side management Scalability Transportation Electric vehicle charging Mathematical models Convolutional neural networks Vehicle dynamics Genetic algorithms

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Self-Supervised Learning with Variational Autoencoders for Anomaly Detection in Autonomous Drone Fleets

Self-Supervised Learning with Variational Autoencoders for A...

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Smart Technologies, Communication and Robotics (STCR)

作者： Kirubhakaran M M.Sundarrajan Gowtham T D. Agila Akshya. J Mani Deepak Choudhry Department of Computer Science and Engineering Surya Engineering College Erode Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Master of Computer Application Excel Engineering College Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Agni College of Technology Chennai Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

However, Unmanned Aerial Vehicles (UAVs) are increasingly being employed for different applications such as surveillance, logistics, disaster response, and smart city management, and thereby, the requirement for robust detection of anomalies and path optimization techniques has arisen. Existing traditional UAV monitoring systems typically depend on anomaly detection based on a rule or an unsupervised clustering method, i.e., DBSCAN and One-Class SVM, balancing high false positives and slow response time, correction, and suboptimal trajectory. To tackle these issues, we develop an AI-powered UAV anomaly detection and adaptive navigation framework where Isolation forests and MDPs are used to predict UAV trajectory, and DQNs are used for adaptive navigation. Experimental results in a simulated smart city arena with a multi-UAV fleet show that our proposed framework achieves 94% accuracy, 92% precision, and 91% recall, which are much superior to the existing approaches, such as One Class SVM (87% accuracy) and DBSCAN (80% accuracy). Additionally, the system compensates for false positives by 30%, improves energy efficiency by 15%, and reduces UAV collision risks by 20%, with high compatibility with real-time UAV fleet management. In the future, the scalability and the adaptability of the UAV-based smart city operation will be furthered through federated learning-based decentralised UAV coordination, reinforcement learning for autonomous swarm intelligence, and hybrid optimisation techniques for improving energy efficiency.

关键词： Support vector machines Accuracy Smart cities Navigation Scalability Autonomous aerial vehicles Energy efficiency Trajectory Anomaly detection Optimization

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An Effective Diagnosis of Alzheimer’s Disease with the Use of Deep Learning based CNN Model

An Effective Diagnosis of Alzheimer’s Disease with the Use ...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： K C Sharmili G P Suja E. Pandian Md. Abul Ala Walid Sripriya Arunachalam G.Charles Babu Computer Science Stella Maris College 17 Cathedral Road Chennai Computer science Muslim Arts College Thiruvithancode Master of Computer Application Sri Muthukumaran Institute of Technology Mangadu Chennai Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Computer Applications-PG School of Computing Sciences VELS Institute of Science Technology & Advanced Studies - VISTAS Velan Nagar P.V. Vaithiyalingam Road Pallavaram Chennai Department of Computer Science and Engineering GRIET Hyderabad

In recent years, Alzheimer’s disease has become a major health concern. Over 45 million individuals worldwide suffer from this illness. Alzheimer’s disease is a neurodegenerative illness of unidentified etiology and process that causes cognitive deterioration, and it mostly affects the elderly. Dementia, the gradual and irreversible loss of brain cells, is the leading cause of Alzheimer’s disease. Those with this illness lost the ability to think, read, and do other basic tasks. A machine learning system may aid in this situation by making accurate predictions about the onset of illness. Dementia diagnosis in a broad patient population is the major objective. This research presents the findings and analysis of many Machine Learning models for identifying dementia. To alleviate this obstacle and help in the diagnosis of AD will create a deep-learning architecture that uses stacked auto-encoders and a softmax output layer. The proposed technique can analyze numerous classes in a single setting, unlike earlier procedures, and only needs a small number of labeled training samples and basic domain expertise to get started. In the present studies, a substantial improvement in performance when it came to classifying all diagnostic subgroups. Using the proposed technique can run these time series through a Convolutional Neural Network (CNN) and ResNet50 model to extract the distinguishing patterns across stages. With an F1-score of 0.99 and an accuracy of 99.91 percent, the CNN-based technique outperformed the standard feature-based classifiers by a significant margin.

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Natural language Processing and Ontology based Decision Support System for Diabetic Patients 9

Natural language Processing and Ontology based Decision Supp...

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9th International Conference on Electrical Engineering, computer science and Informatics, EECSI 2022

作者： Chandra, Ritesh Shukla, Abhaya Tiwari, Sadhana Agarwal, Sonali Svafrullah, M. Adiyarta, Krisna Indian Institute of Information Technology Allahabad Information Technology Department Prayagraj India Manipal Institute of Technology Udupi Electronics and Communication Department Manipal India Universitas Budi Program of Master of Computer Science Luhur Indonesia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

Diagnosis of diabetes disease is very promising because it may create various other acute or chronic health problems in the human body. This study proposes a recommendation system for diagnosis and treatment of Diabetes Mellitus as suggested by the Clinical Practice Guidelines (CPG). A treatment plan is designed which includes medications and lifestyle changes, according to the patient information, symptoms, and lab tests. Various tasks such as gathering, modelling, and implementing diabetic domain expertise knowledge collected from the experts, CPG, and other sources are used to construct an ontology for diabetes and a decision support system to manage diabetes in its early stages. This work also uses an Optical Character Recognition (OCR) and a Natural Language Processing (NLP) model that help directly to read doctors prescription and extract useful information useful for developing the ontology. Web Ontology Language (OWL) is used to build the proposed ontology, and the Semantic Web Rule Language (SWRL) is used to create the rules for providing restrictions on the ontology. © 2022 institute of Advanced Engineering and science (IAES).

关键词： Electronic Health Records Natural Language Processing Optical Character Recognition SWRL

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AI for Disaster Response: Developing Intelligent Systems for Real-Time Emergency Management

AI for Disaster Response: Developing Intelligent Systems for...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： T.K. Lakshmi Naresh Kumar Mangalapu Mary Fathima Anuja Joseph Swaminathan S. Chandravadhana R. Pari T. Ranjana Department of Computer Science and Engineering Malla Reddy University Hyderabad Telangana India Department of Orthodontics & Dentofacial Orthopedics Kamineni Institute of Dental Sciences Narketpally Nalgonda Telangana Department of Computer Science Prince Shri Venketeshwara Arts and Science College Chennai Tamil Nadu India Department of Mechatronics Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering VELS Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India Master of Business Administration S.A. Engineering College Thiruverkadu Tamil Nadu India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

The research introduces the design of an AI-based disaster response system that subscribes to enhance real-time emergency management. Manual processing, slow communication and siloed data sources are common downsides of traditional-approach systems, which ultimately translate into slower response times or insufficient resource spend. The system deals with them by exploiting ML, predictive analytics, and automatic decision-making so that these can consolidate pre-existing real-time information from social media, satellite images or IoT sensors. Several experiments show better-than-previous: response times decrease from an average of 11 hours to 3.6 and prediction accuracy increased from 62.5% up to cover %85 Average efficiency also improved including +23% in resource allocation. The innovative solution shifts disaster response from being entirely reactive to proactive, creating faster and more informed responses during times of crisis, thereby assuring public safety for all in the face of calamities.

关键词： Training Accuracy Disasters Emergency services Real-time systems Public security Time factors Resource management Predictive analytics Intelligent systems

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