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Efficient Dengue Spread Prediction Using Machine Learning Models with Various Preprocessing Techniques

Efficient Dengue Spread Prediction Using Machine Learning Mo...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： K. Saraswathi K. Rohini Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The viral disease dengue, spread by mosquitoes, is a severe hazard to public health worldwide. Predicting dengue outbreaks is an essential field of study that aims to stop and lessen the effects of the virus spread by mosquitoes. Utilizing modern technologies like data analytics and ML(Machine Learning) will significantly improve the capacity to predict and contain dengue outbreaks. The work examines how the effectiveness of Convolutional Neural Networks (CNNs) in forecasting and evaluating Dengue outbreaks is affected by several scaling strategies, including Min-Max Scaling, Z-Score Standardization, and Robust Scaling. For disease control to be effective, outbreaks must be predicted with accuracy and timeliness. Before feeding the input data into the CNN architecture, preprocess takes place in experiments utilizing Robust Scaling, Z-Score Standardization, and Min-Max Scaling. The model is methodically assessed using necessary measures like recall, accuracy, and precision. The findings show that robust scaling consistently performs better than the other scaling methods, improving CNN’s capacity to identify characteristics and patterns important for predicting Dengue outbreaks. The result was that Robust Scaling+CNN gave the best results, with an accuracy of $93 \%$, precision, and recall of 0.92 and 0.85, respectively. The tool used for execution is Python.

关键词： Accuracy Recurrent neural networks Standardization Predictive models Data models Numerical models Convolutional neural networks

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Deep learning: Spatial-Temporal road traffic data congestion using Agglomerative Clustering

Deep learning: Spatial-Temporal road traffic data congestion...

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International Conference on Electrical Energy Systems (ICEES)

作者： S. Senthilarasi S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology ampersand Advanced Studies (VISTAS) Pallavaram Chennai India

In the long run of road traffic data especially in urbanization due to people migration because of employment and other factors still it is a challenging issue in daily life to recover the congestion. In our paper, the causes of road traffic data and its measures have been shown. The algorithm to alternate the route for people in case of traffic has been displayed in flowchart. The different types of traffic sign detection to alert the people in common on road traffic has been discussed with colors. The estimation of road traffic congestion is computed using necessary formulas has been shown. Various measures have been calculated show the higher accuracy of agglomerative clustering compared with other models. The experimental study has been shown in Real Time traffic data of Chenmai city from the year 2015 to 2021. In addition, drastic measures to control road traffic congestion has been discussed for present and future.

关键词： Flowcharts Image color analysis Roads Urban areas Clustering algorithms Estimation Energy measurement

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Exploring Convolution Neural Networks for Image Classification in Medical Imaging 2

Exploring Convolution Neural Networks for Image Classificati...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Vishwa Priya, V. Chattu, Padmini Sivasankari, K. Pisal, Dnyaneshwar Tukaram Renuka Sai, Bantupalli Suganthi, D. Vels Institute Science Technology and Advanced Studies Department of Cs Tamilnadu Chennai India Ace Engineering College India Sri Sai Ram Institute of Technology Department of Ece Tamilnadu Chennai India SVPMs Institute of Management Baramati Pune Maharashtra Malegaon India Raghu Engineering College Computer Science and Engineering Visakhapatnam India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Thandalam Chennai India

ISBN: (纸本)9798350306415

Modern healthcare relies on medical imaging to diagnose and cure diseases. Convolution Neural Networks (CNNs) have excelled at image categorization, and their use in medical imaging could change illness diagnosis. This study examines CNN's medical picture categorization performance. This study used X-rays, CT scans, MRIs, and histopathology slides. Traditional CNNs, transfer learning models, and bespoke networks were constructed and trained. The training data included lung ailments, cancer detection, bone fractures, and neurological disorders. Our investigations showed that CNNs can extract complex characteristics from medical photos, improving classification accuracy. Transfer learning, where pre-trained models were fine-tuned on medical data, performed well with 94.8% accuracy. We used cutting-edge data augmentation and attention strategies to improve model generalization and interpretability. In addition to high classification accuracy, we studied model explainability using gradient-based methods and visualization to highlight medical picture regions of relevance that influenced model predictions. Building trust with medical practitioners and understanding deep learning model decision-making requires interpretability. In clinical settings, CNN deployment raises ethical and practical issues such as data privacy, model robustness, and regulatory compliance. According to our research, CNNs may increase medical picture classification accuracy and speed. X-rays had the best accuracy at 95.2%, followed by CT scans at 92.7% and MRIs at 94.1%. Histopathological slides exhibited 88.6% accuracy;however, this shows that CNNs can diagnose diseases from microscopic images. © 2024 IEEE.

关键词： Convolutional Neural Networks Image Classification Medical Data Analysis Medical Imaging

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Crow Search Optimization to Identify Adversary Nodes in Wireless Networks 2

Crow Search Optimization to Identify Adversary Nodes in Wire...

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2nd International Conference on Edge computing and Applications, ICECAA 2023

作者： Rajaram, Gnanajeyaraman Thangaraj, T. Stephen Packialatha, A. Deepa, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Applied Machine Learning Tamil Nadu Chennai India Paavai Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350347579

Adversary node detection systems have become a vital component due to the malware attacks. These systems are very important for network security due to internet's widespread development and increased accessibility to global data systems. This paper presents a Crow Search Optimization Algorithm (CSOA) to detect the adversary node in a wireless network. The Rivest, Shamir, Adleman (RSA) algorithm transmits secure data at first. It uses the public key to encrypt the data and the private key to decrypt the data and the adversary nodes can't access or modify the real data. One of the significant problems is when an unknown attack occurs on the network due to the high volume of data while the detection's accuracy and false alarm rate decline. The proposed system aims to improve accuracy by identifying the adversary nodes using CSOA algorithm. From the results, it is observed that the CSOA mechanism reduces the loss ratio and improves throughput of the network. © 2023 IEEE.

关键词： Optimization

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Enhancing Academic Performance Predictions Using Deep Belief Networks: A Novel Approach for Educational Data Analysis 3

Enhancing Academic Performance Predictions Using Deep Belief...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Zahoor, Irwa Mani, M.S. Radha Manga Saravanan, B. Kumar, Ajay Bhattacharya, Chaitali Kar, Rohit Department of Computer Science and Engineering Islamic University of Science and Technology Jammu and Kashmir Awantipora India Department of Information Technology SRKR Engineering College Andhra Pradesh Bhimavaram India Department of CSE Sri Eshwar College of Engineering Tamilnadu Coimbatore India Tecnia Institute of Advanced Studies Rohini New Delhi India Tecnia Institute of Advanced Studies CDL Wazirpur New Delhi India Department of Management St. Claret College Autonomous Karnataka Bengaluru India

ISBN: (纸本)9798331532420

The capacity to predict students' performance is vital in matters related to deep learning and its relationship to educational data and, more generally, to higher education. Precise performance prediction facilitates both the selection of appropriate courses and the creation of efficient long-term study plans. It helps with predicting how well students will do in class, and it lets administration and educators keep tabs on students to support the need for training program integration. Preprocessing, feature selection, and model training are all part of the suggested method. Transforming raw data into a usable format is a crucial step in machine learning. It helps fix problems in real-world datasets and produce better outcomes. Principle Component Analysis (PCA) is frequently employed in feature selection. All through the training, DGNN was always in use. This novel approach outperforms GNN and RNN with an average accuracy of 94.27%. © 2024 IEEE.

关键词： Adversarial machine learning

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Next – Gen Driver Monitoring System

Next – Gen Driver Monitoring System

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Global Power, Energy and Communication Conference (GPECOM)

作者： Suganiya Murugan Harish Krishnadas S Abiishek G Pradeep Kumar S Rubini B Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India Department of Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798350351088

ISBN: (纸本)9798350351095

the Next Gen Driver Monitoring System utilizes advanced techniques to enhance road safety by detecting driver drowsiness, nodding, and blink patterns. Integrating cutting-edge technology, the system analyzes facial landmarks and eye movements in real-time through sophisticated algorithms. Through the fusion of computer vision and advanced algorithms, it accurately identifies signs of driver fatigue and distraction, providing timely alerts to mitigate potential risks on the road. This research delves into the design, implementation, and evaluation of the system, demonstrating its effectiveness in reducing accidents caused by drowsy or inattentive driving. The Next Gen Driver Monitoring System represents a significant advancement in automotive safety technology, ushering in safer driving experiences.

关键词： computer vision Vehicle safety Fatigue Road safety Real-time systems Monitoring Vehicles

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Instructors' Perspectives on Capstone Courses in computing Fields: A Mixed-Methods Study 2024

Instructors' Perspectives on Capstone Courses in Computing F...

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29th Working Group Reports on Innovation and Technology in computer science Education, ITiCSE WGR 2024

作者： Hooshangi, Sara Shakil, Asma Dasgupta, Subhasish Davis, Karen C.C. Farghally, Mohammed Fitzpatrick, KellyAnn Gutica, Mirela Hardt, Ryan Riddle, Steve Seyam, Mohammed Department of Computer Science Virginia Tech BlacksburgVA United States School of Computer Science University of Auckland Auckland New Zealand Info Systems and Tech Mgmt Dept George Washington University WashingtonDC United States Computer Science and Software Engineering Dept. Miami University OxfordOH United States School of Computing Instruction Georgia Institute of Technology AtlantaGA United States School of Computing & Academic Studies British Columbia Institute of Technology BurnabyBC Canada Computer & Information Science University of St. Thomas Saint PaulVA United States School of Computing Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional de

关键词： Students

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Instantaneous Interpretation into Sign Language for the Hearing Impaired 1

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Information Technology Guru Ghasidas Vishwavidyalaya Bilaspur India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

Because they are unable to convey their thoughts to others who can hear, deaf persons may have a difficult time controlling their emotions and may find that their routines are often interrupted. This is the primary impetus behind the progression of things. These individuals would be helped by this system, and it would make it possible for them to convey their ideas in a manner that is on par with that of persons who do not have any kind of physical impairment. Recent advances in artificial intelligence have made it possible to design a system that is capable of resolving this problem, which was previously thought to be impossible. The purpose of this research is to develop, for the benefit of the deaf community, a system that can translate speech into text. Because it’s possible that some individuals won’t comprehend only the written word, the speech will also be translated into international sign language (ISL). In this method, persons who are deaf or hard of hearing will have their sign language translated into spoken language. In order to develop the most accurate model possible, we will integrate NLP with a variety of ML and AI techniques. For the purpose of prediction, convolutional neural networks (CNN) will be used since lip movements are continuous and rapid, making them challenging to capture. CNN and attention-based long and short-term memory (LSTM) approaches will function since they are both effective at predicting visual input. In order to get more desirable results, we will be using several techniques for augmenting the data. TensorFlow and Keras are going to be the Python libraries that are employed in order to do the voice-to-text translation. At the moment, there is an abundance of software accessible;however, all of them require the use of a network. This piece of hardware can function very well without being connected to the internet. Using the suggested technique, we were able to attain a 100% accuracy rate in sign language prediction and a 96% a

关键词： Forecasting

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models

Hyperspectral Images for Crop Prediction using Machine and D...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： R. Mahalakshmi K. Kumutha M. Sakthivanitha R. Bagavathi Lakshmi Mohamed Sirajudeen Sankar Padmanabhan Department of Computer Applications-PG Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Computer Applications-PG Vels Institute of Science Technology Advanced Studies Chennai Tamil Nadu India Department of Computer Application Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Information Technology Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India PG and Research Department of Computer Science Nilgiri College of Arts and Science The Nilgiris Tami Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data.

关键词： Support vector machines Radio frequency Deep learning Recurrent neural networks Accuracy Crops Spatial databases Convolutional neural networks Reliability Hyperspectral imaging

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A Detailed Investigation into the Brain Cancer Detection Capabilities of CNN

A Detailed Investigation into the Brain Cancer Detection Cap...

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International Health Informatics Conference, IHIC 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Choudhury, Tanupriya Kotecha, Ketan School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International University Lavale Campus Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819771899

Brain tumour detection is an accepted testing task in the beginning phases of life. Yet, presently it has progressed with different AI calculations. Along these lines, to identify the brain tumour of a patient we consider the data of patients like MRI pictures of a patient’s brain. Here our concern is to spot if the tumour is available in persisting’s brain. It’s crucial to identify the tumour at the beginning level for a sound lifetime of a patient. During this task, we gauge the seriousness utilizing Convolutional Neural Network calculationwhich gives us precise outcomes. Tumour division is a pivotal and burdensome assignment inside the territory of clinical picture preparation as a human-helped manual characterization may bring about erroneous expectations and findings. We suggested using convolutional neural organization to distinguish tumours from 2D appealing reverberation brain images (MRI). The exploratory analysis was performed on a dataset that included tumours of various sizes, regions, forms, and picture forces. We used convolutional neural networks, which are implemented using Keras and Tensorflow because they respect a much better display than standard ones. CNN achieved an accuracy of 83.54% in our research. The recently developed CNN design may be used as a productive decisionaid device for radiologists in clinical diagnostics because of its high speculation capacity and quickness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neuroimaging

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