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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Josy Elsa Varghese Vidya Rao Dept. of CSE (Cyber Security and CSE( AI and ML) Ramaiah Institute of Technology Bangalore India Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

The Internet of Things (IoT) and cloud computing are the key components in the industrial revolution. The IoT platform provides provisioning, administration, and automation of smart items inside a specific IoT architecture, enabling the goal of connecting physical objects to the Internet so they may collaborate, communicate, and act intelligently without the need for human involvement. On the other hand, cloud computing has brought consumer and business applications online, revitalizing IT services and saving businesses money by eliminating the need to build and maintain on-site IT infrastructure for data storage and application operations. The long-term cost-effectiveness of cloud-based solutions includes enhanced security, corporate data mobility, more employee cooperation, and more sophisticated disaster recovery plans. The majority of cyber physical systems are currently hosted on IoT cloud platforms due to their advantages. However, security issues of the platform have to be addressed. Distributed Denial of Service attacks (DDoS) are among the most common. This paper studies the importance of IoT cloud architecture, discusses the defensive measures against DDoS attacks, and suggests a high-level conceptual IoT architecture to defend DDoS attacks while addressing future research challenges.

关键词： Surveys Cloud computing System performance computer architecture Denial-of-service attack Internet of Things Security computer crime Business Self-service

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Image encryption using XOR-based continuous tone MSS and Cellular Automata 6

Image encryption using XOR-based continuous tone MSS and Cel...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Akanksha Garg, Hitendra Shivani, Shivendra GLA University Dept. of Computer Engineering and Applications Mathura India Thapar Institute of Engineering and Technology Dept. of Computer Science Patiala India

ISBN: (纸本)9798350346961

Online communication has become quicker and simpler as a result of the growth of the internet and the advancement in technology. People are using multimedia data in almost all spheres of life. The amount of multimedia data transmitted has been increasing at a massive rate. Multimedia data delivered over open and unreliable networks are susceptible to unauthorized access. As a result, a cryptographic standard is needed to securely and efficiently transmit multimedia data such as images over the networks. This study uses cellular automata and an XOR-based continuous tone MSS approach to create an image encryption method. The suggested approach has eliminated VC's drawbacks, including secret/share quantity constraints, unpredictable share patterns, codebook requirements, and pixel extension in shares and recovered secret pictures. The recommended approach uses an (n,n) multi-secret sharing system that can send n secret photos simultaneously. At the time of encoding, the master share overcomes been made using the secret key, and it may be successfully retrieved when decoding using a comparable secret key. The exploratory results and security examination shows the proficiency of the given approach. The complexity of the proposed approach can be adjusted by using the different combinations of cellular automata rules.. © 2023 IEEE.

关键词： Cellular automata

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Image-Based Distance Estimation of a Target from a Camera using Color Checker Chart

Image-Based Distance Estimation of a Target from a Camera us...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Vedavyasa Kamath Ciji Pearl Kurian Shailesh K. R Suprabha Padiyar U Mangalam Sontakke Dept. of Electrical and Electronics Engg. Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

In recent years, photography-based photometry has gained popularity and is being implemented widely. Under this approach, a camera is used as a sensor to estimate photometric quantities like illuminance or luminance. The absolute distance between the target surface and the camera can be crucial for photometric analysis. The technique proposed in this article employs a fixed focal length lens mounted on a DSLR camera. The standardised classic ColorChecker chart is placed over the target surface, and the scene image with the chart is captured. The intuition is that the average pixel area of the swatches present on the chart decreases as the camera is moved away from the chart. OpenCV libraries are used to calculate the average pixel area of the chart color swatches. Distance is measured using an industry-standard laser-based distance measurement tool. Curve fitting is used to establish a mathematical relationship between camera-chart distance and the average pixel area of the swatches. Distance varies from 650 cm to 2 m during the data collection process. The influence of the tilt of the chart plane with respect to the camera sensor plane is also studied. During testing, the proposed power trendline-based mathematical model performed with an error percentage of less than 1. This approach for distance measurement enables finer tuning of the machine learning-based model and, as a result, more accurate illuminance estimation for the work plane.

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Ensemble of Machine Learning Classifiers for Detecting Deepfake Videos using Deep Feature

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2023年第4期50卷 1279页

作者： Padmashree, G. Karunkar, A.K. Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Rapid progress in artificial intelligence, machine learning, and deep learning over the last few decades has resulted in new methodologies and tools for altering multimedia. Deepfakes represent a technology for seamlessly swapping faces in videos, yielding remarkably lifelike outcomes. While this technology offers numerous applications, its malicious use, such as disseminating false information or participating in cyber harassment, can exert a significant impact on society. This makes the identification of deepfakes a critical issue. In this paper, we propose a hybrid strategy for deepfake identification in videos that combines deep learning and machine learning. Faces are identified in the videos using YOLO-V3 face detectors and using the efficientNet deep learning model, features are extracted from the faces. Deepfakes are identified using an ensemble of machine learning classifiers such as support vector machine (SVM), decision trees(DT), k-nearest neighbor (KNN), and naive bayes(NB) based on the max voting approach, which provides better results for datasets of varying sizes and resolutions. Experiments are carried out by integrating the Celeb-DF(v2) and FaceForensics++ (FF++) datasets and the suggested technique achieves 99.64% accuracy and proves that the suggested method is more effective than state-of-the-art methods. © 2023, International Association of Engineers. All rights reserved.

关键词： Decision trees

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DEAL-YOLO: Drone-based Efficient Animal Localization using YOLO

arXiv

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arXiv 2025年

作者： Naidu, Aditya Prashant Gosalia, Hem Gakhar, Ishaan Rathore, Shaurya Singh Didwania, Krish Verma, Ujjwal Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Dept. of Mechatronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Dept. of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Dept. of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

Although advances in deep learning and aerial surveillance technology are improving wildlife conservation efforts, complex and erratic environmental conditions still pose a problem, requiring innovative solutions for cost-effective small animal detection. This work introduces DEAL-YOLO, a novel approach that improves small object detection in Unmanned Aerial Vehicle (UAV) images by using multi-objective loss functions like Wise IoU (WIoU) and Normalized Wasserstein Distance (NWD), which prioritize pixels near the centre of the bounding box, ensuring smoother localization and reducing abrupt deviations. Additionally, the model is optimized through efficient feature extraction with Linear Deformable (LD) convolutions, enhancing accuracy while maintaining computational efficiency. The Scaled Sequence Feature Fusion (SSFF) module enhances object detection by effectively capturing inter-scale relationships, improving feature representation, and boosting metrics through optimized multiscale fusion. Comparison with baseline models reveals high efficacy with up to 69.5% fewer parameters compared to vanilla Yolov8-N, highlighting the robustness of the proposed modifications. Through this approach, our paper aims to facilitate the detection of endangered species, animal population analysis, habitat monitoring, biodiversity research, and various other applications that enrich wildlife conservation efforts. DEAL-YOLO employs a two-stage inference paradigm for object detection, refining selected regions to improve localization and confidence. This approach enhances performance, especially for small instances with low objectness scores. © 2025, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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Bayesian Belief Network Analysis for SPAD System in Railways 4

Bayesian Belief Network Analysis for SPAD System in Railways

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4th International Conference on Multimedia Processing, Communication and Information technology, MPCIT 2024

作者： Krishnamurthy, Manjunath Sargur Rahul, Bommisetty Bondala, Chandrahas Reddy Reddy, M. Vishnu Vardhan Magha Raghul, G. Ambika, B.J. JP Morgan & Chase Co. HoustonTX United States BMS Institute of Technology and Management Dept. of AI&ML Bengaluru India Manipal Institute of Technology Bengaluru Dept. of Computer Science and Engineering Karnataka Manipal576104 India

ISBN: (纸本)9798350375466

Even with a powerful network of signaling and warning systems in the country, there have been many examples of trains crossing the red signal due to various factors, even today. These occurrences, known as Signal Passed At Danger (SPAD) events, could potentially result in severe consequences such as train derailments, train collisions, infrastructure collisions, and other dangerous events. Traditionally, these events have been analyzed using the Fault Tree Analysis (FTA) approach Bayesian Network (BN) is considered to be a better model to represent this situation when it comes to handling complexity. Bayesian Network allows for integration and includes systematic knowledge about the system, which helps to get a very flexible concise, and simple graphed representation. This study demonstrates applications of Bayesian Belief Networks (BBN) analysing and enlightening the Signal Passed at Danger (SPAD) system in railway processes. Execution of BBN offers more dynamic and flexible models compared to outmoded Fault Tree Analysis (FTA), struggling with complexity. BBN efficiently catches the inter dependencies between factors that are contributing to SPAD events, such as signal conditions, human errors, and mechanical issues. Over simulations and probability calculations using OpenMarkov software, Study revealed key intuitions, such as identifying highly risk apparatuses within SPAD system. The outcomes include enhanced risk calculation accuracy and recommendations for improving signal systems and safety protocols. These conclusions contribute to a safer and reliable railway structure, highlighting the success of BBN for complex, safety critical systems in real world applications. © 2024 IEEE.

关键词： Derailments

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Car Defect Detection Using Image Processing

Car Defect Detection Using Image Processing

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Durai, S. Sujithra, T. Charan, Athmakuri Sai Pulyala, Sai Kiran Satyam, Battula Vishnuwardhan Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Avadi Dept. of Cse Chennai India Manipal Institute of Technology Computer Science and Engineering Manipal India

ISBN: (纸本)9798350334357

Defect Detection became one of the hardest problems in this world nowadays. As of now humans detected the defects with the help of their naked eye. This process is time consuming and this can be done by only experts and these decisions may be differ from expert to expert. To avoid this problem we are training the collected data set. This will consume very less time and provides the best solution. The detection of defects is essential in quality control in manufacturing to get high quality products in less time. Humans can identify large defects but in case of micro defects humans can't found those so, this implementation will helpful in finding large as well as micro and milli defects as well. In this project we are trying to detect the defects such as dents and ripples present on the surface of the car which are macro in size at the time of manufacturing the cars and in the case of accidents happened. We'll use a high-quality camera to simultaneously capture photographs of the vehicle's five sides. We will use Convolution Neural Network [CNN] for training (which highlights the location of the defects). Once trained, that model we can use it for different end user applications. © 2023 IEEE.

关键词： Feature extraction

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A comparative Analysis of ensemble methods and their efficiency in the classification of 'HIT AND RUN' cases in an imbalanced dataset (Traffic Crashes) with and without using "Synthetic minority oversampling technique" 33

A comparative Analysis of ensemble methods and their efficie...

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33rd International Conference on computer Theory and applications, ICCTA 2023

作者： Manjula Shenoy, K. Prabhu, R Srinivas Manipal Academy of Higher Education Manipal Institute of Technology Information and Communication Technology Department Manipal576104 India Manipal Academy of Higher Education Manipal Institute of Technology Data Science and Computer Applications Department Manipal576104 India

ISBN: (纸本)9798350309577

In some cases of developing a predictive model for a classification problem, the distribution of classes is skewed, meaning a majority of the training samples belong to a single class and a very small number of samples belong to a minority class. If Machine Learning techniques are applied directly to such data sets, it may result in a model which either ignores the minority class as a whole or gives less importance to the samples belonging to the minority class. Such datasets are often called imbalanced datasets. There are multiple ways to deal with these problems some include Random Over Sampling, Random Under Sampling, Threshold value tuning, one class classification to find the outlier, etc. One such technique is called the "Synthetic minority oversampling technique (SMOTE)". In this paper we are going to analyze the efficiency of ensemble methods for the classification of imbalanced datasets with and without using SMOTE. The dataset used for this purpose is an open-source Traffic crashes dataset available on the Chicago data portal. The target variable for our study is the "HIT-AND-RUN-I"column available in the dataset. This is a dichotomous variable that has 2 valid values. "Y"for the cases that are of positive outcomes as in "HIT-AND-RUN-I"= yes. "N"for the cases that are of negative outcomes as in "HIT-AND-RUN-I"= No. © 2023 IEEE.

关键词： Labeled data

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Machine Learning Classifiers for Predicting the Risk of Cardiovascular Disease in Healthcare 4.0 3

Machine Learning Classifiers for Predicting the Risk of Card...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Kumari, Palak Kumar, Ashish Chatterjee, Kakali Master of Computer Applications Patna Women's College Patna India National Institute of Technology Patna Dept. of Computer Science and Engineering Patna India

ISBN: (纸本)9798331528201

Cardiovascular disease (CVD) generic phrase used to describe heart or blood vessel illness. Conditions that impact the body's, brain's, or heart's blood flow. Reduced blood flow can result from factors such as blood clots, leading to heart attack, angina, or stroke. Other potential meanings of CVD. These models offer key benefits like clear interpretability and the ability to analyze complex patterns, significantly enhancing the accuracy of cardiovascular disease risk predictions. The demerits of using SVM, LR, K-NN, and MLP for CVD prediction include inter-pretability issues, overfitting risks, data requirements, feature selection challenges, scalability concerns, algorithm complexity, and class imbalance. Collaboration between machine learning experts and healthcare professionals is essential for developing accurate and reliable CVD prediction models that enhance patient care. CVD is a significant health threat, often leading to fatal complications. Early detection is crucial for effective intervention. This study employs SVM (accuracy is 0.84%), LR (accuracy is 0.86%), K-NN (accuracy is 0.94%), and MLP (accuracy is 0.79%) machine learning techniques to enhance CVD detection by addressing outliers and null values, contributing to advancing automatic detection initiatives. © 2024 IEEE.

关键词： Nearest neighbor search

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HealthPredictor: Accurate Health Insights at Your Fingertips 3

HealthPredictor: Accurate Health Insights at Your Fingertips

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Sajjan, Mahantesh M. Ammanagi, Nausheen Pujari, Laxmi Kamatar, Vishwanath S Belavatgi, Meenaz K.L.E. Institute of Technology Dept. of Master of Computer Applications Hubballi580 027 India K.L.E. Institute of Technology Dept. of Computer Science and Engineering Hubballi580 027 India

ISBN: (纸本)9798350364569

The "HealthPredictor: Accurate Health Insights at Your Fingertips"system transforms healthcare by predicting diseases based on user symptoms. Utilizing advanced machine learning models such as Decision Tree, SVM, Random Forest, and Logistic Regression, it assesses the likelihood of various illnesses. The system compares symptoms to a comprehensive dataset to provide accurate predictions, aiding in early detection. Users can consult with specialist doctors through an integrated chat feature, ensuring quick access to medical advice. By continuously learning from historical data, the system enhances its predictive capabilities, identifying new health patterns and trends. This proactive approach empowers users with timely information and personalized consultations, leading to better health outcomes. © 2024 IEEE.

关键词： Electronic health record

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