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Proceedings of the 4th International Conference on Information Management & Machine Intelligence

作者： Ratnesh Kashyap Juhi Singh Shweta Sinha Computer Science & Engineering Amity University India Computer Science and Engineering Manipal University JIPUR India CSE_ ASET_AUH Amity University Haryana India

ISBN: (纸本)9781450399937

Technology in education has the potential to drive pupils to learn in a more interactive and perceptible manner. The application presented here is focused on the used materials during the teaching-learning process, such as text and photos, and displays a major 3-dimensional model or video on the screen. These applications provide the students by boosting them to peruse innovative ideas by using the graphical guide. Apart from utilizing it in school, it can be used for higher education in universities. The advantages of Augmented Reality are also discussed in comparison to old tactics such as chalkboards and textbooks. The findings demonstrate that, in general, Augmented Reality technologies offer good potential and benefits that can be applied in education. The paper also identifies AR's shortcomings, which could be solved through further research.

关键词： ARCORE

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Evaluating Atherosclerotic Plaque Composition Using 3D Magnetic Resonance Imaging 1

Evaluating Atherosclerotic Plaque Composition Using 3D Magne...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Salagrama, Shailaja Bhardwaj, Rajat Logeshwaran, J. Shukla, Rishi Prakash University of the Cumberland's Department of Information Technology Kentucky Williamsburg United States Amity University Aset Department of Computer Science and Engineering Karnataka Bengaluru India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Ait Management Chandigarh University Punjab Mohali Gharuan India

ISBN: (纸本)9798350359756

This study aims to evaluate the usage of three-dimensional (3D) magnetic resonance imaging (MRI) for determining the composition of athermanous plaques gift in the arterial partitions of sufferers. Atherosclerosis is a continual and innovative disease characterized by the build-up of lipid-wealthy plaques inside the arteries. It is the main reason for coronary artery disease and stroke. These plaques can rupture, mainly to a thrombus that could result in addition to damage to the arteries' partitions. The usage of 3D MRI for the assessment of extreme, huge plaques is high-quality in imparting critical data on the composition and, therefore, the stability of the plaque. This approach shows promise in assisting the choice-making system by imparting valuable statistics on plaques' dimensions, morphology, and traits. Furthermore, it can predict the development of arterial thrombosis and the want for open surgical tactics. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Survey on One-Shot Learning for Image Recognition using Machine Learning and Deep Learning Techniques

Survey on One-Shot Learning for Image Recognition using Mach...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Ganesh Vitthal Kadam Ghanshyam Prasad Dubey Computer Science Amity School of Engineering & Technology Gwalior Maharajpura Gwalior Madhya Pradesh ASET-CSE Amity School of Engineering & Technology Maharajpura Gwalior Madhya Pradesh

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Despite their success, the traditional deep neural networks are unable to rapidly generalize from a limited number of samples. To address the issue of large training data requirements with deep neural networks, one-shot learning restricting the number of data in each category is developed to attain enhanced results. Specifically, the traditional neural networks lead to over-fitting problems in training with a limited number of samples. In contrast, Machine Learning algorithms are capable of one-shot learning from limited samples like humans have the ability of oneshot learning. Hence, it is necessary to analyze one-shot object recognition techniques prevalent in image processing systems. This study will explore different one-shot learning techniques and methods from the state-of-the-art models and examine the merits and challenges of the approach with 25 literature papers. The effectiveness of the existing approaches will be evaluated by various techniques, datasets, and performance metrics are discussed in detail. At last, the paper discusses the potential future studies and challenges in achieving better detection accuracy.

关键词： Training Measurement Surveys Image recognition Accuracy Training data One shot learning Artificial neural networks Reliability Object recognition

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An ensemble learning-based framework for breast cancer prediction

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Decision Analytics Journal 2024年 10卷

作者： Sharma, Aman Goyal, Divyam Mohana, Rajni Department of Computer Science & Engineering Jaypee University of Information Technology Himachal Pradesh Solan 173234 India Department of Computer Engineering ASET Amity university of Punjab India

Breast cancer is a disease characterised by abnormal cell growth in the breast, with various types and unique characteristics depending on the malignant cells involved. It predominantly affects women, and researchers in the field of clinical sciences are increasingly interested in leveraging Artificial Intelligence (AI) techniques to predict and diagnose breast cancer. This study utilises the widely-used Wisconsin Breast Cancer Dataset (WBCD) from the University of California Irvine (UCI) machine learning repository. The dataset contains 30 features, including mean, standard error, and “worst” values, providing insights into different aspects of breast cancer. A stacked ensemble classifier is proposed to evaluate the effectiveness of the proposed model, incorporating multiple machine learning algorithms such as the Decision Tree Classifier, AdaBoost Classifier, Gaussian Naive Bayes (GaussianNB), and Multi-layer Perceptron Classifier (MLPClassifier). Various performance measures, including the Receiver Operating Characteristic Curve (ROC curve), Area Under the Curve (AUC), specificity, F1-score, sensitivity, and accuracy, are used to assess the model's performance. The results demonstrate that the proposed ensemble technique achieves an accuracy of 97.66%, surpassing the performance reported in existing literature. This highlights the superior performance of the developed model compared to previous approaches. The study underscores the potential of AI techniques and machine learning algorithms in advancing breast cancer prediction and diagnosis, offering promising avenues for improving clinical decision-making and patient outcomes. © 2023 The Authors

关键词： Artificial intelligence Breast cancer classification Breast cancer dataset Machine learning Predictive analytics Stacked ensemble method

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Data Augmentation: Synthetic Image Generation for Medical Images Using Vector Quantized Variational Autoencoders

Data Augmentation: Synthetic Image Generation for Medical Im...

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Disruptive Technologies (ICDT), International Conference on

作者： Chaitanya Singla Rajat Bhardwaj Nilesh Shelke Gurpreet Singh Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Computer Science and Engineering ASET Amity University Bengaluru Karnataka India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed) University Pune India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Artificial intelligence (AI) has revolutionized medical imaging, significantly improving diagnostic accuracy via the evaluation of X-rays, MRIs, and CT scans. However, the effectiveness of AI models is hindered due to high costs, privacy concerns, and challenges in acquiring large annotated datasets. Artificial data generation techniques, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and hybrid models like Disc-VAE, have gained prominence in dealing with these barriers. These techniques aim to augment datasets while preserving the complicated capabilities inherent to medical images. This paper compares the efficacy of Vector Quantized VAE with GAN and Disc-VAE using metrics Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR). The Vector Quantized VAE outperforms GAN and Disc-VAE in terms of SSIM and PSNR metrics. The findings spotlight the transformative capability of synthetic information augmentation for the medical system by enabling early and accurate disease diagnosis. These improved SSIM and PSNR metrics suggest that the generated images preserve essential diagnostic details, thereby enhancing the potential for early and accurate clinical diagnosis. It guarantees advanced performance and robustness of AI models, facilitating timely intervention and improved patient outcomes.

关键词： Measurement Accuracy PSNR Autoencoders Generative adversarial networks Data augmentation Vectors Data models Artificial intelligence Medical diagnostic imaging

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Effects of Gate Metal engineering on Electrical Characteristics of SOI MOSFET

Effects of Gate Metal Engineering on Electrical Characterist...

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IEEE Electron Devices Kolkata Conference (EDKCON)

作者： Sonam Khera Neetu Gupta Prashant Kumar Lalit Rai Neeraj Gupta Rashmi Gupta Department of Electronics Engineering. J. C. Bose University of Science and Technology YMCA Faridabad Haryana India Department of Electronics and Communication Engineering. ASET Amity University Haryana Gurgaon Haryana India Department of Computer Science & Engineering ASET Amity University Haryana Gurgaon Haryana India

ISBN: (数字)9798350374643

ISBN: (纸本)9798350374650

This paper explores the influence of gate metal engineering on the performance of Silicon -on -Insulator (SOI) MOSFETs. Conventional MOSFETs are prone to short-channel effects (SCEs), which degrade their performance as device sizes shrink. SOI MOSFETs provide improvements over conventional designs by minimizing SCEs. This study focuses on enhancing SOI MOSFET performance by introducing Dual Material Gate (DMG) technology. The DMG -SOI MOSFET demonstrates significant advantages in suppressing SCEs, particularly by reducing Drain -Induced Barrier Lowering (DIBL) and increasing drive current, which is beneficial for applications such as amplifiers. Through detailed simulations, this paper compares the electrical characteristics of single-gate (SG) and DMG SOI MOSFETs, revealing that the DMG structure offers superior performance.

关键词： Performance evaluation MOSFET Metals Voltage Logic gates Silicon Electron devices Electric fields Periodic structures

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Explainable Federated Learning Based Secure and Transparent Object Detection Model for Autonomous Vehicles

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IEEE Transactions on Consumer Electronics 2024年

作者： Devarajan, Ganesh Gopal Thirunavukkarasan, M. Mahalinghum, Maheswari Arockiam, Daniel Wu, Mu-En Mahapatra, Rajendra Prasad Delhi NCR Campus SRM Institute of Science and Technology Department of Computer Science and Engineering Faculty of Engineering and Technology Uttar Pradesh Ghaziabad201204 India Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore632014 India Sathyabama institute of science and technology School of computing Department of Computer science and engineering Tamil Nadu Chennai India CSE-ASET Amity University Madhya Pradesh India National Taipei University of Technology Taipei Taiwan

Intelligent Transportation Systems (ITS) and autonomous vehicles (AVs) are two fast-moving research fields. Understanding the surroundings and acting quickly is essential for AV control, especially in dynamic environments. Object detection algorithms must function properly under these demanding circumstances. Even though deep learning models are excellent at detecting objects, they are sometimes criticized for having unclear decision-making. Recently, explainable artificial intelligence (XAI) has improved these judgments' interpretability. This paper proposes a federated learning system that protects data privacy while enabling decentralized training across many AVs of an upgraded object detection algorithm, Yolov7-E. Grad-CAM visualization techniques are used to illustrate the deep learning models, and for easier interpretability, semantic data is fused with object detection. Enhanced security through federated learning, reducing data privacy risks by 40%. Based on comparative studies, it is demonstrated that the suggested model, which utilizes the federated learning paradigm, attains higher mAP and is appropriate for AV decision mechanisms while maintaining data privacy. © 1975-2011 IEEE.

关键词： Federated learning

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Exploratory Data Analysis of Titanic Survival Prediction using Machine Learning Techniques

Exploratory Data Analysis of Titanic Survival Prediction usi...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Anshika Gupta Deepak Arora Shivam Tiwari Dept. of Computer Science & Engineering ASET Lucknow Amity University Uttar Pradesh India Dept. of Computer Science & Engineering GL Bajaj Institute Technology & Management Greater Noida India

It is very important to find out the root causes of human tragedies in the past so that future crises can be avoided. The incident of 15 April 1912 is an example of a human tragedy in which around 1500 passengers and working staff lost their lives. Continuous research in today's time shows that if the proper statistical assessment was done then it would probably be possible that human devastation could be reduced. In today's era, many new and powerful technologies are available, with the help of which accurate statistical calculations can be done. In this research study, Titanic survivals have been studied based on machine learning techniques. In the study, out of the total entities, 891 entities have been used for training and 418 entities have been used for the test set and the comparative study of different machine learning algorithms gives importance to this research study.

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An Efficient Machine Learning Model for Identification of Seagrasses through Morphometrics

An Efficient Machine Learning Model for Identification of Se...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Ashwani Kumar Dubey Jibi G Thanikkal E. Dilipan Puneet Sharma Manoj Kumar Shukla Dept.of Electronics and Communication Engineering ASET Amity University Uttar Pradesh Noida UP India Dept.of Computer Science and Engineering ASET Amity University Uttar Pradesh Noida UP India Center for Marine Omics Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India School of Computer Science & AI SR University Warangal Telangana India

Seagrasses are marine flowering plants capable of completing their life cycle underwater. Globally, 72 species of seagrasses have been identified, including 15 species from India. Artificial intelligence/ Machine learning techniques are gaining wide use in many fields, and their applications in marine sciences are still in the initial stage. Considering this, a study is attempted to identify seagrass species through Artificial intelligence/ Machine learning techniques. Since the characteristics of seagrass vary depending on location and other environmental factors, identifying seagrass species is regarded as a difficult task. In the present study, a known seagrass species is taken as a reference and its leaf morphological features are considered for shape description and deep learning-based identification. Leaf morphometrics of seagrass species are drawn to develop the databases. Morphological feature extraction algorithm, descriptor generation algorithm, and deep learning-based identification are applied for species identification. Existing artificial intelligence/ machine learning models use image pixels as input to the deep learning model. However, this is the first attempt to recognize seagrasses through their morphological properties. Here, the leaf shape property is utilized for seagrass identification. The proposed machine learning model received 60% accuracy during testing. Additional investigation was possible with the same algorithm for future seagrass species identification. Utilizing Artificial intelligence/ Machine learning algorithms may lead to identifying new seagrass species.

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Prediction model using SMOTE, genetic algorithm and decision tree (PMSGD) for classification of diabetes mellitus

Prediction model using SMOTE, genetic algorithm and decision...

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作者： Azad, Chandrashekhar Bhushan, Bharat Sharma, Rohit Shankar, Achyut Singh, Krishna Kant Khamparia, Aditya Department of Computer Applications National Institute of Technology Jamshedpur India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Electronics & Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology NCR Campus Delhi- NCR Campus Ghaziabad India Department of CSE ASET Amity University Noida201301 India Bengaluru India School of Computer Science and Engineering Lovely Professional University Punjab India

Diabetes mellitus is a well-known chronic disease that diminishes the insulin producing capability of the human body. This results in high blood sugar level which might lead to various complications such as eye damage, nerve damage, cardiovascular damage, kidney damage and stroke. Although diabetes has attracted huge research attention, the overall performance of such medical disease classification using machine learning techniques is relatively low, majorly due to existence of class imbalance and missing values in the data. In this paper, we propose a novel Prediction Model using Synthetic Minority Oversampling Technique, Genetic Algorithm and Decision Tree (PMSGD) for Classification of Diabetes Mellitus on Pima Indians Diabetes Database (PIDD) dataset. The framework of the proposed PMSGD prediction model is composed of four different layers. The first layer is the pre-processing layer which is responsible for handling missing values, detection of outlier and oversampling the minority class. In the second layer, the most significant features are selected using correlation and genetic algorithm. In the third layer, the proposed model is trained, and its effectiveness is evaluated in the fourth layer in terms of classification accuracy (CA), classification error (CE), precision, recall (sensitivity), measure (FM), and Area_Under_ROC (AUROC). The proposed PMSGD algorithm clearly outperforms its counterparts and achieves a remarkable accuracy of 82.1256%. The best outcome achieved by the proposed system in terms of CA, CE, precision, sensitivity, FM and AUROC is 82.1256%, 17.8744%, 0.8070%, 0.8598, 0.8326 and 0.8511, respectively. The obtained simulation results show the effectiveness and superiority of our proposed PMSGD model and their by reduced error rate to help in decision-making process. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision trees

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