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Video Anomaly Detection using Factorized Self-Attention Transformer 2

Video Anomaly Detection using Factorized Self-Attention Tran...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Karthik, R. Adithya, S. Shalmiya, P. Subramaniyaswamy, V. School of Computing SASTRA Deemed University Thanjavur India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798331504106

ISBN: (纸本)9798331504106

Recently, video anomaly detection has seen significant interest in computer vision for identifying unexpected events in video streams using computational cameras, which has quite important applications in video surveillance, security, safety, and industrial control. Existing methods heavily rely on handcrafted features and rule-based algorithms to detect such anomalies, but these models typically tend to fail to generalize scenarios. Recently, with the success of deep learning-based approaches, particularly deep convolutional and recurrent neural networks, state-of-the-art models have been proposed to exploit the capability of deep learning to learn complex patterns from raw video without handcrafted features. Although deep neural networks have been shown to be the suitable solution to automatically capture complex relationships, there are still issues with addressing a wide spectrum of scenarios and acquiring large, well-labelled training datasets. In this research, a transformer-based architecture is proposed for anomaly detection using labelled training data. A Video Vision Transformer (ViT) is used to extract complex visual patterns from video frames, while audio data is encoded as spectrograms and transformed into audio features using an audio transformer. The outputs of the transformers are fused using a cross-modal relation-aware network and fed to a robust temporal feature magnitude (RTFM) learning module, which provides an anomaly score and classifies the input as anomaly or normal. Furthermore, different evaluation metrics are used to measure how well the model works, highlighting its ability. The proposed model is effective since transformer architectures are capable of grasping rich temporal relationships. ©2024 IEEE.

关键词： Recurrent neural networks

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Unlocking the Power of Long Short-Term Memory Networks: A Text Classification Approach 14

Unlocking the Power of Long Short-Term Memory Networks: A Te...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Pandiaraj, A. Ramshankar, N. Venkatesan, R. SRM Institute of Science and Technology Computing Technologies School of Computing Chennai India RMD.Engineering College Computer Science and Engineering Tamilnadu Chennai India Bannari Amman Institute of Technology Information Technology Tamilnadu Erode India

ISBN: (纸本)9798350335095

Text classification typically suffers from the fundamental issue of latter-day data with a very high dimension, which hinders the model developed because training time grows exponentially with the number of features used and model risk of overfitting increases with increasing number of features. Since the recurrent structure is ideal for processing long varied texts, recurrent neural networks (RNNs) are among the most often used designs in natural language processing (NLP). RNN using the Long Short-Term Memory (LSTM) architecture is one of the deep learning techniques suggested in this study. In natural language processing for English, one element stores the subject's gender, while another element records whether the subject is singular or plural. Long Short-Term Memory (LSTM) supplies elements that are intended to be able to record an input characteristic. LSTM will discover these properties throughout the training procedure. The results of this study are most accurate when variable word sequence characteristics are taken into account. © 2023 IEEE.

关键词： Long short-term memory

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Pancreatic Cancer Prediction Machine Learning using Multi Fusion Model

Pancreatic Cancer Prediction Machine Learning using Multi Fu...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sathiyapriya, V. Devi, K. Nethra Deepa, S. Marimuthu, M. Manikandan, P. Prabu, S. Knowledge Institute of Technology Department of Computer Science and Engineering Kakapalayam India Gnanamani College of Technology Department of Artificial Intelligence and Data Science Namakkal India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Tiruchirappalli India

ISBN: (纸本)9798350375442

Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. Various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches. © 2024 IEEE.

关键词： Computerized tomography

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Skin Cancer Classification using A Hybrid Convolutional Neural Network with SVM Classifier

Skin Cancer Classification using A Hybrid Convolutional Neur...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Chatrapathy, K. Prasad, M Lakshmi Kiran, Ajmeera Shaker Reddy, Pundru Chandra Babu, G Charles Partheeban, N. Reva University School of Computing & Information Technology Karnataka Bengaluru India Institute of Aeronautical Engineering Computer Science and Engineering Telangana Dundigal India Mlr Institute of Technology Computer Science and Engineering Telangana Dundigal Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Gokaraju Rangaraju Institute of Engineering and Technology Computing Science and Engineering Telangana Hyderabad India Galgotias University School of Computing Science and Engineering Utter Pradesh Greater Noida India

ISBN: (纸本)9798350308167

Digital dermoscopy is commonly employed by dermatologists for the diagnosis of malignancy. Clinicians' recognition of tumor is not only highly subjective, but also highly dependent on their level of experience. The inherent inter-operator unevenness in the private processing of dermoscopy pictures calls for fully automated computer-aided diagnosis(CAD) methods. The fine-grained differences in the manifestation of these abrasions on the skin-surface make automated skin lesion classification difficult. In several different types of fine-grained object classifications, deep convolutional-neural-network(CNN) has demonstrated excellent reparability. In order to categorize dermoscopy pictures into benign or malignancy lesions, this research introduces two innovative hybrid CNN strategies that use a support-vector-machine (SVM) at the output layer. In order to classify data, the characteristics mined by the first &second CNN strategies are combined and supplied to the SVM. The suggested model's efficacy is measured against a reference set of labels provided from a dermatology specialist. In a study using CAD for skin disease categorization, the generated CNN-segmented image folder distinguished atopic dermatitis from nasty diseases and obstacles like mycosis fungoides, impetigo, and herpesvirus contagion with a sensitivity and specificity of around 98%. In comparison to the traditional CNN strategies, the proposed models performed better on the ISBI 2016 public dataset. Both of the proposed models, with an accuracy of 88.02% and 87.43%, outperformed the standard CNN models. Reduced reliance on physician annotation and enhanced CAD performance are both benefits of our CNN segmentation algorithm, which automatically removes lesions and segments images of the skin area across all image fields. © 2023 IEEE.

关键词： CAD Convolutional-neural-network deep learning dermoscopy Skin cancer SVM

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Interpretation of Electroencephalogram using RNNs to Predict Cognitive Disorders 3

Interpretation of Electroencephalogram using RNNs to Predict...

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

作者： Moovendran, Manikandan Surendran, S. Midhun, C. Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India SRM Institute of science and technology Chennai603203 India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532420

EEG signals are very useful in giving an insightful view of what's going on inside the brain. However, such readings are still tricky and challenging to interpret because of the inherent non-linearity and dynamic behavior of the data. The present paper introduces a new approach to the analysis of EEG signals using Recurrent Neural Networks, with a particular focus on recognizing temporal dependencies that exist in sequential data. By exploiting the RNN property of information retention at different time steps, we demonstrate enhanced accuracy in pattern recognition and classification of cognitive states. Our model is trained over a massive dataset of EEG recordings using the LSTM units to get rid of vanishing gradients frequently encountered in deep-learning models. The results from the experiments are given below and illustrate how the proposed method compares with traditional machine learning techniques in terms of accuracy and robustness. It is a promising tool for all applications in neurological diagnosis, cognitive research, and developing a brain-computer interface. © 2024 IEEE.

关键词： Recurrent neural networks

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Game Therapy for Specially-Abled Individuals with PPO Reinforcement Learning in VR-based Educational Games 5

Game Therapy for Specially-Abled Individuals with PPO Reinfo...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Joypriyanka, M. Surendran, R. Sathish Kumar, P.J. Sivasangari, A. Saveetha Institute of Medical and Techinical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Sathyabama Institue of Science and Technology School of Computing Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

This research study presents a new application of reinforcement learning (RL), specifically Proximal Policy Optimization (PPO), in creating immersive virtual reality (VR) games aimed at aiding skill acquisition and emotional regulation for individuals with anger management difficulties. People with Down syndrome, Autism Spectrum Disorder, Cerebral Palsy (CP), Intellectual Disabilities (ID), and Stroke Survivors are mainly considered. The study examines how PPO-trained agents improve cognitive, social, and emotional regulation. Techniques include thorough hyperparameter optimization for the PPO algorithm with two sets. Iterative agent training in Unity and AWS Sagemaker uses cloud computing resources for parallelized training. Emotional regulation and positive behaviour change are assessed empirically and through user studies. Virtual reality (VR) cooperative RL games may offer comprehensive treatments for anger management, according to the results, which show promising gains in emotional regulation and teamwork. By enhancing interest and effectiveness in satisfying emotional regulation demands, the study demonstrates that AI-driven therapies and "gamified therapy,"which mixes game components into therapy, can revolutionize mental health. © 2024 IEEE.

关键词： Reinforcement learning

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Smart GPS Based Vehicle Speed Limit Controller on zone identification using Geo-Fencing Algorithm 3

Smart GPS Based Vehicle Speed Limit Controller on zone ident...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Upendra, Modem Reddy, Varun Safa, M. College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Networking and Communications Kattankulathur Chennai603203 India

ISBN: (纸本)9798350381931

In light of growing concerns about road safety and the surge in traffic accidents, this project aspires to an Internet of Things smart GPS-based vehicle speed limit controller system. The primary objective is to elevate road safety by harnessing real-time data and automated control mechanisms to enforce speed limits, thereby diminishing incidents of speeding-related accidents. This project adopts a comprehensive approach, integrating both hardware and software components. Vehicle-mounted sensors, encompassing speed sensors and GPS receivers, continually gather real-time data on vehicle speed and location. Furthermore, road condition data is sourced from multiple outlets, such as weather stations and traffic cameras. This data is wirelessly transmitted to a central control unit, which processes and analyzes it. The control unit deploys machine learning algorithms to evaluate vehicle speed limits against predefined speed limits for specific road segments. Depending on this analysis, it transmits control signals back to the vehicle when necessary. These signals can encompass driver warnings, power limitations, or autonomous speed control. These methodologies collectively enabled the creation of an intelligent and adaptable GPS-based vehicle speed control system, offering the potential to significantly enhance road safety by dynamically enhancing road safety regulating vehicle speeds in different zones, ensuring the safety of people and drivers alike. The Smart GPS-based vehicle speed limit controller system has undergone successful development and testing, both in controlled environments and on public roads. The outcomes manifest a remarkable reduction in incidents of speeding and an enhanced adherence to speed limits, exhibiting an accuracy level exceeding 96%. In real-world scenarios, the system effectively mitigates speeding-related risks, leading to a substantial decline in traffic accidents. Its ability to adapt to changing road conditions and. Its scalability ren

关键词： Internet of things

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Design of Area Effcient Multiplier Using High Speed 15-4 Counter 9

Design of Area Effcient Multiplier Using High Speed 15-4 Cou...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Vasan, J.K. Shettygari, Sudeshna Hariprasad, S. Maria Dominic Savio, M. Srm Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377972

The high-speed counters are the building blocks to reduce the partial products, increasing the efficiency and speed of the multiplier. This paper presents an innovative approach to designing a 16 × 16 multiplier using a 15-4 counter that utilizes cross-wired 1ogic(CWL) based 3-2 counter instead of traditional full adders. A 15-4 counter is designed by employing a 5-3 counter and a CWL-based 3-2 counter to reduce circuit complexity. Using multiple 5-3 counters instead of conventional full adders reduces the computational effort, strategically integrating these 15-4 counters within specific multiplier columns, achieving greater area efficiency and speed. This method addresses the high computational demands associated with adders, optimizing both performance and area in high-speed multiplier applications. From the simulation result, it can be inferred that the proposed 15-4 counter and cross-wired logic 3-2 counter has excellent efficiency, and the power and delay product(PDP) has reduced by 3.6 % when compared to the existing 15-counter design and 30.27 % decreased in the 3-2 counter design © 2024 IEEE.

关键词： Logic design

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RecipeLens: Revolutionizing Meal Preparation with Image-Based Ingredient Detection and Recipe Suggestions

RecipeLens: Revolutionizing Meal Preparation with Image-Base...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Prajapati, Abhijit Nigam, Muskan Priyanka, R. School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications TamilNadu Kattankulathur603203 India

ISBN: (纸本)9798350375237

In today's fast-paced lifestyle, the importance of preparing meals at home has significantly increased due to the high costs and time constraints associated with dining out. However, while home-cooked meals offer numerous benefits, such as cost-effectiveness and convenience, maintaining variety in one's diet is crucial to preventing culinary monotony. Contrary to common belief, creating diverse recipes does not necessarily require an extensive array of ingredients;rather, innovative combinations and techniques can transform familiar ingredients into exciting culinary experiences. This research paper introduces RecipeLens, a novel approach that combines food ingredient detection with recipe suggestions to streamline the cooking process and enhance culinary creativity. Building upon existing studies in food ingredient detection and recipe recommendation, RecipeLens integrates both functionalities into a unified platform, offering users a seamless and efficient way to discover and prepare diverse dishes using available ingredients. To expedite ingredient classification, RecipeLens employs the MobileNet_v2 model, renowned for its accuracy and efficiency in image recognition tasks. Additionally, recipe suggestions are sourced from the Food Recipe API, ensuring the validity and relevance of each recommended dish. Furthermore, RecipeLens accommodates users with limited knowledge of fruit and vegetable names, making it accessible to individuals of varying culinary expertise. Through the integration of ingredient detection and recipe recommendation, RecipeLens empowers users to unleash their culinary creativity, discover new dishes, and enjoy diverse homemade meals with ease and efficiency. © 2024 IEEE.

关键词： Image classification

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IoT Based Monitoring System for Pesticide Residue in Fruits and Vegetables 5

IoT Based Monitoring System for Pesticide Residue in Fruits ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Karri, Varun Kumar Prasath, N. School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Chennai Tamilnadu Kattankulathur India

ISBN: (纸本)9798331518097

This research study presents the design of an IoT-based system that detects pesticide residues in fruits and vegetables. Enhancements in food safety status decrease the health hazards attributed to chemicals used in agriculture. The ESP32-CAM captured images are sent over to cloud storage for further analysis with the aid of advanced image processing techniques in MATLAB as well as CNNs for pesticide detection and quantification. The IoT platform enables real-time monitoring with alert notifications using a database for reference. The performance of the system with three machine learning models, KNN, and CNN was tested. Among those, CNN performed the best perfectly with accuracy at 0.9999% and the specificity is also obtained at 0.9997% where specificity is found better compared to KNN and XGBoost. This scalable precise solution opens up a promising avenue for improvement in food quality and safety, with CNN being its best real-time pesticide residue detection model because of its very high sensitivity and very low false positives. © 2024 IEEE.

关键词： Fruits

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