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Advancing Alzheimer's Nodule detection Through a Comprehensive Multi-Scene deep Learning Framework 10

Advancing Alzheimer's Nodule Detection Through a Comprehensi...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Premkumar, C. rajeshwari, r. remya, r. Cholaathiraj, M.S. reethika, A. Bharathidasan, S. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Vel Tech Rangarajan Dr Sagunthala R&d institute of science and Technology Department of Electronics and Communication Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350384369

determining the precise location of Alzheimer's nodules is essential for estimating the risk of brain cancer. Conventional CAd modules, including MrI, PET, and CT, struggle with feature extraction and segmentation due to time limits and complexity. This study proposes an efficient brain nodule detection technique based on the Multi-Scene deep Learning Framework (MSdLF) that makes use of the vesselness filter. A four-channel CNN model is developed using information from two image sequences to enhance radiologists' ability to recognise four-stage nodules. This adaptable model may be used in two different classes. The outcomes demonstrate how well the MSdLF performs while processing substantial volumes of image data for brain nodule recognition, improving with accuracy 98.86% and 98.45% of sensitivity by using decreasing false positives. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Advanced dual Module Weapon detection for Public Safety and Surveillance System 4

Advanced Dual Module Weapon Detection for Public Safety and ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sirigineedi, Manikanta Sowmiya, M. Hemavath, I.U. Bharathi, V.C. Karmode, Sayali S. Sathya, r. Vishnu Institute of Technology Department of Information Technology Bhimavaram Andhra Pradesh Vishnupur India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science and Engineering Avadi600062 India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Mahatma Gandhi Mission's College of Engineering & Technology Kamothe Maharashtra Navi Mumbai India Kongunadu College of Engineering and Technology Department of It Tamil Nadu Trichy India

ISBN: (纸本)9798331540364

This research presents a novel weapon detection system that combines state-of-the-art technologies for enhanced security. The system integrates a Convolutional Neural Network (CNN) for precise image-based weapon classification and the YOLO algorithm for real-time detection in live camera feeds. The proposed system generates email alerts to promptly inform users of detected weapons, enabling timely action to mitigate risks. By combining these advanced modules, the proposed system offers an effective solution for addressing weapon-related violence and improving security measures in various environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Artificial Intelligence Model for Classification of Sensitivity data Utilized at Industrial Applications

Artificial Intelligence Model for Classification of Sensitiv...

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2022 International Conference on Electronic Systems and Intelligent Computing, ICESIC 2022

作者： Mohammad, Ashraf Sree, Mupparaju Kavya dheeraj Pedapalli, Saam Prasanth raveendra, Muddineni Kumar, T. Yaswanth Pavan National Ilan University Department of Electronic Engineering Yilan City Taiwan Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India Wistron Corporation Failure Data Analysis Engineer Hsinchu City Taiwan

ISBN: (纸本)9781665483858

Every electronic gadget in the structure has the potential to cause ignite, whether through a short circuit or another means which is also the primary cause of fire accidents. Smoke detectors are used to save the lives of causalities during hazards. In the manufacturing industry of smoke detectors, around 12 percent of products are getting failed due to the parameters of sensitivity criteria. This paper provides an approach to improve the success rate sensitivity data of the products as an industrial application using a sequential model provided by the tensor flow. Model accuracy and loss were results compared at different epochs. The final artificial intelligence model was developed with a minimum number of layers and requires less computational time. This paper helps the industry to improve product quality and efficiency. © 2022 IEEE.

关键词： Tensors

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Efficient diabetes detection using Hybrid Machine Learning Model

Efficient Diabetes Detection using Hybrid Machine Learning M...

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2024 International Conference on distributed Systems, Computer Networks and Cybersecurity, ICdSCNC 2024

作者： Gowri, A.S. Jose, P. Karthik, S.M. Shaheema, S Berlin Karuppasway, K.M. Balamurugan, r. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Department for Applied Research Gandhigram India Stella Mary's College of Engineering Department of Artificial Intelligence and Data Science Azhikal India Bharath Institute of Higher Education and Research Department of Mathematics and Statistics Chennai India Dmi College of Engineering Department of Information Technology Chennai India

ISBN: (纸本)9798350375442

diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with rBF function and achieve the accuracy as 98,04%. © 2024 IEEE.

关键词： Adversarial machine learning

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Towards Seamless Connectivity: Implementing 6G Communication Technologies in Next-Generation Networks 3

Towards Seamless Connectivity: Implementing 6G Communication...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Kalaiselvi, K. Sankar, r. Supriya, S. Kaushik, G. Swamy, Hemanth devika, M. Saveetha College of Liberal Arts and Sciences SIMATS Department of Data Analytics Tamilnadu Chennai India Chennai Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India MLR Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Motorola Solutions United States Dhanalakshmi College of Engineering Department of AI& DS Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

Advanced wireless networks are essential given the increase in demand for smart services enabled by the Internet of Everything (IoE). despite the fact that 5G networks could enable Internet of Everything (IoE)-based services, they can't meet all of the requirements needed to create intelligent applications. In order to overcome the shortcomings of the current infrastructure, there is an increasing demand to investigate the potential of 6G wireless communication technologies. Complex network optimization problems can be resolved by integrating artificial intelligence into sixth generation networks. An increasing user base and support for large-scale data-driven applications are essential given the fast evolving 6G landscape. In addition to highlighting network needs, critical supporting technologies, and a comparison of 5G and 6G networks, this study looks at recent advancements and trends in 6G technology. The report also explores new approaches to 6G connectivity, such as edge computing, backscatter communications, AI-driven IoT networks, and holographic beamforming, all of which are meant to support intelligent communities. In order to achieve the goal of 6Gbased IoT networks, it concludes by outlining potential research avenues. © 2024 IEEE.

关键词： Internet of Everything

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Baby Physical Safety Monitoring in Smart Home Using Action recognition System

Baby Physical Safety Monitoring in Smart Home Using Action R...

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IEEE Southeastcon

作者： Victor Adewopo Nelly Elsayed Kelly Anderson School of Information Technology University of Cincinnati Cincinnati OH Corporate R&D Data Science & AI Procter & Gamble (P&G) Cincinnati Ohio United States

Humans are able to intuitively deduce actions that took place between two states in observations via deductive reasoning. This is because the brain operates on a bidirectional communication model, which has radically improved the accuracy of recognition and prediction based on features connected to previous experiences. during the past decade, deep learning models for action recognition have significantly improved. However, deep neural networks struggle with these tasks on a smaller dataset for specific Action recognition (Ar) tasks. As with most action recognition tasks, the ambiguity of accurately describing activities in spatial-temporal data is a drawback that can be overcome by curating suitable datasets, including careful annotations and preprocessing of video data for analyzing various recognition tasks. In this study, we present a novel lightweight framework combining transfer learning techniques with a Conv2d LSTM layer to extract features from the pre-trained I3d model on the Kinetics dataset for a new Ar task (Smart Baby Care) that requires a smaller dataset and less computational resources. Furthermore, we developed a benchmark dataset and an automated model that uses LSTM convolution with I3d (ConvLSTM-I3d) for recognizing and predicting baby activities in a smart baby room. Finally, we implemented video augmentation to improve model performance on the smart baby care task. Compared to other benchmark models, our experimental framework achieved better performance with less computational resources.

关键词： deep learning Pediatrics Adaptation models Computational modeling Smart homes Predictive models Feature extraction

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Twitter Spam detection Using Word Vector and Binary Classifier

Twitter Spam Detection Using Word Vector and Binary Classifi...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Marimuthu, ramaprabha Muthulingam, Gurusigaamani Ayyanar Vinoth, N.A.S. Ganesan, Sivasathiya Ponnuchamy, Kardeepa Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India Easwari Engineering College Department of Artificial Intelligence and Data Science Chennai Ramapuram India

ISBN: (纸本)9798350300857

In recent years, social media communication plays an important role in sharing messages, thoughts, comments and ideas around the world. Among them, Twitter is one of the most popular communication mediums. Twitter provides free services for sharing information's that are limited to 140 characters. Each month nearly 40 million new twitter accounts were created. Since usage of twitter increases rapidly, fake accounts were created for posting the spam messages and links for redirecting to phishing websites. researchers proposed various machine learning methodologies for filtering the spam to handle and maintain the social network security problems. But still detecting spam tweets, malicious link in real world scenario is still a complex task. In the proposed work, a novel deep learning technique is introduced to address the above mention issues. Word Vector model is used for learning the syntax of each tweet. Later by using proceeding representation data set, binary classifier is developed. For experimental purpose 1-month twitter datasets are collected. Proposed work outperforms when compared with various existing methodologies and also to detect the non-text based as well. The ratio of spam is determined using the precision, recall, and F-measure metrics. © 2023 IEEE.

关键词： Classification (of information)

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Advanced Wallis: A domain adaption method based on remote sensing image homogenization technique

Advanced Wallis: A domain adaption method based on remote se...

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Machine Vision, Image Processing and Imaging technology (MVIPIT), International Conference on

作者： Hui Wang Mengdie Zhou Panpan Tang Bo Zhao Research Center of Big Data Technology Nanhu Laboratory & Advanced Institute of Big Data Jiaxing & Beijing China Remote Sensing Platform R&D Center KQ GEO Technologies Co. Ltd Beijing China

ISBN: (数字)9798350306545

ISBN: (纸本)9798350306552

domain adaption has been a hot topic in artificial intelligence (AI) -driven understanding tasks. Simultaneously, as a critical technology in image preprocessing, the homogenization method plays an irreplaceable role in remote sensing interpretation. This paper proposes a domain adaptation method based on the Wallis homogenization algorithm to improve the reasoning performance of deep models, which is named advanced Wallis. Its core idea is to reduce the variability of intra-class features by reducing the hue difference between the source and target domains. The method was tested on three publicly available datasets and yielded an average improvement of 2.3% in Fl scores. A convenient implementation of the method is provided to make sure it does not consume additional computational resources and storage space.

关键词： Training Adaptation models Computational modeling Machine vision Imaging Stability analysis Inference algorithms

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Autoregressive diffusion Networks for Trustworthy Node Identification and Optimized routing in Wireless Sensor Networks

Autoregressive Diffusion Networks for Trustworthy Node Ident...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： M. Satheesh Kumar P. Ganesh Kumar S. Amudha T. Kavitha P. ravikumar M. raghini Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of IT K.L.N. College of Engineering Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Instrumentation K.L.N. College of Engineering Department of Artificial Intelligence and Data Science Velammal College of Engineering and Technology

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Finding and eliminating unwanted nodes is a foremost challenge in wireless sensor networks (WSNs). This paper proposes an Autoregressive diffusion Network (AdN) to find and categorize the nodes in the network as affected or trustworthy. Here, the proposed AdN provides a strong foundation for finding uncommon or insecure actions by effectively recording the characteristics of the sensor over a period of time. Thus, using the capability to capture successive data. In addition, an improved routing mechanism is developed using the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocols. Then, the Pelican Optimisation Algorithm (POA) is used to optimize the cluster heads optimally. To further improve the routing mechanism using fitness proportional selection technique to transfer the data safely and effectively. Simulation results show that the proposed technique enhances the performance by reducing energy consumption and increasing the detection accuracy of affected nodes. The proposed LEACH + POA + AdN model shows the improved detection accuracy of 77%, false positive rate of 7.50%, average energy consumption of 8.02 J, network lifetime of 92.94 rounds, and packet delivery ratio of 92.97%, which is high compared to other benchmark models.

关键词： Wireless sensor networks Energy consumption Accuracy Protocols Simulation Clustering algorithms routing Energy efficiency Security reliability

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Oral Lesion Cell Segmentation and Classification using Convolution Neural Network Technique 3

Oral Lesion Cell Segmentation and Classification using Convo...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Amutha, J. Priyadarsini, S. Prasanth, S. Senkumar, M.r. ramnath, M. Jasperline, T. P.S.R Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India Unnamalai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Kovilpatti India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam India Dr.G.U.POPE College of Engineering Sawyerpuram Department of Computer Science and Engineering Tamil Nadu Tuticorin India

ISBN: (纸本)9798350384598

Oral Cancer of the mouth kills millions of people, Oral cancer is better treated and more often survived when caught early. Convolutional neural networks (CNNs) have recently shown remarkable promise for cancer diagnosis and other medical image processing applications. In this study, we train deep convolutional neural networks (CNNs) to detect oral cancer early. The suggested approach makes use of a large database of pictures of the mouth and its cavities. develop and refine a convolutional neural network (CNN) model using the use of radiographs, histology slides, and intraoral images. Oral carcinogenic tumours, both precancerous and malignant, may be identified and classified automatically using this approach. The process begins with picture pre-processing, continues with dataset supplementation for better model generalizability, and culminates in the use of complex neural networks for feature extraction and learning. Transfer learning methods modify previously taught models for this medical imaging task. The system's performance, sensitivity, and accuracy are evaluated using a set of images that have pre-existing diagnoses. deep learning CNNs were able to identify oral cancer with an efficiency and accuracy of 93.62%, according to the research. The trained model is a valuable tool for early cancer screening because of its high sensitivity and specificity. By automating the process, oral cancer may be detected and treated more quickly, which improves patient outcomes and decreases the disease burden. © 2024 IEEE.

关键词： Convolutional neural networks

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