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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kanimozhi, K.V. Neelaveni, P. Seethalakshmi, K. Rao, N.Venkatesvara Prabhu, M. Naganathan, Suresh Babu Thandullu Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Information Technology Chennai Thandalam India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India J.N.N.Institute of Engineering Department of Computer Science and Engineering Kannigaipair Chennai India Mohamed Sathak College of Arts and Science Department of Computer Science Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This paper develops the contemporary role of real-time analytics combining IOT-enabled smart grid systems in order to improve energy efficiency, strengthen grid dependability and privacy sustainability. With help of the set of sensors data analyzed in real-time coming from the smart grid environment, the study will be focused on the energy consumption patterns, demand response efficiency, decision-making ability of predictive maintenance and security threats' detection rate. The findings emphasize a pattern of energy usage which reflects the current scenario as well as the requirements to develop demand response initiatives that are effective in stabilizing energy excesses during peak demand and encourage energy economizing. Firstly, the implementations of predictive maintenance confirm that real-time data analytics effectively sets proactive maintenance appointments thereby, scheduling minimal on-and-offs leading to extended life-span of the equipment. Also, the ripple effect of the recognized cybersecurity threats emphasizes the need for an ongoing monitoring and a substitute of the strong-security system for smart grid infrastructure. This study, therefore, spotlights the importance of current data analytics in headway of smart grid systems' energy management and sustainability solutions, thus urgently calls for new and improved technologies advancement in smart grids and mitigating emerging energy problems. © 2024 IEEE.

关键词： Energy efficiency

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Integrating DCCS-Net for High-Fidelity Watermarking and Tampering Detection in Brain Scan Images 6

Integrating DCCS-Net for High-Fidelity Watermarking and Tamp...

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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Ahamed, Bsh. Shayeez Baskar, Radhika Nalinipriya, G. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science & Engineering Chennai India Madanapalle Institute of Technology & Science Department of CSE-AI&ML Madanapalle India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Information Technology Tamilnadu Chennai India

ISBN: (纸本)9798331505790

This study presents a new approach that combines the Deep Cascade Compressed Sensing Network (DCCS-Net) with a watermarking and tampering detection framework to improve the accuracy and quality of medical images. The system analyzes brain scan images by incorporating watermarks and identifying any tampering, thereby guaranteeing a high level of accuracy and maintaining the structural resemblance to the original images. The essential steps involve preprocessing the image using Discrete Cosine Transform (DCT), employing compressed sensing for efficient data management, and embedding watermarks using binary quantified reference values. The evaluation metrics, namely Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), indicate that the watermarked images exhibit a PSNR of 41.906 and SSIM of 0.98458. These values suggest that there is minimal degradation in the quality of the images. The tampering detection process achieves a precision of 0.94531, effectively identifying tampered blocks. The reconstructed images exhibit minimal degradation in quality, as evidenced by a PSNR of 36.21 and SSIM of 0.9789. The results highlight the ability of DCCS-Net to effectively manage noise, minimize artifacts, and maintain intricate details, thereby improving the accuracy and reliability of diagnostics. The graphical depiction of metrics provides additional evidence to support the strength and reliability of the methodology in preserving image quality and reconstructing manipulated images, thus ensuring the trustworthiness and reliability of medical image analysis. © 2024 IEEE.

关键词： Discrete cosine transforms

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Design Amharic Text Sentiment Analysis Model Using Machine Learning Techniques. in Case of Restaurant Reviews 5

Design Amharic Text Sentiment Analysis Model Using Machine L...

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5th International Conference on information and Communication Technology for Development for Africa, ICT4DA 2023

作者： Gedif, Birku Alemu, Abebe Assefa, Yibeltal Nibret, Shegaw College of Informatics University of Gondar Information Science Ethiopia College of Informatics University of Gondar Computer Science Ethiopia Kombolcha Institute of Technology Wollo University Software Engineering Ethiopia Bahir Dar Institute of Technology Bahir Dar University ICT4D Research Center Ethiopia

ISBN: (纸本)9798350328486

Sentiment analysis is a type of natural language processing for tracking the attitude of the public about a particular product, service or topic. It is also highly challenging as natural language processing research topic, and covers many novel sub-problems. Now business organizations and academics are putting forward their efforts to find the best system for sentiment analysis. The focus of this study was an Amharic unstructured restaurant review on the web. The objective of the paper was to design Amharic text sentiment analysis model using supervised machine learning techniques and evaluate the performance of classifiers. This paper explored the supervised machine learning classification approaches (naive Bayes, support vector machine and k-nearest neighbor) with different feature selection schemes to obtain a sentiment analysis model for domain specific restaurant review dataset at sentence level. The proposed model has the following components: Data preparation, preprocessing such as tokenization, normalization, filter stop words, feature extraction and selection to prepare feature vector, polarity classification. Performance analysis carried out on classifiers, based on n-grams proposed. From the results of the experimental studies, all algorithms are known to be highly effective classifiers, and are able to achieve good accuracy in this experiment. The experiments show that Term frequency (TF) and the TF-IDF scheme gives maximum accuracy 80.43 % and 79.49 % respectively for SVM in bigram features. Term frequency and term occurrence also give maximum accuracy 78.37 % and 78.00 % respectively for Naive Bayes classifier at bigram features. TF-IDF also give maximum accuracy 78.00% for KNN at 4-gram. The challenge was opinion holders sometimes give objective text to express their opinion, but the classifier did not identify those facts from opinions. These kinds of complexities of natural languages make sentiment mining systems more challenging and to resolving th

关键词： Sentiment analysis

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Support Vector Machines Based Predictive Seizure Care using IoT-Wearable EEG Devices for Proactive Intervention in Epilepsy 2

Support Vector Machines Based Predictive Seizure Care using ...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Srinivas, Porandla Arulprakash, M. Vadivel, M. Anusha, N. Rajasekar, G. Srinivasan, C. Malla Reddy Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chenagalpattu Tamil Nadu Chennai India Vidya Jyothi Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Vellore Institute of Technology School School of Computer Science and Engineering Department of IoT Tamil Nadu Vellore India Vinayaka Mission's Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Epilepsy, a neurological illness that causes repeated seizures, can interfere with everyday life and needs prompt treatment. Internet of Things (IoT) wearable Electroencephalogram (EEG) devices and Support Vector Machines (SVM) for predictive analytics are used in this study to suggest a unique strategy for proactive seizure treatment. Wearable EEG devices feed real-time brain activity data. It uses SVM, a strong machine learning method, to build a prediction model using historical EEG data to identify and predict seizures. The prediction software analyses EEG data in real-time to detect pre-seizure patterns and initiate preventive treatments. The seizure prediction method uses SVM's capacity to handle high-dimensional data and catch complicated patterns to improve accuracy and reliability. Healthcare practitioners and caregivers may get timely warnings and react efficiently thanks to the IoT infrastructure's seamless connectivity between wearable devices and a centralized monitoring system. It discusses the ethical and privacy issues of installing such a system, stressing user permission and data protection. Pilot investigations show promising prediction accuracy and reaction time. SVM with IoT-wearable EEG sensors for predictive seizure care offers a forward-looking technique for enhancing epilepsy patients' quality of life by enabling individualized and proactive treatments. © 2024 IEEE.

关键词： Electroencephalography

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Intelligent Irrigation Mechanism to Enhance The Sensor Network Topology by Using Machine Learning And Artificial Intelligence 2

Intelligent Irrigation Mechanism to Enhance The Sensor Netwo...

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2nd IEEE International Conference on Advances in information Technology, ICAIT 2024

作者： Sriram, K.P. Anbalagan, E. Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sujatha, P. Kola Assistant Professor Information technology St.Joseph's Institute of Technology OMR Chennai – 600119 Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattur OMR Siruseri Chennai Associate Professor Department of Information Technology MIT Campus Anna University Chennai Tamil Nadu India – 600044

ISBN: (纸本)9798350383867

This research paper is about the effectiveness of a fusion of machine learning and artificial intelligence into smart irrigation systems in terms of both safety and benefit of the environment and the economy in farming. We've experienced a intensive process of experiment and application of this succeeded method in a real world scenario and we've also detailed the performance of the dynamic irrigation scheduling and the adapter sensor network optimization. Results indicate a significant improvement in key metrics: scheduling was done dynamically which on average was a 10% rise in crop yield and 15% water saving, on the other hand, sensor network optimization with adaptive technology resulted into a 15% increase in cover area and 20% improvement achieved in data transmission reliability. Furthermore, the introduction of the smart irrigation system got impressive results signified by a 10 kg more production per hectare, irrigation efficiency that rose from 70% to 85%, and saving 20% of the water consumed. This calls to mind the capability of cutting-edge technologies to contribute to innovative water management paradigms, improved resource use, and security in food production in face of unpredictable climatic variability. ©2024 IEEE.

关键词： Adversarial machine learning

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Integration of Clinical and Imaging Data for Enhancing Oral Cancer Detection Using Deep Learning

Integration of Clinical and Imaging Data for Enhancing Oral ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Shakila, M. Dhasaratham, M. Saranya, N. Naga Thotapalli, Aruna Telrandhe, S.V. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Tkr College of Engineering and Technology Department of Information Technology Hyderabad India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India Cvr College of Engineering Department of Information Technology Telangana Hyderabad India Guru Nanak Institute of Engineering & Technology Department of Etc Nagpur India

ISBN: (纸本)9798350360660

This study aims at unlocking the integration of clinical and imaging data using the deep learning model for enhancing the diagnosis of oral cancer cases. Therefore, for image analysis, in this paper proposed to major on CNNs while for clinical data, our Research used RNNs and hence came up with a unique architecture. The proposed model was trained on the patients' records and Histopathological Images Database from 5000 patients. As for the overall measure of accuracy of all of the constructed classifiers applying our integrated model the result was 94 %. Specificity achieved for the system was 7% while sensitivity was 93% for the oral cancer. 2%, sensitivity of 69.1%. This is an improvement of the previous methods that required either imaging or clinical data to obtain accuracy approximated at 85-90 percent. As a unique effectiveness factor of increasing the accuracy of oral cancer diagnosis, it is proposed to use an integration of clinical and imaging data through deep learning, which can also result in inclusion of the diagnosis at the early stages of the disease, thereby enhancing the prognosis of the illness. © 2024 IEEE.

关键词： Lung cancer

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Cloud-Enabled Security for Luggage Protection through LSTM Networks and IoT in Contactless Travel 8

Cloud-Enabled Security for Luggage Protection through LSTM N...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sairam, A. Fathima, S. Shafla Prabakaran, T. Murugan, M. Pavithra, B. Mohankumar, N. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Joginpally B.R. Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Srm Institute of Science and Technology Faculty of Management Tamil Nadu Chennai India Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India Symbiosis Institute of Technology Nagpur Campus Maharashtra Pune India

ISBN: (纸本)9798350376425

In contactless travel, luggage security is very crucial. A new luggage protection method is presented using Long Short-Term Memory (LSTM) networks and IoT technologies with cloud-enabled security measures. Real-time monitoring and data analysis improve contactless travel security. In luggage, Internet of Things (IoT) devices communicate with cloud servers efficiently to track luggage. The proposed system relies on LSTM networks for predictive analytics and anomaly detection. LSTM networks can detect abnormal behavior by analyzing past data and learning trends. Cloud infrastructure allows data storage, processing, and centralized management and monitoring, improving security system scalability and dependability. IoT and LSTM networks provide a flexible security architecture that can react to new threats. The technology gives travelers and authorities real-time information to reduce luggage loss, theft, and tampering. In line with travel industry trends, the solution is contactless, making it safer and more convenient for travelers. Cloud-enabled solutions, LSTM networks, and IoT technologies can secure bags in contactless travel. © 2024 IEEE.

关键词： Anomaly detection

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JMCD Dataset for Brain Tumor Detection and Analysis Using Explainable Deep Learning

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SN computer science 2023年第6期4卷 840页

作者： Verma, Aryan Gupta, Nidhi Bhatele, Pushpraj Khanna, Pritee Department of Computer Science and Engineering National Institute of Technology HP Hamirpur 177005 India Department of Computer Applications National Institute of Technology HR Kurukshetra 136119 India NSCB Medical College MP and MRI Scan Centre Nagpur Road MP Jabalpur 482003 India Computer Science and Engineering Discipline PDPM Indian Institute of Information Technology Design and Manufacturing MP Jabalpur 482005 India

The growth of abnormal cells in the brain gives rise to a deadly form of cancer known as a brain tumor. The mass of brain tumors proliferates and rises very fast, and if not appropriately treated, the patient’s survival rate is less or can rapidly lead to death. A very exigent task for radiologists is early brain tumor detection which may help to evaluate the tumor and plan treatment for an effective prognosis. In this work, we used deep learning to detect and localize brain tumors from MRI slices. After the detection of the tumor, we generate a color heatmap to show the tumor’s location, severity, and possible affected regions. Our novel image processing algorithm swiftly segments the skull region from the background of MR images, augmented over heatmap for better observation. We analyzed the performance of VGG 16, Resnet 50, and EfficientNet deep learning architectures. Transfer learning on Resnet 50 model with custom top layers achieved excellent performance with 99.37% accuracy, 99.68% precision, and 99.69% specificity, which is better than most of the works in brain tumor detection using machine learning. Expert radiologists critically examined our model outcomes in detail. They found our method effective to triage people with brain tumors and helpful in decision making for the clinicians by severity indication through heatmaps. They observed the prime cause for false positives to be unbalanced contrast in some MR images. Through this work, we also made the novel dataset named as JMCD (Jabalpur medical College Dataset) publicly available for research which contains all four MR pulse sequences annotated by expert radiologists for 140 patients. Each patient has 15–20 sequences containing 250 images. Through this work and accompanying data, we aim to give better methods for brain tumor detection and help research in the same field. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Brain tumor Deep learning Glioma medical image analysis MRI

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The Integration of 6G and Blockchain into an Efficient AIoT-Based Smart Education Model 6

The Integration of 6G and Blockchain into an Efficient AIoT-...

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6th World Symposium on Communication engineering, WSCE 2023

作者： Lioupa, Anna Memos, Vasileios A. Stergiou, Christos L. Ishibashi, Yutaka Psannis, Konstantinos E. School of Information Sciences University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

ISBN: (纸本)9798350339505

The technological advances impact the future classrooms and the whole education system. The evolution of 6G promises faster speeds based on terahertz, wireless communication, the leverage of artificial intelligence (AL), virtually reality (VR) and generally a fully connected world. The use of applications of IoT and AIoT improves the education environment, but the intelligent network services undoubtedly gather, store and analyze big data which are vulnerable to attackers. As a solution we may consider the technology of blockchain. The trust and security of next generation intelligent networks are essential part of their design. In this article we suggest a data security system based on blockchain for AL applications in 6G networks. First, we present two Al-enabled applications, indoor positioning and mobile payment within the framework of 6G in the university area. We present the efficiency of blockchain in data security for Al-enabled systems using two case studies of indoor positioning system and mobile payment. The 6G system which is fully supported by Al and the integration of this in technology of blockchain, is ensured the security in future wireless communication. © 2023 IEEE.

关键词： Blockchain

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Deep Learning Based License Plate Number Recognition for Smart Cities

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computers, Materials & Continua 2022年第1期70卷 2049-2064页

作者： T.Vetriselvi E.Laxmi Lydia Sachi Nandan Mohanty Eatedal Alabdulkreem Shaha Al-Otaibi Amal Al-Rasheed Romany F.Mansour Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTrichy620002India Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science&Engineering Vardhaman College of Engineering(Autonomous)Hyderabad501218India Department of Informatics and Computing Singidunum UniversitySerbia Computer Science Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended *** controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city *** the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic *** VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number *** current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license *** proposed model involves two main stages namely,license plate detection and Tesseract-based character *** detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 ***,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)*** performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.

关键词： Deep learning smart city tesseract computer vision vehicle license plate recognition

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