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Integrated and Intelligent Elder-Friendly E-commerce Framework using NLP techniques

Integrated and Intelligent Elder-Friendly E-commerce Framewo...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Sivasanjith G mohan Prabhu N Hemaswathi S Rajkumar P Rajeshkannan C R. Dhivya Dept. of Information Technology KARE Krishnankovil India Artificial Intelligence and Data Science Indra Ganesan College of Engineering Tiruchirappalli India Dept. of Computer Science and Engg. Mount Zion College of Engg. and Tech. Pudukottai India Dept. of Computer Science and Engg. Suguna College of Engineering Coimbatore India Dept. of Information Technology M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

All over the world, the population of elderly people is increasing a lot. There needs to be special attention to the welfare of the elderly person to make them confident in their independent quality of living. In recent years, researchers have shown much interest in smart home automation for people who live alone without any human assistance. However, the purchase of medical drugs, diapers, etc., outside the house has to be fulfilled by their family members or caretakers. In most cases, it is not feasible, as the family members may stay long away from the elders. Purchasing online is easy; however, analyzing the best product requires knowledge in each e-commerce application for better quality at a low cost. This paper proposes an integrated and intelligent e-commerce framework to avoid the drawback of purchasing items online in a single window using the NLP technique. The proposed framework provides offers, discounts, star ratings, and product reviews by analyzing other customers’ review data and purchase patterns. It provides interactive data analysis at a single window for the ease of elderly persons. The performance of the proposed framework was evaluated by 120 elderly persons using a simple questionnaire with a statistical analysis of a sample purchase.

关键词： Drugs Sentiment analysis Reviews Statistical analysis Urban areas Stars Smart homes Electronic commerce Older adults Recommender systems

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HiAt-Net: A Novel Three-Layered Encoder-Decoder with Hierarchical Attention for Classifying Cervical Cancer with Cytology Images

HiAt-Net: A Novel Three-Layered Encoder-Decoder with Hierarc...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Rakesh Kumar mahendran Parthasarathy Ramadass m.Kiruthiga Devi R Sanmugasundaram Usharani Thirunavukkarasu A.Peter Soosai Anandaraj Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information and Technology Dr. M.G.R. Educational and Research Institute Chennai India Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Biomedical Engineering Saveetha School of Engineering SIMATS School of Engineering Saveetha University Chennai Tamil Nadu India Vel Tech Rangarajan Dr:Sagunthala R&D Institute of Science and Technology Chennai

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Cervical cancer (CeCA) is a major worldwide health concern, particularly in low- and middle-income countries where screening and treatment facilities are not widely available. Cervical cells, which connect the lower portion of the uterus to the vagina, are where CeCA, a particular type of cancer, originates. Sexually transmitted infections such as certain strains of the Human Papillomavirus (HPV) are the main cause of cervical cancer. But not every HPV infection results in CeCA; other risk factors include smoking, having several sexual partners, and having a weaker immune system.. Additionally, the classification of CeCA using Deep Learning (DL) can pose challenges related to computational resources, model complexity, and robustness compared to state-of-the-art methodsOur solution, HiAt-Net, tackles these challenges by introducing a 3-layer encoder-decoder approach that incorporates a hierarchical attention mechanism. This method is specifically designed to extract features effectively from complex input data, such as the Herlev dataset and SIPaKmeD dataset. By integrating the attention mechanism at multiple levels, our model can capture hierarchical dependencies and selectively focus on relevant features, resulting in enhanced performance in classification tasks. We conducted a thorough comparison and analysis of our proposed method against existing approaches, evaluating performance metrics such as accuracy, precision, recall, and F1-Score.

关键词： Analytical models Attention mechanisms Art Computational modeling Uterus computer architecture Feature extraction Decoding Complexity theory Cervical cancer

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Optimizing User Data Privacy and Confidentiality in Cloud Storage Systems Through Advanced Obfuscrypt Encryption methods for Enhanced Security and Efficient Data Protection

Optimizing User Data Privacy and Confidentiality in Cloud St...

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Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： P.S. Ramesh I. Sudha J. Jagannathan Rajkumar Pandiarajan A. Ponmalar Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India Department of Information and Technology School of Computing Kalasalingam Academy Of Research and Education Krishnankoil Virudhunagar India R.M.K. Engineering College Chennai Tamilnadu India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

With a steady increase in the adoption of cloud storage systems, the client’s data are at risk of being leaked. There are several methods of encrypting data which are quite useful but they lack some of the features of today’s sophisticated threats. The methodology used in this research propose an encryption framework known as Obfuscrypt to improve the issues of data privacy as well as confidentiality of data stored in cloud storage systems. Obfuscrypt uses conventional encryption strategies incorporated with obfuscation and post-quantum cryptographic algorithms to provide a complex security plan immune to classic and quantum approaches. Some of the proposed other innovations include the decentralised key management with multi-factor authentication and RBAC-ABE as the fine-grained access control techniques. These features make it possible for only the authorized personnel to be in a position to be in a position of decrypting and retrieving the stored data thereby minimizing the possibility of the data being accessed by unauthorized people. The work also considers the issue of performance efficiency of the cloud storage systems. Similar to other symmetric encryption programs, Obfuscrypt uses light encryption algorithms and brings in to play the hardware acceleration so that it may infuse lesser computational overhead in the process of encryption and decryption. As can be clearly seen from the results, the actual work proves that the proposed framework not only offers a higher level of security but does not affect the system performance to a great extent. Also, the study addresses the post-quantum cryptographic algorithm for making the system resistant to an attack that can be posed by quantum computing systems. With the help of these improved algorithms, Obfuscrypt guarantees the permanent data security in the world of constantly emerging cyber threats.

关键词： Resistance Cloud computing technological innovation multi-factor authentication Quantum computing System performance memory management Software performance Encryption Protection

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Enhancing Healthcare Data Security in Cloud Computing Systems through Advanced Homomorphic Encryption techniques Integrated with Cuckoo Search Optimization Algorithm

Enhancing Healthcare Data Security in Cloud Computing System...

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Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： I. Sudha P.S. Ramesh A. Ponmalar J. Jagannathan Rajkumar Pandiarajan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India R.M.K. Engineering College Chennai Tamilnadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India Department of Information and Technology School of Computing Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The studies in this research are focused on the mix of homomorphic encryption mechanisms and the Cuckoo Search Optimization Algorithm to promote the security of the healthcare data in the cloud computing system. I have observed that cloud solutions are now at the center of the contemporary health care systems, and security of patient’s data remains a top priority. Earlier used encryption techniques, though secure if implemented, have problems in the way that data is to be processed first it has to be decrypted and then made ready for use - it is in this phase that data can be easily hacked into. Homomorphic encryption provides solution to this issue since it enables one to perform computations on encrypted data while maintaining its confidentiality. However, it takes a lot of computation which presents a lot of problems even in terms of simple performance. The Cuckoo Search Algorithm, a new nature-inspired metaheuristic, is incorporated into the encryption process to provide further optimization, whilst preserving the security benefits of the algorithm. The use of the proposed methodology takes place employing Python and its deployment at the cloud platform with elaborated dataset containing anonymized records extracted from electronic health records. The outcome proves that there are great enhancements in encryption and decryption time, computational complexity, and capacity, and thereby making the system appropriate for real-world healthcare solutions. This research offers a strong base for protection of healthcare data in cloud and is useful for the enhancement of adaptive, comprehensive and secure cloud computing systems.

关键词： Cloud computing machine learning algorithms metaheuristics Distributed databases medical services Organizations machine learning Homomorphic encryption Protection Electronic medical records

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Securing multi-Tenant Cloud Environments with Fully Homomorphically Encrypted Secure multiparty Computation

Securing Multi-Tenant Cloud Environments with Fully Homomorp...

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Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： A. Ponmalar Rajkumar Pandiarajan I. Sudha P.S. Ramesh J. Jagannathan R.M.K. Engineering College Chennai Tamilnadu India Department of Information and Technology School of Computing Kalasalingam Academy Of Research and Education Krishnankoil Virudhunagar India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The aim of this work is to examine the application of FHE-SmPC as a viable security model for protecting the tenant isolation in multi-tenant cloud model. In multi-tenant environments, where various customers use the same cloud system, controlling the data leakage and protecting privacy is a major issue. Actual safeguards fail to meet the needs of data protection during processing, therefore, the introduction of highly elaborate encryption processes must happen. This research work focuses on the use of FHE technique and SmPC such that data can undergo processing while the plaintext remains unknown to any party. A kind of approach is based on the acquisition of synthetic and real data sets, system-level architecture, and cryptographic algorithms using open-source libraries with cloud service providers. The results of such implementation indicate that despite the fact that the FHE-SmPC system has a considerable computational overhead in comparison with classical schemes it is very secure and protected against different types of cryptographic attacks. The problem of scalability is still an open issue, but the complex is quite efficient with various data types that is why it can be used in various cloud applications. This paper offers a pertinent solution for security of data that is housed in multitenant environment and will go along way in creating fertile ground for research and innovation in secure clouding.

关键词： Resistance technological innovation Scalability Data protection Libraries Service-oriented architecture Encryption Cryptography Time complexity Secure storage

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Cyberattack Aware Load Frequency Control in Power Systems

Cyberattack Aware Load Frequency Control in Power Systems

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Asilomar Conference on Signals, Systems & computers

作者： Shahriar Rahman Fahim Rachad Atat Cihat Kececi Abdulrahman Takiddin muhammad Ismail Katherine R. Davis Erchin Serpedin Electrical & Computer Engineering Department Texas A&M University College Station TX USA Department of Computer Science & Mathematics Lebanese American University Beirut Lebanon Department of Electrical & Computer Engineering Florida State University Tallahassee FL USA Department of Cybersecurity Education Research and Outreach Center Tennessee Tech University Cookeville TN USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

The load frequency control (LFC) is crucial for stabilizing the frequency of the power grid in the intermittency of renewable energy sources. modern LFC systems utilize open communication and automation networks that expose power systems to potential cyberattacks, which could lead to frequency instability throughout the network. Current research often treats frequency control and cybersecurity independently, which could lead to ineffective solutions and increased system instability. This paper introduces a cyber-resilient LFC strategy that improves both control performance and security. We employ reinforcement learning for adaptive frequency control and integrate it with a graph convolutional neural network to enhance control responses during a cyberattack event. Our strategy also includes a graph auto encoder-based attack detector that is trained in various scenarios and has shown more than 92% in detection rate while tested on the Iberian power system topology, which includes 486 buses. This highlights the robustness and reliability of our approach in ensuring resilience against cyberattacks within the realm of frequency control in power systems.

关键词： Renewable energy sources Automatic frequency control Filters Autoencoders Reinforcement learning Power system stability Convolutional neural networks computer crime Resilience Load modeling

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Performance of Deep Learning Assisted Visible Light Communications Impaired by Blockages

Performance of Deep Learning Assisted Visible Light Communic...

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IEEE Conference on Global Communications (GLOBECOm)

作者： Parvez Shaik Cihat Keçeci Kamal K. Garg Ali Boyaci muhammad Ismail Erchin Serpedin Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Department of Electrical and Computer Engineering Texas A&M University College Station TX USA School of Technology Pandit Deendayal Energy University Gandhinagar Gujarat India Department of Computer Engineering Istanbul Commerce University Istanbul Turkey Department of Computer Science Tennessee Tech University Cookeville TN USA

This study investigates the performance of visible light communications (VLCs) in the presence of blockages. An indoor office scenario with a single VLC access point serving the user nodes in the presence of human blockages is examined. System performance is assessed through closed-form expressions for outage probability and symbol error rate for binary phase shift keying and quadrature amplitude modulation. A deep neural network for symbol detection is deployed at the receiver. Performance metrics illustrate that the blockages cause significant impact on signal detection. computer simulations corroborate the correctness of the obtained analytical expressions.

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Advanced machine Learning Algorithms for 1Predictive Analytics in Healthcare to Enhance Patient Outcomes with Data-Driven Insights

Advanced Machine Learning Algorithms for 1Predictive Analyti...

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Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： Rajkumar Pandiarajan J. Jagannathan P.S. Ramesh A. Ponmalar Sudha Department of Information and Technology School of Computing Kalasalingam Academy Of Research and Education Krishnankoil Virudhunagar India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India R.M.K. Engineering College Chennai Tamilnadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

This research aims to explore the use of modern complex defensive machine learning algorithms in the provision of predictive analytics for health improvement. Incorporating electronic health records, medical image information, and genomic data the work proposes and assesses multiple machine learning models including deep learning, ensemble learning, and reinforcement learning. The main findings shown very high performance in disease prediction, in chronic illness and in the application of efficiency improvements in hospitals as a whole. Overall, such threats as data quality, data privacy, and model interpretability have been discussed in the research and emphasize the importance of the machine learning approach in healthcare. It is highlighted that further development and integration of the interdisciplinary approaches as well as the use of the transparent and interpretable artificial intelligence and machine learning models can be urgent to increase the acceptance among the healthcare staff. This paper enhances the literature on applying machine learning in healthcare systems adding to the repertoire of knowledge for improved patient care.

关键词： Data privacy machine learning algorithms Hospitals Data integrity machine learning Reinforcement learning Predictive models Data models Predictive analytics Diseases

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Advanced multi-Spectral Image Processing techniques for Enhanced Remote Sensing and Comprehensive Environmental monitoring in Diverse Ecosystems

Advanced Multi-Spectral Image Processing Techniques for Enha...

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Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： J. Jagannathan A. Ponmalar Rajkumar Pandiarajan I. Sudha P.S. Ramesh Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India R.M.K. Engineering College Chennai Tamilnadu India Department of Information and Technology School of Computing Kalasalingam Academy Of Research and Education Krishnankoil Virudhunagar India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The research is aimed at improvement of multi-spectral image analysis for remote sensing and monitoring the environment at various ecosystems. A combined spectral imagery encompasses several spectrums at a time compared to single spectral imagery that takes only one spectrum at an instance or at a given area to cover all these spectrums. The study employs digital high resolution satellite images and ground based data to quantify the vegetation vigour, water quality, soil characteristics and changes in land cover. Features, indexes, and classification routines are employed to process the data that is collected with the aid of avionics and other peripherals. This provincial level modelling highlights important gains in environmental status, which includes the general improvement in vegetation condition, water quality as well as physical properties of the soil in the province. This study focuses on the complementary possibilities of integration of multi-spectral imaging with other remote sensing technologies and the high performance of data processing. These techniques play significant roles if used to give current and comprehensive information in the management and conservation of the environment as highlighted in the study.

关键词： Accuracy Wildfires multispectral imaging Disasters Ecosystems Vegetation mapping Water quality Soil Sensors Remote sensing

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An Innovative method for mobile Network Payment Security System Based on hybrid LSTm-Ensemble Learning-Based model

An Innovative Method for Mobile Network Payment Security Sys...

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Advancement in technology (ICONAT), International Conference for

作者： N Sujatha S Pramod S Aravindh Balagopala Ramesh Babu S. Rukmani Devi m. Ananthi Department of Mathematics B. M. S. College of Engineering Bengaluru Karnataka India Department of Mathematics Nitte Meenakshi Institute of Technology Yelahanka Bangalore Karnataka India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science and Engineering SRKR Engineering College Bhimavaram Andhra Pradesh India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamilnadu India Department of Computer Science and Business Systems KGiSL Institute of Technology Coimbatore Tamilnadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

It is common practice in many industries to use cash since it accounts for more than 85% of payments in almost all developing countries. A cell phone is become an everyday item. mobile phones have practically become people’s constant companions due to the plethora of other uses they offer beyond just communicating. Since they are both cheap and multipurpose, soon everyone will be utilizing them. In an ideal world, everyone would use their cell phone for everything. Verification, preprocessing, and training the model are the three parts that make up the method. In order to confirm the payment, the customer’s biometric template is checked during the verification procedure. The three stages of fingerprint preparation include matching fingerprint templates, extracting fingerprint minutiae, and fingerprint preprocessing. The training of the model was carried out using a Hybrid LSTm-Ensemble Learning. The average accuracy of the suggested method is 92.76%, which is higher than LSTm and Ensemble Learning.

关键词： Training Accuracy Cellular phones Biological system modeling Fingerprint recognition Vectors mobile handsets Security Ensemble learning Long short term memory

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