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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Sahaya Sheela, M. Muthu Krishnammal, P. Shetty, Surendra Surendran, S. Vinothkumar, K. Venkataramanaiah, B. Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Avadi India School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Master of Computer Application NMAM Institute of Technology Nitte Deemed University Karnataka India Department of Computer Science and Engineering Tagore Engineering College Tamil Nadu Chennai India Department Of ECE PSNA College Of Engineering And Technology Tamilnadu Dindigul India Department Of ECE Veltech rangarajan Dr sagunthala R&D institute of science and Technology Chennai Avadi India

ISBN: (纸本)9798350305418

Not only do people enjoy listening to piano music, but it may also be differentiated from vocal music in the realm of digital music. With its ongoing development, the piano music recommendation algorithm has found solutions to a great number of practical issues, and its technical path has also progressed from its initial recommendation based on the behavioral preferences of users to the correlation recommendation among users to the mining of users' potential preferences in recent years. The birth of The ONE smart piano means that a new teaching mode will be produced, that is, piano learning and user experience will be integrated, and the latest achievements of modern science and technology will be applied and rooted in the field of music education and teaching, so that the piano, a large musical instrument, can be easily combined with a variety of intelligent devices. This is significant in terms of the combination of offline artificial intelligence and music education because it means that a new teaching mode will be produced. Additionally, this new teaching mode will The improved piano music recognition system is investigated in this study, which uses artificial intelligence as its foundation. In the beginning, both the overall architecture of the system and the functional module design plan are shown. It is well known that the feature matrix of a piano is distinct from that of other musical instruments. This distinction was discovered when a digital recognition approach was used to extract the music timbre feature matrix of a piano. The findings of the experiments indicate that the suggested system has a high recognition accuracy for the purpose of timbre recognition of piano arrangements, which demonstrates that it is deserving of further promotion and application. © 2023 IEEE.

关键词： Artificial intelligence improved recognition algorithm piano music

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Interface Ion-Driven, Highly Stable Synaptic Memristor for Neuromorphic Applications

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ACS Applied Electronic Materials 2023年第4期5卷 2439-2446页

作者： Gawai, Umesh Wu, Chien-Hung Kumar, Dayanand Chang, Kow-Ming UST-IPPP College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Optoelectronics and Materials Engineering Chung Hua University Hsinchu30012 Taiwan Institute of Microelectronics Department of Electrical Engineering National Cheng-Kung University Tainan701 Taiwan Institute of Electronics Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

Because of the continual advancements in the artificial intelligence technology, its practical applications (e.g., in computer vision, health care, and pattern recognition) are expanding. Various architectures integrating memory cells and transistors have been used to demonstrate artificial synaptic arrays. However, designing memory cells with superior analogue switching is a major challenge in the development of neuromorphic systems. We evaluated a TiN/AlN/Cu/AlN/Pt memristive synapse for neuromorphic computing. The synaptic device exhibited multilevel characteristics with varying reset stop voltages ranging from −2.7 to −2.2 V. The device also exhibited highly stable reparative 200 potentiation and depression cycles with high nonlinearity values of approximately 2.39 for potentiation and −2.19 for depression. The device further exhibited highly stable DC endurance over 1000 cycles, AC pulse endurance (1 M), and stable retention (104 s) at 100 °C without any degradation. The experimental potentiation and depression data were used to train a Hopfield neural network to effectively identify input images with a resolution of 28 × 28 pixels, representing 784 synapses used in the simulation process. The training resulted in an accuracy of >91% in 28 epochs. Therefore, the newly designed device exhibits promising features, such as high linearity and accuracy, and is thus highly suitable for neuromorphic computing purposes. © 2023 American Chemical Society.

关键词： Memory architecture

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Breaking Boundaries: Harnessing the Power of Deep Learning Model Design to Predict Diabetes with Artificial Intelligence Assisted Prediction Strategy 4

Breaking Boundaries: Harnessing the Power of Deep Learning M...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Senthil Kumar, N.K. Kalpana, S. Karthika, K. Anandh, A. Anusha, Badepalli Santhoshini, P. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology School of Computing Department of Cse Tamil Nadu Chennai India Department of Computer Science Dubai United Arab Emirates Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Tamil Nadu Chennai India Kamaraj College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Virudhunagar India Mlr Institute of Technology Department of Ece Dundigal Hyderabad India Rmd Engineering College Department of Electronics and Communications Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350349900

Predicting the onset of diabetes at an early stage might greatly mitigate the disease's devastating effects;the condition is currently incurable and results from chronically elevated blood sugar levels. Predicting the onset of diabetes has been the focus of current efforts within the deep learning community, building on decades of prior study. Nowadays, diabetes is a common and unfortunately incurable health problem. People can't give their health the attention it needs because of their busy schedules. The food we eat is broken down into glucose, which is then released into our bloodstream. When blood sugar levels are too high, the pancreas secretes a hormone called insulin. A key function of insulin is to facilitate the uptake of glucose by cells for energy production. Identifying diabetes at an early stage can greatly improve the chances of leading a sustainable life. A fruitful way would be to use machine learning algorithms, which can improve themselves with future predictions after being taught and tested with massive amounts of data. This study presents AI-based Diabetes Prediction (AIDP), a model that explores diabetes data to make better predictions about the illness, with the severity of the condition in mind. The suggested rationale is tested against the traditional K-Nearest Neighbor (kNN) learning paradigm to see how well it works. Results from the planned study demonstrate that the model can accurately forecast the onset of diabetes, if not better. When compared to another deep learning method known as kNN, our results reveal that the suggested AIDP attained superior accuracy. © 2024 IEEE.

关键词： Prediction models

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Deep Learning based Sentiment Analysis on Images 2

Deep Learning based Sentiment Analysis on Images

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Nimitha, N. Bala, Saroj Satheeswaran, V. Janani, M. Selvanayaki, S. Ramasami, S. Rmk College of Engineering and Technology Electronics and Communication Engineering Chennai India Ajay Kumar Garg Engineering College Department of Mca Ghaziabad India Nehru Institute of Technology Department of Ece Coimbatore641105 India Kpr Institute of Engineering and Technology Computer Science and Engineering Coimbatore641 407 India Saveetha Engineering College Department of Ai & Ds Chennai India Bannari Amman Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350347579

Sentiment analysis is a powerful tool that has been gaining popularity among businesses in recent years. It involves analyzing text, speech, or images to understand the emotions or opinions conveyed by them. This process can provide valuable insights into customer preferences, behavior, and satisfaction levels. By leveraging sentiment analysis, businesses can gather customer feedback on their products and services, which can help them identify areas for improvement and develop stronger customer relationships. Furthermore, sentiment analysis can also be used to support emotional marketing campaigns, allowing businesses to create more targeted and personalized messaging that resonates with their audience. For example, by analyzing social media posts and comments, businesses can gain a better understanding of how their brand is perceived and adjust their marketing strategies accordingly. One popular method for this is transfer learning, where pre-trained models are used to analyze new datasets. Deep learning algorithms, such as convolutional neural networks (CNNs), have been particularly successful in achieving accurate results in a variety of applications, including image sentiment analysis. However, analyzing emotions conveyed through images is a complex task, and there is still much room for improvement. The Inception-v3 technique is a notable development in this field, as it can easily identify key parts of the body, such as the face, which is essential for accurately detecting emotions. The results of this model were compared to those of various other machine learning approaches, and the suggested model showed superior accuracy levels of up to 99.5%. This research demonstrates the potential of using deep learning algorithms and transfer learning methods to improve image sentiment analysis. For example, in the field of Healthcare, image sentiment analysis can be used to detect emotional expressions of patients, which can be valuable for assessing their mental and e

关键词： Sentiment analysis

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Innovations in Natural Language Processing through Enhanced Linguistic Model Accuracy and Efficiency using Advanced Reinforcement Learning Techniques 2

Innovations in Natural Language Processing through Enhanced ...

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

作者： Kalaiselvi, K. Shanmugam, Raju Tamilselvan, S. Manikandan, S.P. Rajasekar, P. Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Tamil Nadu Coimbatore India Unitedworld School of Computational Intelligence Karnavati University Gujarat India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Saveetha University Tamil Nadu Chennai India School of Engineering and Technology CMR UNIVERSITY Karnataka Bengaluru India Department of ECE Sri Krishna College of Technology Kovaipudur Coimbatore India

ISBN: (纸本)9798350383867

The paper will be focusing on combining sophisticated reinforcement learning approaches with natural language processing (NLP) platforms to step up the accuracy, efficiency, and adaptability of the linguistic models. The methodology approach is based on stepwise creation of efficient basic model architectures which is combined with formulation of tasks as RL problems and careful selection of appropriate algorithms and training processes. In our study on the development of an advanced AI model which took varying evaluation metrics, it is now apparent through the comprehensive evaluation of the same, that our model's performance on different evaluation metrics was significantly better than that of the baseline approaches. For instance, this technique leads to better precision and operation speed, allowing NLP systems to undertake more complex linguistic tasks with higher rate of precision and faster inferential speeds. Furthermore, this approach facilitates the model adaptation process which helps in accommodating a hybrid intelligence environment where models are able to evolve in changing language contexts and domains. Interpretability assessment is focused on producing not only the transparency of the described method but also the ability for the user to understand the reasoning of the model and the decision-making processes. Smart cities also undertake a set of ethical considerations that encompass the significance of regards to bias mitigation, fairness awareness and transparency to build equitable and trustworthy outcomes in the real-world implementation. In total, research done shows the possibility for advanced reinforcement learning techniques to have a significant impact to NLP, thus creating innovative NLP models capable of ensuring that they are diverse, adaptable and have necessary measures of ethics as the needs are changing. © 2024 IEEE.

关键词： Adversarial machine learning

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Past, Present, and Future of CASS Educational Programs and Initiatives [Feature]

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IEEE Circuits and Systems Magazine 2024年第2期24卷 7-15页

作者： Rokhani, Fakhrul Zaman Zhang, Xinmiao Joshi, Rajiv V. Reis, Ricardo Grimblatt, Victor Tang, Kea-Tiong Li, Yongfu Amara, Amara Delgado-Restituto, Manuel Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Systems Engineering Serdang43400 Malaysia The Ohio State University Department of Electrical and Computer Engineering ColumbusOH43210 United States IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States Institute of Informatics Federal University of Rio Grande do Sul Porto Alegre90010-150 Brazil Synopsys California CA92037 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Shanghai Jiao Tong University Department of Micro and Nano Electronics Engineering Shanghai200240 China Paris75006 France Institute of Microelectronics of Sevilla Sevilla41092 Spain

The Circuits and Systems Society (CASS) places a strong emphasis on advancing education within its domain. Various educational programs and resources have contributed to the professional development of CASS members for more than 20 years now. A major transformation within the Society was started in 2021 with a clear vision to become a premier educational resource within circuits and systems areas of interest. This article provides a comprehensive review of flagship educational programs and past initiatives. Insights from the lessons learned and future trends in the education ecosystem guided society's strategies and frameworks for improving and developing new educational products and services. The benefits of learning with the CASS program are presented, highlighting the Society's deep commitment to serving the community at large in educational pursuit for years to come. © 2001-2012 IEEE.

关键词： engineering education

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Personalized Health Monitoring Platform with Privacy Preservation in IoT Cloud Networks using Deep Learning 8

Personalized Health Monitoring Platform with Privacy Preserv...

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

作者： Bhat, Devidas Kansal, Vipashi Kumar, B.N. Mohan Patil, Harshal Vekariya, Daxa Kannan, Sathish Department of Information Science and Engineering Karkala Udupi District Karnataka Nitte India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India R R Institute of Technology Department of Electronics and Communication Engineering Karnataka Bangalore560090 India Iilm University School of Computer Science & Engineering Greater Noida201306 India Parul Institute of Engineering and Technology Parul University Department of Computer Science & Engineering Post Limda Gujarat 391760 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350376425

Deep learning-based models have demonstrated the potential for personalized health monitoring, creating revolutionary opportunities in healthcare delivery and patient outcomes. In the present study, different deep learning algorithms like LSTM, GRU, CNN and Autoencoders are compared on health data. There are various performance measures for each model such as accuracy, precision, recall and F1-score to see how well it can deal with different types of health data. As a result, LSTM achieved remarkable performance metrics including an accuracy of 0.92, a precision of 0.88, recall of 0.94 and F1 score of 0.91 although it required long training time of about 12 hours. GRU had an accuracy value of 0.89, precision was at 0.85;recall stood at 0.91 and f-1 score was at 0.88 while still having a shorter training time which was ten hours unlike CNN that performed well in image processing with an accuracy level of o'95% and preciseness that amounted to o'92%. Besides this CNN took longer to train since the time was twenty-four hours but not like Autoencoders whose anomaly detection efficiency resulted in low reconstruction errors amounting to only five (5%) percent or less even though the training time is just eight (8) hours with relatively lower accuracies and precisions reaching eighty-seven (87%) percent or eighty-two (82%) percent respectively These findings highlight the multifaceted approach and promise that deep learning models bring to transforming individualized health monitoring, allowing accurate extraction of actionable information from personal biometrics and enabling proactive healthcare. These models, which employ advanced computational tools, offer exciting opportunities for improving the speed and precision of diagnoses, refining treatment strategies, and advancing personalized medicine globally. © 2024 IEEE.

关键词： Electronic health record

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An Advanced Decentralized Architecture enabled Logical Identity based Encryption Logic using Blockchain Assistance 4

An Advanced Decentralized Architecture enabled Logical Ident...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Geetha, R. Kalpana, S. Roopa Chandrika, R. Preetha, M. Santhoshini, P. Nithila, Ezhil E Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Eee Saveetha School of Engineering Tamil Nadu Chennai India Department of Computer Science Dubai United Arab Emirates Karpagam Academy of Higher Education Department of Computer Science and Engineering Tamil Nadu Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Rmd Engineering College Department of Electronics and Communications Engineering Tamil Nadu Chennai India Veltech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Ece Tamil Nadu Chennai India

ISBN: (纸本)9798350349900

Using electronic signatures, blockchain technology, and identity-based encryption, essential documents may be certified and electronic contracts can be signed. All of the system's nodes, operations, and transactions may benefit from blockchain technology's distributed ledgers. This study introduces a novel encryption system, Decentralized Identity based Encryption Logic (DIEL), and assesses its efficacy by comparing it to the established Identity based Encryption (IBE) scheme. In order to guarantee the privacy and confidentiality of patients' data in cloud health systems, the suggested technique offers first-rate security using bilinear pairings. Additionally, this strategy authenticates users through challenge-response procedures. Furthermore, the suggested method guarantees the accuracy and integrity of shared data via the use of blockchain technology. Using the standard model, we can confirm that the suggested protocol is valid and that it is secure. Because all nodes are contributing data, cyberspace is more open and behavior can be more easily tracked. Data certification and the contract signing procedure are both finished and in compliance. Users may protect themselves from fraud and identity theft with the help of the Identity based Management system's encryption features. When used together, these two technologies will provide a safer method of data storage while yet preserving user privacy. © 2024 IEEE.

关键词： Decentralized finance

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Polarization-induced photocurrent switching effect in heterojunction photodiodes

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Nano Research 2023年第4期16卷 5503-5510页

作者： Dingbo Chen Yu-Chang Chen Guang Zeng Yu-Chun Li Xiao-Xi Li Dong Li Chao Shen Nan Chi Boon S.Ooi David Wei Zhang Hong-Liang Lu State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics&SystemsSchool of MicroelectronicsFudan UniversityShanghai 200433China Key Laboratory for Information Science of Electromagnetic Waves Department of Communication Science and EngineeringFudan UniversityShanghai 200433China Photonics Laboratory Division of ComputerElectricaland Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia

The unipolar photocurrent in conventional photodiodes(PDs)based on photovoltaic effect limits the output modes and potential versatility of these devices in *** photodiodes with photocurrent switching are emerging as a promising solution for obtaining photoelectric devices with unique and attractive functions,such as optical logic ***,we design an all-solid-state chip-scale ultraviolet(UV)PD based on a hybrid GaN heterojunction with engineered bipolar polarized electric *** introducing the polarization-induced photocurrent switching effect,the photocurrent direction can be switched in response to the wavelength of incident light at 0 V *** particular,the photocurrent direction exhibits negative when the irradiation wavelength is less than 315 nm,but positive when the wavelength is longer than 315 *** device shows a responsivity of up to−6.7 mA/W at 300 nm and 5.3 mA/W at 340 nm,*** particular,three special logic gates in response to different dual UV light inputs are demonstrated via a single bipolar PD,which may be beneficial for future multifunctional UV photonic integrated devices and systems.

关键词： GaN heterostructure ultraviolet(UV)photodiodes bipolar photocurrent optical logic device

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Energy-Efficient Resource Allocation for Smart Grids using Deep Reinforcement Learning in IoT Cloud Systems 8

Energy-Efficient Resource Allocation for Smart Grids using D...

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

作者： Patil, Harshal Kansal, Vipashi Balasubramani, R. Manohar, Talari Vekariya, Daxa Kannan, Sathish Iilm University School of Computer Science & Engineering Uttar Pradesh Greater Noida201 306 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Department of Information Science and Engineering Karnataka Nitte574110 India Anantha Lakshmi Institute of Technology and Sciences Department of Electrical and Electronics Engineering Andhra Pradesh Ananthapuramu515721 India Parul Institute of Engineering and Technology Parul University Department of Computer Science & Engineering Post Limda Gujarat 391760 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350376425

This study describes the use of deep reinforcement learning (DRL) models to fine-tune resource allocation for smart grids in order to reduce energy waste and operational costs. The research experiment evaluates four different DRL algorithms, which include SAC, DDPG, PPO, and A3C. It demonstrates performance through various evaluation metrics, which include impeccable energy efficiency, perfect resource utilization, load balancing ratio, average response time, and computational overhead. SAC is proven to achieve 92.5% energy efficiency, 88.7% resource utilization, 90% load balancing ratio, and response time of 120ms. The computational overhead assessed supplements to 15 GFLOPS. DDPG showcased 90.1% energy efficiency and 85.4% resource utilization, 88.9% load balancing, a response time of 110 ms, and a computational overhead of 14.8 GFLOPS. On the other hand, PPO and A3C also proved to work well and have a promising future to be implemented in a dynamic grid. This analysis demonstrates that practical applications from DRL, including real-time decision-making and adaptive control approaches, can prove vital while assigning and working on energy tasks and renewable conduct. These models prove important and beneficial in enhancing the operational efficiency of the smart grid, understanding and learning continuously from real-time data. It will be a vital concept and idea that would be helpful in ensuring that smart grids work perfectly to help in a reduction in carbon footprints and effective energy resource management to ensure end-use service provider satisfactions. © 2024 IEEE.

关键词： Deep reinforcement learning

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