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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Fatima, Shaista Sangeetha, G. Ponmurugan, P. Arularasan, A.N. Prabhu Chakkaravarthy, A. Denis, R. Chinnasamy, A. Department of Electronics and Communication Engineering MJ College Telangana Hyderabad500053 India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Puduvoyal Tamil Nadu Thiruvallur India Department of Research Meenakshi Academy of Higher Education and Research Tamil Nadu Chennai600078 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Poonamallee Tamil Nadu Chennai600123 India Department of Networking and Communications School of Computing College of Engineering and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science Mount Carmel College Autonomous Karnataka Bengaluru560052 India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur campus Tamil Nadu 603203 India

ISBN: (纸本)9798350398496

As a result of ML, the healthcare industry undergoes substantial innovation and improvement. As a result, data management, clinical operations, drug research, and surgery are all progressing more quickly. The healthcare sector is now required to use this cutting-edge technology because of the Covid-19 pandemic. More importantly, people stand to benefit the most from technology because it may improve their health outcomes by identifying the best treatment alternatives for them. Thanks to ML's enhanced early disease detection capabilities, the frequency of re-admissions to hospitals and clinics can be reduced. In this article, we'll look at the main applications of machine learning in healthcare as well as its exceptional benefits in changing the industry. © 2023 IEEE.

关键词： Machine learning

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Personalized Care for Multiple Sclerosis with IoT Wearables and Deep Learning

Personalized Care for Multiple Sclerosis with IoT Wearables ...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Vetriselvi, D. Sampathrajan, Rajeshkumar Deepa, A. Sujatha, V. Deepa, P. Pandi Murugan, S. Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India McKinsey & Company Fort WorthTX United States Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering Shree Sathyam College of Engineering and Technology Tamil Nadu Salem India Department of Artificial Intelligence and Data Science Agni College of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350374933

This research work presents a new method for improving individualized treatment for patients with Multiple Sclerosis (MS) by combining Internet of Things (IoT) wearables with deep learning (DL) methods, namely Long Short-Term Memory (LSTM) networks. MS is a long-term autoimmune disorder that affects the central nervous system. It is marked by unexpected episodes of worsening symptoms and periods of improvement, highlighting the need for tailored therapy. The widespread use of IoT devices presents unique possibilities for the ongoing monitoring of many physiological and activity measurements important to the development and symptoms of MS. Using LSTM networks, which are very proficient in analyzing temporal sequences, create a prediction model that may anticipate exacerbations of MS symptoms and customize therapies appropriately. Through the analysis of longitudinal data obtained from IoT devices, our model acquires knowledge about unique patterns of illness development and treatment response. This knowledge allows us to create customized care plans for individuals. These paths include proactive activities such as adjusting medication, making lifestyle changes, and seeking early medical advice. The goal is to reduce the worsening of symptoms, improve the quality of life, and optimize treatment results for individuals with MS. The proposed method shows potential for transforming the treatment of MS by offering proactive and patient-centered therapy adaptable to individual requirements and the dynamics of the illness. © 2024 IEEE.

关键词： Patient treatment

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LithoHoD: A Litho Simulator-Powered Framework for IC Layout Hotspot Detection

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 1098-1111页

作者： Shao, Hao-Chiang Chen, Guan-Yu Lin, Yu-Hsien Lin, Chia-Wen Fang, Shao-Yun Tsai, Pin-Yian Liu, Yan-Hsiu National Chung Hsing University Institute of Data Science and Information Computing Taichung402202 Taiwan Novatek Microelectronics Corporation Hsinchu300092 Taiwan National Tsing Hua University Department of Electrical Engineering Hsinchu300044 Taiwan National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu300044 Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106335 Taiwan United Microelectronics Corporation Product Engineering Department Hsinchu300094 Taiwan United Microelectronics Corporation Development of Smart Manufacturing Hsinchu300094 Taiwan

Recent advances in VLSI fabrication technology have led to die shrinkage and increased layout density, creating an urgent demand for advanced hotspot detection techniques. However, by taking an object detection network as the backbone, recent learning-based hotspot detectors learn to recognize only the problematic layout patterns in the training data. This fact makes these hotspot detectors difficult to generalize to real-world scenarios. We propose a novel lithography simulator-powered hotspot detection framework to overcome this difficulty. Our framework integrates a lithography simulator with an object detection backbone, merging the extracted latent features from both the simulator and the object detector via well-designed cross-attention blocks. Consequently, the proposed framework can be used to detect potential hotspot regions based on 1) the variation of possible circuit shape deformation estimated by the lithography simulator and 2) the problematic layout patterns already known. To this end, we utilize RetinaNet with a feature pyramid network as the object detection backbone and leverage LithoNet as the lithography simulator. Extensive experiments demonstrate that our proposed simulator-guided hotspot detection framework outperforms the previous state-of-the-art methods on real-world data. © 2024 IEEE.

关键词： Integrated circuit layout

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Energy Price Forecasting Through Novel Fuzzy Type-1 Membership Functions

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Computers, Materials & Continua 2022年第10期73卷 1799-1815页

作者： Muhammad Hamza Azam Mohd Hilmi Hasan Azlinda A Malik Saima Hassan Said Jadid Abdulkadir Centre of Research in Data Science Computerand Information Sciences DepartmentUniversiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Petroleum Engineering Department Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Institute of Computing Kohat University of Science and TechnologyKohat26000K.P.KPakistan

Electricity price forecasting is a subset of energy and power forecasting that focuses on projecting commercial electricity market present and future *** price forecasting have been a critical input to energy corporations’strategic decision-making systems over the last 15 *** strategies have been utilized for price forecasting in the past,however Artificial Intelligence Techniques(Fuzzy Logic and ANN)have proven to be more efficient than traditional techniques(Regression and Time Series).Fuzzy logic is an approach that uses membership functions(MF)and fuzzy inference model to forecast future electricity *** c-means(FCM)is one of the popular clustering approach for generating fuzzy membership ***,the fuzzy c-means algorithm is limited to producing only one type of MFs,Gaussian *** generation of various fuzzy membership functions is critical since it allows for more efficient and optimal problem *** a result,for the best and most improved results for electricity price forecasting,an approach to generate multiple type-1 fuzzy MFs using FCM algorithm is ***,the objective of this paper is to propose an approach for generating type-1 fuzzy triangular and trapezoidal MFs using FCM algorithm to overcome the limitations of the FCM *** approach is used to compute and improve forecasting accuracy for electricity prices,where Australian Energy Market Operator(AEMO)data is *** results show that the proposed approach of using FCM to generate type-1 fuzzy MFs is effective and can be adopted.

关键词： Fuzzy logic fuzzy C-means type-1 fuzzy membership function electricity price forecasting

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Medical Imaging Analysis Using Machine Learning By Design Thinking Approach 4

Medical Imaging Analysis Using Machine Learning By Design Th...

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4th IEEE International Conference on Smart Technologies in computing, Electrical and Electronics, ICSTCEE 2023

作者： Devi, N. Ramya Revathi, K. Lekhaa, T.R. Aravindhan, K. Kavinkumar, K. Sarumathi, U. Sns College Of Engineering Department Of Information Technology Tamil Nadu Coimbatore India Reva University Department Of Computing And Information Technology Bangalore India Sns College Of Engineering Department Of Artificial Intelligence And Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384628

Breast cancer is a significant global health concern and a leading cause of cancer-related morbidity and mortality among women. Early detection and accurate prediction of breast cancer are crucial for improving outcomes and reducing the impact of this disease. The development of predictive models and risk assessment tools has become an important area of research, as they can help healthcare professionals identify individuals at a higher risk of developing breast cancer and tailor screening and prevention strategies accordingly. This introduction provides an overview of breast cancer prediction, highlighting the importance of the topic and the motivation behind predictive models. It sets the stage for a more comprehensive exploration of the subject and the various methods, technologies, and factors involved in breast cancer prediction. © 2023 IEEE.

关键词： Machine learning

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First-principles electron-phonon interactions and polarons in the parent cuprate La2CuO4

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Physical Review Research 2025年第1期7卷 L012073-L012073页

作者： Benjamin K. Chang Iurii Timrov Jinsoo Park Jin-Jian Zhou Nicola Marzari Marco Bernardi Department of Applied Physics and Materials Science and Department of Physics California Institute of Technology Pasadena California 91125 USA PSI Center for Scientific Computing Theory and Data Paul Scherrer Institut 5232 Villigen PSI Switzerland Department of Physics Pohang University of Science and Technology Pohang 37673 Korea School of Physics Beijing Institute of Technology Beijing 100081 China Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL) École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland

Understanding electronic interactions in high-temperature superconductors is an outstanding challenge. In the widely studied cuprate materials, experimental evidence points to strong electron-phonon (e-ph) coupling and broad photoemission spectra. Yet, the microscopic origin of this behavior is not fully understood. Here, we study e-ph interactions and polarons in a prototypical parent (undoped) cuprate, La2CuO4 (LCO), by means of first-principles calculations. Leveraging parameter-free Hubbard-corrected density functional theory, we obtain a ground state with the band gap and Cu magnetic moment in nearly exact agreement with experiments. This enables a quantitative characterization of e-ph interactions. Our calculations reveal two classes of longitudinal optical (LO) phonons with strong e-ph coupling to hole states. These modes consist of bond stretching and bond bending in the Cu-O plane as well as vibrations of apical O atoms. The hole spectral functions, obtained with a cumulant method that can capture strong e-ph coupling, exhibit broad quasiparticle peaks with a small spectral weight (Z≈0.25) and pronounced LO-phonon sidebands characteristic of polaron effects. Our calculations predict features observed in photoemission spectra, including a 40-meV peak in the e-ph coupling distribution function not explained by existing models. These results show that the universal strong e-ph coupling found experimentally in doped lanthanum cuprates is also present in the parent compound, and elucidate its microscopic origin.

关键词： High temperature superconductors

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Mobile Captured Document Classification based on Blind Image Quality Assessment Model using Brisque

Mobile Captured Document Classification based on Blind Image...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Koushik K S Bipin Nair B J N. Shobha Rani Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru India Department of Artificial Intelligence and Data Science GITAM School of Technology Bengaluru GITAM

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Assessment of document image quality helps in applying suitable pre-processing methods for enhancing document images. The result of enhancement leads to improved recognition rates by OCR. This paper proposes a mobile-captured document classification model based on the blind image quality score. The quality scores are generated by fitting the document wise opinion scores with the Brisque metric. Document wise opinion scores are collected using subjective method. Initially, learning models are created based on the document wise opinion scores. Two methods are proposed as part of this study to generate the document opinion scores, one is using a subjective assessment method, and the other through a random value generation function. The attempt to fit the created learning models using the most dominantly used Brisque's blind image quality measure. The classification of mobile-captured handwritten documents is performed solely using of blind quality score generated by Brisque using simple linear classifiers. datasets of mobile-captured handwritten documents of shadow, blur, and good readability are considered for experimentation. The evaluation of proposed models for training and test sets shows that the learning models created with subjectively assessed opinion scores produced improved results.

关键词： Image quality Measurement Training Analytical models Text analysis Computational modeling Optical character recognition Fitting

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Sequential modeling of temporal timbre series for popular music sub-genres analyses using deep bidirectional encoder representations from transformers 150

Sequential modeling of temporal timbre series for popular mu...

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150th Audio Engineering Society Convention, AES 2021

作者： Geng, Shijia Ren, Gang Pan, Xu Zysman, Joel Ogihara, Mistu Institute for Data Science and Computing University of Miami Department of Computer Science University of Miami

ISBN: (纸本)9781713830672

The timbral analysis from spectrographic features of popular music sub-genres (or micro-genres) presents unique challenges to the field of the computational auditory scene analysis, which is caused by the adjacencies among sub-genres and the complex sonic scenes from sophisticated musical textures and production processes. This paper presents a timbral modeling tool based on a modified deep learning natural language processing model. It treats the time frames in spectrograms as words in natural languages to explore the temporal dependencies. The modeling performance metrics obtained from the fine-tuned classifier of the modified Deep Bidirectional Encoder Representations from Transformers (BERT) model show strong semantic modeling performances with different temporal settings. Designed as an automatic feature engineering tool, the proposed framework provides a unique solution to the semantic modeling and representation tasks for objectively understanding of subtle musical timbral patterns from highly similar musical genres. © 2021 150th Audio Engineering Society Convention, AES 2021. All rights reserved.

关键词： Textures

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An Intelligent Machine Learning Approach to Detect Spam in Social Media

An Intelligent Machine Learning Approach to Detect Spam in S...

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data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： R. Mohandas D.S. John Deva Prasanna N. Meenakshi Kaviyaraj R S. Nathiya N. Arivazhagan Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur India Research Scholar Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Machine learning based intelligent approach is applied to finding spam in YouTube videos. Spam comments are ones that are promotional or unrelated. A growing number of users have been drawn to the idea of making money by placing advertisements on popular social networking platforms over time. These remarks damage a channel's reputation. YouTube has addressed the issue with a limited set of solutions that concentrate on preventing comments from including links. Such heuristics are now futile since spammers have found ways to get around them. Despite the fact that common machine learning classification techniques have demonstrated considerable success, new methods can still increase accuracy. Therefore, this research attempt to identify such statements using machine learning techniques like Random Forest, SVM, and Naive Bayes. In order to convey a clear concept, graphs are used to illustrate data cleaning, data analysis, text preparation, and model building in this research.

关键词： Support vector machines Video on demand data analysis Social networking (online) Forestry data models Bayes methods Web sites Random forests Videos

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

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IEEE Transactions on Mobile computing 2025年第7期24卷 5731-5748页

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen the College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore the School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore the School of Automation Guangdong University of Technology Guangzhou510641 China the Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

—Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Markov processes

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