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Molecular Design for Drug Discovery with the Use of Generative Adversarial Networks 5

Molecular Design for Drug Discovery with the Use of Generati...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Sumathi, G. Juliet, P. Sudha Banu, M. Shafiya Sujitha, S. Daisylin Anbu Yuvaraj, S. Sujatha, S. Department of Information Technology Tamil Nadu Trichy India Meenakshi College of Engineering Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Rajalakshmi Engineering College Artificial Intelligence and Machine Learning Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Chemistry Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379945

This research study describes how Generative Adversarial Networks (GANs) are used to improve drug discovery molecular design. The goal is to maximize the discovery of potential drug candidates by making use of GANs' ability to produce a wide variety of chemical structures. The goal is to use better molecular design methods to speed up and improve the medication development process. The purpose of using GANs is to make it easier to build new molecular structures with certain features, such more potency and selectivity. This technology has the potential to bypass the drawbacks of conventional drug discovery techniques and speed up the process of exploring chemical space. It demonstrates that GANs have the potential to revolutionize drug development through computational simulations and validation tests. One potential way to find new treatments and solve medical problems that have so far gone unsolved is to include GANs into molecular design processes. Results from Drug Design Data Resource, Kaggle datasets shows that the molecular formula with id Activity-Based learning (ABL) minimum value starts with 0.055 and maximum of 10, from the sample of 10 compounds the minimum affinity value is 0.075 and maximum of 3.42. In another data surveyed the drug discovery values show the minimum of the molecular weight is 356.3 and maximum is 471.5. © 2024 IEEE.

关键词： Generative adversarial networks

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Advanced Deep learning Architectures for Real-Time Human Emotion Recognition and Behavioral Prediction

Advanced Deep Learning Architectures for Real-Time Human Emo...

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2024 International Conference on Communication, Computer Sciences and engineering, IC3SE 2024

作者： Reddy, C.H. Vijendar Reddy, N. V. Uma Sharma, Smita Kumar, J. Pradeep Lakhanpal, Sorabh Albawi, Ali Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Artificial Intelligence And Machine Learning Bangalore India Amity University Department Of Electronics & Communication Engineering Greater Noida India Mlr Institute Of Technology Department Of Information Technology Telangana Hyderabad India Lovely Professional University Phagwara India College Of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350366846

In the expanding domain of affective computing, the quest for real-time human emotion recognition and behavioral prediction has been significantly invigorated by advancements in deep learning architectures. This paper presents a comprehensive examination of novel deep learning frameworks that are at the forefront of accurately discerning a spectrum of human emotions and predicting subsequent behaviors in real time. These architectures harness the power of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and groundbreaking attention mechanisms to process and analyze complex emotional cues from multimodal data sources, including facial expressions, vocal intonations, and physiological signals. Enhanced by adaptive learning rates and novel regularization techniques, the proposed models demonstrate remarkable proficiency in mitigating overfitting and adapting to dynamic emotional states, thereby offering improved robustness and reliability over traditional methods. Empirical evaluations, conducted on diverse datasets, reveal significant enhancements in recognition accuracy and prediction speed, paving the way for real-time applications in areas such as interactive gaming, mental health assessment, and autonomous vehicles. Moreover, the exploration of transfer learning and generative adversarial networks (GANs) within these architectures provides insights into efficient training methodologies and the generation of synthetic emotional data for improved model robustness. This work not only advances the theoretical understanding of complex emotional recognition in AI but also illustrates practical implementations that stride toward empathetic and intuitive human-machine interactions. © 2024 IEEE.

关键词： Forecasting

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Hybrid Makes Better: Privacy-Preserving Medical Image Classification Based on Federated learning

Hybrid Makes Better: Privacy-Preserving Medical Image Classi...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Zhi Chi, Haotian Li, Yonggang Zhang, Yuxuan Jiang, Shunrong Du, Xiaojiang Guizani, Mohsen China University of Mining and Technology School of Computer Science Xuzhou221116 China Shanxi University School of Automation and Software Engineering Taiyuan030006 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350351255

The power of machine learning makes it available for medical image classification. However, machine learning requires large medical datasets to improve accuracy, which will involve patients' private information and lead to their privacy leakage. Federated learning is a trending technique to both protect patients' privacy and improve the accuracy of medical image classification. Unfortunately, current research shows that federated learning faces the risk of privacy leakage. In this paper, we propose a privacy-preserving scheme FHDM. Specifically, we construct a local hybrid differential privacy algorithm (LHDP) against patients' privacy leakage. This hybrid algorithm utilizes both Gaussian and Laplace differential privacy without enlarging the privacy budget. We prove that the algorithm applies to the model parameter. Moreover, we design loss optimization and global optimization strategies on the algorithm to achieve higher accuracy in medical image classification. Finally, we validate FHDM in terms of privacy-preserving and model accuracy on real datasets. Experiments show that FHDM effectively protects privacy and improves the average accuracy by 10.0% compared to adopting the LDP-based scheme with the same medical image dataset and privacy budget. © 2024 IEEE.

关键词： Federated learning

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A Survey on IoT Protocol in Real-Time Applications and Its Architectures 1

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3rd International Conference on Data Science, machine learning and Applications, ICDSMLA 2021

作者： Umashankar, M.L. Mallikarjunaswamy, S. Sharmila, N. Kumar, D. Mahesh Nataraj, K.R. Department of Artificial Intelligence and Machine Learning BMS Institute of Technology and Management Karnataka Bangalore560064 India Department of Electronics and Communication Engineering JSS Academy of Technical Education Karnataka Bangalore560060 India Department of Electrical and Electronics Engineering JSS Science and Technology University Karnataka Mysore570006 India

ISBN: (数字)9789811959363

ISBN: (纸本)9789811959356

In recent days, activities are completely automated and are more flexible because of Internet of Things [IoT]. Due to IoT devices, effective integrated communication between all types of embedded devices is possible. The devices should have communication in stipulated range through the support of Internet. This significant functionality is built by the IoT protocols. In the digital world, internet-connected devices has enabled the IoT protocols to be one of the significant fields in computing, and it acts as a bridge between the physical and the cyber-world. This paper gives a brief overview of Internet of Things (IoT) and standards used to it. Important IoT protocols and their review are presented. The similarities and dissimilarities of Advanced Message Queuing Protocol (AMQP) and Message Queue Telemetry Transport (MQTT) protocols are also discussed. The main aim of this review papers is to provide a functional view of IoT architecture, standards and protocols. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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AFHO-DL: Enhancing Energy Efficiency through Resource Allocation AI-enabled WSNs and IoT Integration

AFHO-DL: Enhancing Energy Efficiency through Resource Alloca...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： S A Kalaiselvan J. Manoranjini S. Hemalatha M. Lenin Kumar Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Electronics Communication and Engineering Vignan Institute of Technology and Sciences Telangana India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

In the realm of AI-enabled Wireless Sensor Networks (WSNs) and Internet of Things (IoT) integration, efficient resource allocation is paramount for enhancing energy efficiency and optimizing data utilization. The dynamic nature of environments poses challenges to achieving optimal performance measures, necessitating sophisticated solutions. To address this, we propose the AFHO-DL model, which combines the Fire Hawk Optimizer (FHO), UNet architecture, and Multi-objective Jaya algorithm. The FHO algorithm, inspired by the foraging behavior of Fire Hawks, efficiently optimizes resource allocation in WSNs by iteratively updating node positions based on objective functions. Leveraging FHO, we fine-tune the parameters of the UNet architecture, a deep learning model widely used in image processing applications. The UNet architecture, consisting of encoder and decoder paths, extracts features and reconstructs images, enhancing adaptability to various data types. Furthermore, the Multi-objective Jaya algorithm is employed to refine solutions by striking a balance between exploration and exploitation in the solution space, further improving resource allocation strategies. Our experimental results demonstrate the effectiveness of the proposed AFHO-DL model in enhancing energy efficiency and optimizing data utilization in dynamic environments. Through post-processing and visualization, we evaluate the performance of the optimization algorithms, generating Pareto-optimal solutions that represent the best trade-offs between competing goals. The AFHO-DL model facilitates intelligent decision-making and resource allocation strategy optimization, ensuring smooth data transfer and improving network efficiency in AI-enabled WSNs coupled with IoT platforms.

关键词： Wireless sensor networks Adaptation models Heuristic algorithms Scalability Quality of service Throughput Energy efficiency Resource management Internet of Things Optimization

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Proactive Alzheimer's Disease Identification Through EEG-Based Biomarkers

Proactive Alzheimer's Disease Identification Through EEG-Bas...

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artificial intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Gaurav Bhendare Leelkanth Dewangan Artificial Intelligence and Machine Learning Faculty of Engineering &Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India Artificial Intelligence and Data Science Faculty of Engineering &Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India

To detection of Alzheimer's disease (AD) therapy is crucial for patients. Because it enables patients to take prevention actions before brain damage takes place. Although inherited effects limit most computer detection methods, computers have been employed in numerous research to identify AD The “Electroencephalogram (EEG)” signal is used in this study to implement a deep learning detecting of Alzheimer's disease (AD). Initially, conventional internet databases are used to get the necessary EEG signal and there after used the The signal is divided into many current Neural Networks using the “Lifting Wavelet Transform (LWT)” decomposition level. Extracts the temporal properties from the signal that has been divided up.

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Role of machine learning in Climate Change Prediction and Mitigation

Role of Machine Learning in Climate Change Prediction and Mi...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Nayan Jikar Yash Tale Abhay Tale Aditya Barhate Prateek Verma Aman Jikar Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. machine learning (ML) has emerged as a transformative tool to address climate challenges through data-driven solutions, offering predictive capabilities for extreme weather events and enhancing the efficiency of clean-energy systems. This technology benefits the optimization of renewable energy generation, intelligent grid management, and resource utilization towards reducing greenhouse gas emissions. Moreover, ML promotes climate justice through targeted region-specific intervention and inclusive solution-making for the most vulnerable. Several technical and ethical challenges associated with data quality, scalability, and model generalization must still be overcome for ML to realize its potential. This paper reviews the role of ML in climate change prediction and mitigation discusses ongoing real-world applications and outlines the future research agenda necessary to address existing challenges. Integrating ML with emerging technologies like the Internet of Things (IoT) and explainable AI, alongside global collaboration and a focus on climate justice, will be essential in advancing effective climate action.

关键词： Climate change Renewable energy sources Ethics Prevention and mitigation Scalability Data integrity Collaboration Data models Internet of Things Meteorology

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A Multiclass Deep learning Approach using CRelu - to Enhance Brain Tumor Identification

A Multiclass Deep Learning Approach using CRelu - to Enhance...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Kusuma P Sheba Selvam Shreedevi S Ronihal Computer Science and Engineering BNM Institute of Technology Bangalore India Artificial Intelligence and Machine Learning BNM Institute of Technology Bangalore India Information Science and Engineering BNM Institute of Technology Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Research analyses the deployment of Convolutional-Neural-Networks (CNN) equipped with the CReLU (Concatenated Rectified Linear Units) activation function for identifying brain tumors MRI scan images. The CNN model is designed to be trained to classify various types of tumors, including glioma, meningiomas, pituitary tumors, and cases with no tumor present. By leveraging MRI images, the study evaluates how the CReLU activation function can enhance the model's capability to reliably distinguish between these tumor types, aiming to improve diagnostic accuracy and facilitate early detection through advanced image analysis techniques using deep learning.

关键词： Deep learning Electric potential Accuracy Image analysis Magnetic resonance imaging Computational modeling Brain tumors Computer architecture Brain modeling Reliability

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Italian-Chinese Neural machine Translation: results and lessons learnt 23

Italian-Chinese Neural Machine Translation: results and less...

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3rd ACM Conference on Information technology for Social Good, GoodIT 2023

作者： Delnevo, Giovanni Im, Marcus Tse, Rita Lam, Chan-Tong Tang, Su-Kit Salomoni, Paola Pau, Giovanni Ghini, Vittorio Mirri, Silvia Department of Computer Science and Engineering University of Bologna Cesena Italy Faculty of Applied Sciences Macao Polytechnic University China Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Ministry of Education Macao Polytechnic University China

ISBN: (纸本)9798400701160

Today, access to the Internet provides access to various forms of knowledge like free online lecture series offered by prestigious universities, massive open online courses, films and books, and Wikipedia. In addition, it is possible to join online communities on any topic of interest, get to know people with common interests, exchange thoughts and participate in debates. To enable access to these unprecedented knowledge bases, it is crucial to be able to translate texts into any language known by users. For this reason, machine Translation has been a very active research field for the last thirty years. In this paper, we investigate the task of Chinese-Italian translations by exploiting Neural machine Translation approaches. We trained several deep neural networks starting from two already available datasets containing Chinese-Italian parallel corpora. Then, we compared their performance against some of the most common machine translation services freely available online. In particular, we take advantage of Microsoft Translator, Google Translate, DeepL, and ModernMT. © 2023 Owner/Author.

关键词： Neural machine translation

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Particle Swarm Optimization for Efficient Brain Tumor Classification Using InceptionV3 Deep learning Model 9

Particle Swarm Optimization for Efficient Brain Tumor Classi...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Akhila, P. Prabaharan, G. Pandiiyan, K. Swarna, S.L. Hussain, A. Sakthivelu, U. B V Raju Institute of Technology Department of Artificial Intelligence and Machine Learning Telangana Narsapur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Srm Arts and Science College Department of Computer Science with Artificial Intelligence Kattankulathur Chengalpattu India Sona College of Technology Department of Computer Science Science and Engineering Junction Main Road Tamil Nadu Salem India College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350377972

This research proposes a novel framework for brain tumor classification utilizing a combination of deep learning and optimization techniques. The framework employs an autoencoder network for image preprocessing, followed by SegNet for accurate tumor segmentation. The core classification is performed using the InceptionV3 model, further optimized through Particle Swarm Optimization (PSO) to enhance predictive performance. The proposed system achieved a classification accuracy of 98.75% on a dataset of 7,023 MRI images, demonstrating its effectiveness in accurately classifying brain tumors. This research contributes to the advancement of medical image analysis and provides a valuable tool for supporting clinicians in the early diagnosis and treatment of brain tumors. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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