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AI-Driven Drug Discovery: Computational Methods and Applications

AI-Driven Drug Discovery: Computational Methods and Applicat...

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Recent Trends in computer Science and Technology (ICRTCST), International Conference on

作者： Gali Nageswara Rao C. Gunasundari S. B G Tilak Babu Pavithra G Vijay Kumar Dwivedi Bazani Shaik Department of Information Technology Aditya Institute of Technology and Management Srikakulam Andhra Pradesh Department of Computer Science and Engineering Roever Engineering College Perambalur Tamilnadu Department of ECE Aditya Engineering College Surampalem Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Bangalore Karnataka Department of Mathematics Vishwavidyalaya Engineering College Surguja Chhattisgarh Department of Mechanical Engineering Ramachandra College Of Engineering Eluru Andhra Pradesh India

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

Artificial intelligence (AI) and its revolutionary effects on the medication development process. The project explores the potential integration of artificial intelligence tools into the different phases of drug development using state-of-the-art computational methods. This project’s overarching goal is to assess how well target selection, lead compound optimization, and toxicity prediction are served by data analytics, predictive modelling, and machine learning algorithms. This research aims to analyze contemporary uses and breakthroughs in artificial intelligence (AI) to better understand how it improves drug discovery pipeline efficiency and accuracy. Beyond this, it delves into the challenges and opportunities of AI-driven drug discovery, with an emphasis on finding fresh approaches to old biomedical problems. The dynamic environment at the crossroads of AI and pharmaceutical sciences is better-understood thanks to this comprehensive analysis. It opens the door to more efficient drug development processes and the development of new therapeutic approaches.

关键词： Drugs Toxicology Machine learning algorithms Pipelines Predictive models Market research Data models

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A Deep Learning Approach for Automated Skin Disease Classification and Diagnosis

A Deep Learning Approach for Automated Skin Disease Classifi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Helina Rajini Suresh M. Amutha A. Tamizharasi A. P. Jaya Krishna S. Lalitha P Nithya Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of ECE Sona College of Technology Salem Tamil Nadu India Department of Computer Science and Engineering R.M.D. Engineering College Kavarapettai Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamilnadu India Department of Computer Science and Engineering Arunai Engineering College Tiruvannamalai Tamil Nadu India

Everyone is ware that a wide range of problems affect skin structure, function, and appearance. Dermatological issues or dermatoses are common names used to represent the skin related illness. Early detection of skill llness is possible by employing Artificial Intellengence. A wide range of artificial intelligence techniques have been devised to promptly detect and diagnose skin problems. In this article we are proposing Mobilenetv2 and LSTM algorithms to identify skin disorders at an early stage. The combination of MobileNetV2 and LSTM (Long Short-Term Memory) for skin disease detection involves leveraging a convolutional neural network (CNN) for image feature extraction and a recurrent neural network (RNN) with LSTM units for sequence modeling, this architecture aims to capture both spatial and temporal dependencies in skin image data, making it well-suited for skin disease detection tasks. The method shown in this study can identify five distinct diseases with ease, and with accuracy rates of 99.2%.

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Analysis of Network Technologies and Cyber security Assessment for Enhancing Machine Learning, Grid Computing and Cyber-Physical Connectivity Internetwork Effectiveness 2

Analysis of Network Technologies and Cyber security Assessme...

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2nd International Conference on Innovative Practices in Technology and Management, ICIPTM 2022

作者： Dhanalakshmi, B. Singh, Chaitanya Surya, S. Hedage, Atharav Kuchhal, Shikha Tongkachok, Korakod B.S. Abdur Rahman Crescent Institute Of Science And Technology Computer Science And Engineering Tamilnadu Vandalur India Swarrnim Startup Innovation University Computer Engineering Department Gujarat Gandhinagar India School Of Computing Kalasalingam University Computer Science And Engineering Tamilnadu Sirivelliputhur India Vishwakarma Institute Of Information Technology Electronics And Telecommunications Pune Belgaum Karnataka India Spitm Dcrust Department Of Ece Murthal India Thaksin University Faculty Of Law Thailand

ISBN: (纸本)9781665466431

The fast improvement of the Internet has incredibly worked with individuals spreading information worldwide, prompting the Internet turning into a pivotal apparatus in many fields like science, trade, schooling, etc. With the Internet common in such countless parts of day to day existence, network security has become basic and requests consistent consideration. For this situation, an IDS's motivation is to recognize between real organization associations and possibly hurtful ones. The recommended method in this examination centers around an interruption identification and avoidance system that comprises of Cloudlet Controller (CC), Trust Authority (TA), and Virtual Machine Manager to recognize questionable practices in a cloud climate. Cloud clients are migrated to different regions in the climate, as indicated by our proposed approach. Whenever information bundles from different clients are gathered, network traffic happens. To resolve this issue, we assembled the Cloudlet Controller, which gets bundles from different clients through switch. CC gets parcels until they arrive at their edge level;if the cloudlet is over-burden, the bundles are shipped off inactive cloudlets. In this philosophy, network traffic is decreased, and it is significantly helpful in identifying interruptions in a straightforward way. In this review, a cross breed arrangement based interruption location and counteraction framework on the cloud is worked to distinguish gatecrasher exercises in the framework. A cross breed characterization based interruption location framework is created with a few cloudlets, a cloudlet regulator, a confided in power, and a virtual machine supervisor to distinguish interloper assaults in the framework, as well as a deterrent instrument to safeguard bundles from gatecrashers. © 2022 IEEE.

关键词： Machine learning

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An Efficient Clustering Approach Broadcast Scheduling Process in Heterogeneous Vehicular Communication

An Efficient Clustering Approach Broadcast Scheduling Proces...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： S. Vamsi Ujwala M. Venkatanaresh V Jai Kumar Amneh Alamleh Prasad A Hatwalne M Senthil Murugan Dept of MBA QIS College of Engineering and Technology Andhra Pradesh IN Department of ECE School of Engineering Mohan Babu University Tirupati Andhra Pradesh India Department of ECE QIS College of Engineering and Technology Ongole AP IN Department of Computer Science Faculty of Information Technology Jordan Zarqa University Zarqa University of Business and Technology (UBT) Jeddah SA Yeshwantrao Chavan college of Engineering Nagpur Maharashtra India Department of Electronics and Communication Engineering St. Joseph's Institute of Technology OMR Chennai

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

An ECABS proposed model introduces a fresh approach to vehicular communication in Vehicular Ad Hoc Networks (VANET) Using a multi-band network architecture with frequencies of 6 GHz, 5.9 GHz, and 60 GHz, ECABS forms a hierarchical multi-hop structure that dynamically adapts cluster formations based on real-time and historical vehicle data, optimizing data transmission, and improving network stability when fading. The model assigns vehicles to cluster heads (CH) and cluster members (CM) based on connectivity and mobility patterns, ensuring efficient communication. Experimental simulations conducted on Ubuntu OS ns-3 demonstrated that ECABS excels beyond the three current models BIOS, P20-ACH, and various CSCFs. ECABS achieved a data success rate of 102.84%, the lowest data loss rate of 12.11 %, and reduced the average). delay to 84.78 milliseconds. It also had the highest network throughput of 401.87 Kbps and the lowest routing load of 187.45 packets. ECABS has shown to be highly effective in enhancing communication reliability, reducing delays, and optimizing resource utilization in VANETs over time, making it a promising candidate for future intelligent transportation systems.

关键词： Adaptation models Biological system modeling Vehicular ad hoc networks Routing Data models Stability analysis Real-time systems Delays Vehicle dynamics Load modeling

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The Efficient Energy Routing Strategy Making Through Hybrid Techniques of ANFIS and SVM

The Efficient Energy Routing Strategy Making Through Hybrid ...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： M Shanmathi T Thirumurugan S Suhasini N Manoj Kumar Hayder Saadoon Abdulaali Huthaifa Alani Imad Al-Din Hussein Al-Saidi Department of electronics and communication engineering Saveetha engineering college Chennai department of ece Christ college of engineering & technology Puducherry cvr college of engineering hyderabad Department of computer science Soundarya institute of management and science bangalore karnataka College of technical engineering national university of science and technology dhi qar iraq Al-Farahidi University Baghdad Iraq Computer Engineer Department Al-Kunooze University College basra iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Due to their reliance on batteries for power, conventional wireless sensor networks (WSNs) continue to have significant energy-related limitations. Consequently, prolonging the lifespan and enhancing the performance of WSNs requires improved energy efficiency. The aim of this study is to address WSN energy utilisation by selecting relay nodes and cluster heads (CH) using adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM). The CH is required for data transmission and collection from all other nodes in the cluster, and the book concentrates on advanced optimisation approaches to reduce power usage at this node. By dramatically decreasing energy consumption at the relay node and CH by 30%, the recommended optimisation strategy enhances WSN’s energy-efficient operations and extends its lifespan. This paper presents the development and impact of the revolutionary optimisation technique.

关键词： Support vector machines Wireless sensor networks Energy consumption Routing Energy efficiency Magnetic heads Batteries

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Signal Processing Techniques for Enhanced Wireless Network Performance

Signal Processing Techniques for Enhanced Wireless Network P...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： G. Nirmala M P Haripriya Mohammed Rizwan Shaik S. Vijayalakshmi Jason Elroy Martis M.Dinesh Babu Department of Computer Science and Engineering R.M.D. Engineering College Chennai India Department of Computer Science Kristu Jayanti College (Autonomous) Bengaluru Karnataka India Department of Electrical & Electronics Engineering College of Engineering & Computer ScienceJazan University Jazan K.S.A Department of ECE R.M.K. Engineering College Chennai India Department of ISE NMAM Institute of Technology Nitte Deemed to be University Mangaluru Karnataka India Department of Mechanical Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

With increasing complexity and data rates of wireless communication networks, an effective signal processing technique is significantly required. Memoryless predistortion techniques undoubtedly help to minimise nonlinear distortions in RF power amplifiers. This is so because they guarantee high amplifier output. Methodology: This paper presents a new multichannel wireless signal processing technique to give wireless communication systems memoryless predistortion. The predistortion approach is efficiently applied over numerous channels using wireless signal processing resources. This method lets run in real time with minimum unnecessary hardware overhead. Findings: The experiments reveal that the method based on wireless signal processing is an effective approach for correcting for nonlinear distortions developing in RF power amplifiers.

关键词： Wireless communication Radio frequency Wireless networks System performance Nonlinear distortion Signal processing Throughput Predistortion Real-time systems Testing

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Experimental Evaluation of Pancreatic Cancer Identification based on CT Images by using Intelligent Deep Learning Procedure

Experimental Evaluation of Pancreatic Cancer Identification ...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Ramachandran. R J. Anvar Shathik J. Sivakumar P. Manothini G. S. Jackulin Asha M. Venkatanaresh Department of Biomedical Engineering Rajalakshmi Engineering College Chennai Department of Computer Science & Engineering Anjuman Institute of Technology and Management Belalkanda Bhatkal Karnataka Department of Electronics and Communication Engineering St. Joseph's College of Engineering Chennai Department of CSE(IoT) Nandha Engineering College Erode Department of Artificial intelligence and Data Science St. Joseph College of Engineering Sriperumbudur Department of ECE School of Engineering Mohan Babu University Tirupati Andhra Pradesh

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Pancreatic cancer is one of the most aggressive malignancies, necessitating early and accurate detection for effective treatment. This study proposes an intelligent deep learning framework for pancreatic cancer identification using Computed Tomography (CT) images. The methodology involves advanced preprocessing techniques, including noise reduction, contrast enhancement, and segmentation, followed by a deep learning-based classification approach. The proposed model integrates a CNN backbone with an attention mechanism, enhancing its ability to focus on critical pancreatic regions. Additionally, data augmentation strategies such as rotation, flipping, and brightness adjustments improve the model's generalization ability. Performance evaluation using 10-fold cross-validation demonstrates that the proposed model achieves 95.8% accuracy, 94.5% precision, 96.7% recall, and an F1-score of 95.6%, outperforming baseline models, including ResNet-50 (90.3% accuracy) and EfficientNet-B0 (93.6% accuracy). Explainability techniques such as Grad-CAM visualization provide interpretability, highlighting the regions influencing predictions. Comparative analysis confirms the model's superior capability in distinguishing pancreatic cancer cases from healthy subjects. The impact of data augmentation is also assessed, showing significant improvements in model robustness. The high specificity (95%) and AUC-ROC (97.3%) further validate the effectiveness of the proposed framework in medical imaging applications. This research underscores the potential of deep learning in early pancreatic cancer detection, contributing to improved diagnostic accuracy. Future work will focus on multi-modal data integration, including clinical and histopathological information, to further enhance model reliability and deployment in real-world clinical settings.

关键词： Deep learning Performance evaluation Accuracy Computed tomography Noise reduction Pancreatic cancer Data augmentation Data models Robustness Biomedical imaging

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LiFiData: A Secured Data Communication over Defense Fields by using Intelligent Visible Light Assisted Transmissions

LiFiData: A Secured Data Communication over Defense Fields b...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： L. Chithra S. Padma Farzhana. I L. Saravanan S. Krishnakumari Department of ECE S.A.Engineering College Chennai Department of EEE Sona College of Technology Salem Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of ECE PERI Institute of Technology Chennai Department of Electronics and Communication Engineering Loyola ICAM College of Engineering and Technology Chennai

The electronic industry is currently experiencing a transformation due to the rise of solid state devices. Light-Emitting-Diode (LED) are the quintessential solid-state gadget example. LEDs' are a viable alternative to both incandescent and fluorescent lighting. They produce little to no heat and can save you money. As an added bonus, LEDs have a far longer lifespan than conventional light bulbs. LEDs generate higher-quality light in terms of color temperature than both incandescent and fluorescent bulbs. In addition, they can rapidly cycle across a wide range of light intensities. This system must be safe, cheap, and environmentally benign in order to successfully initiate electronic communication utilizing visible light. The suggested system uses a Arduino microcontroller in the transmitting portion, where a high-brightness LED light serves as the channel for communication and a transistor, BC 547, is employed to transform 5v to *** a microcontroller receives data from a computer, it decrypts the information by first converting it to binary, and only then sending it to the led as a digital pulse. The lightbegins to blink as data is transmitted to the receiver; the photo diode begins to sense light, generating electric pulses; the amplifier processes these pulses into data; the data reaches the Arduino, where it can be viewed in the program's serial monitor. For this transmission, direct line-of-sight is essential; however interference from other sources can improve the results in a dark environment. In the lab, LiFi has reached very high speeds, and it also outperforms Wi-Fi in terms of bandwidth, efficiency, availability, and security. This study provides a clear, experimental perspective on the benefits of LiFi technology as well as its potential future applications. However, the hybrid model that incorporates the WiFi and LiFi technologies is expected to see significant growth in demand in the telecommunications industry.

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Enhancing Quality-of-Service in LoRa Low-Power Wide-Area Networks via Multi-Hop Optimized Radio Resource Management

Enhancing Quality-of-Service in LoRa Low-Power Wide-Area Net...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Laith H. Jasim Alzubaidi G Sunil Hirald Dwaraka Praveena N. Sathishkumar Manjula B. M The Islamic University Najaf Iraq School of Computer Science and Artificial Intelligence SR University Warangal India Department of ECE Erstwhile Sree Vidya Nikethan Engineering College School of Engineering Mohan Babu University Tirupati Andhra Pradesh India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

In transport systems in particular, LoRa wireless communication has become an essential technology for intelligent critical infrastructures. Optimizing transmission policy parameters, including carrier frequency, bandwidth, coding rate, and spreading factor, is crucial for achieving excellent Quality of Service (QoS) for LoRa systems; however, current methods have not fully optimized all LoRa parameters. In order to efficiently share information, the proposed Multiobjective Q Learning based LoRa (MQ-LoRa) uses a hybrid topology consisting of a cluster root rotating tree, wherein IoT nodes adhere to a cluster topology inspired by cellular tissues, while gateways follow a tree topology. For cluster construction, a membrane-inspired clustering algorithm is created, and influence scores are used to determine which header node is best. The study presents Data QoS Ranking for IoT nodes, which uses the new LoRa frame structure field QoS Ranking (QRank) to classify data into priority and non-priority categories. The Soft Actor Critic (SAC) fast deep reinforcement learning model is used for optimal transmission policy enforcement, taking into account signal-to-interference-plus-noise ratio, QRank, and other environmental characteristics. After that, the transmission policy is optimized taking carrier frequency, bandwidth, coding rate, and spreading factor into account. Furthermore, a multi-hop routing method with concurrent optimization that uses shuffling shepherd and mayfly optimization rates routes according to fitness standards. A multi-weighted sum approach is used to establish a weighted duty cycle in LoRa IoT networks to minimize resource inefficiency and loss of information. Using the NS3.26 LoRaWAN module, performance evaluation measures energy consumption, latency, throughput, packet receipt ratio, and packet rejection ratio, among other parameters. Comparison with popular LoRa techniques shows that the suggested MQ-LoRa performs better energy consumption of 56 mJ

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AI-Optimized Hardware Design for Internet of Things (IoT) Devices

AI-Optimized Hardware Design for Internet of Things (IoT) De...

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Recent Trends in computer Science and Technology (ICRTCST), International Conference on

作者： P Vishnu Kumar Adokshaja Kulkarni Dinesh Mendhe Durgesh Kumar Keshar S. B G Tilak Babu N Rajesh Department of CSO G Pullaiah College of Engineering and Technology Kurnool Andhra Pradesh Department of Computer Science Engineering Tontadarya College of Engineering Gadag Karnataka Office of Research Computing Rutgers the State University of New Jersey New Brunswick New Jersey Department of Computer Science Kalinga University New Raipur Chhattisgarh Department of ECE Aditya Engineering College Surampalem Department of Electronics and Communication Engineering Maharaja Institute of Technology Mysore Karnataka

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

The Internet of Things (IoT) has emerged as a transformative paradigm, connecting billions of devices to the Internet and enabling seamless communication and data exchange. As the IoT ecosystem continues to expand, there is an increasing demand for energy-efficient, high-performance hardware tailored to the unique requirements of AI-powered applications deployed on IoT devices. This research focuses on the design and optimization of hardware architectures to meet the computational demands of AI algorithms in IoT environments. The proposed AI-optimized hardware design leverages the latest advancements in semiconductor technology and integrates specialized processing units for efficient execution of machine learning tasks. The architecture is tailored to address the constraints of IoT devices, including limited power resources, stringent size constraints, and the need for real-time processing. Key components of the design include low-power processors, hardware accelerators, and memory hierarchies optimized for AI workloads. Additionally, the system employs advanced algorithms for task offloading and distributed processing, enabling collaborative and energy-efficient execution of AI tasks across a network of interconnected IoT devices. The proposed AI-optimized hardware design is evaluated through simulations and real-world experiments, demonstrating superior performance in terms of energy efficiency, processing speed, and scalability. The results showcase the potential of the designed hardware to unlock new possibilities for AI applications in diverse IoT scenarios, ranging from smart cities and industrial automation to healthcare and environmental monitoring. The proposed research serves as a foundation for the development of next-generation IoT devices capable of seamlessly integrating AI capabilities while meeting the stringent requirements of the IoT ecosystem.

关键词： Machine learning algorithms Smart cities Scalability Ecosystems computer architecture Hardware Energy efficiency

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