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AYUsearch: a repository to facilitate the biochemical understanding of natural products-derived chemical substances

Current science 2024年第6期126卷 706-713页

作者： Tilwani, Komal Patel, Abhishek Patel, Ghanshyam Rahevar, Mrugendra Dave, Gayatri P.D. Patel Institute of Applied Sciences Changa 388 421 India U & P U Patel Department of Computer Engineering Charotar University of Science and Technology Changa 388 421 India

Ayurveda - a traditional Indian medicinal (TIM) practice advocates a lifestyle regimen utilizing natural products to cure complex ailments. The famous Ayurveda classics, viz. Charak Samhita, Ashtanga Hridayam and Sushruta Samhita, elaborate on various plant and animal products, in which the descriptions are narrated in Sanskrit and often signified through a metaphor. The particulars are empirical and usually lack experimental evidence. Its efficacy needs to be ensured towards the gold standard of biomedical sciences. Indeed, an ordered catalogue on a natural product is quintessential to designing the validation strategies for TIM. We obtained evidence on 60 natural products with a mentioned history in Ayur-veda. For this, we manually curated a list of natural products-derived molecules (NDMs). Next, they were sorted into universally accepted chemical identifiers and processed into SDF formats. The 1004 gene targets were identified for 636 NDMs through molecular and reverse docking studies. Subsequently, the functional annotation of differentially expressed genes using KEGG pathways and Gene Ontology analysis unravelled the role of the scrutinized ligands on cell functions. AYUsearch (database) is an attempt to redefine the in-formation embedded in Ayurveda, where the biological role of natural chemical products is decoded through network pharmacology. © (2024), (Indian Academy of sciences). All Rights Reserved.

关键词： Gene Ontology

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Cyber Security Threats on Multifunctional Devices and Mitigation Techniques 2

Cyber Security Threats on Multifunctional Devices and Mitiga...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Al-Dmour, Nidal A. Farooq, Umer Sarwar, Irfan Iqbal, Muhammad Waseem Aqeel, Muhammad Khan, Wasim Ahmad Al Hamadi, Hussam Mutah University College of Engineering Department of Computer Engineering Jordan LGU Department of Computer Science Lahore Pakistan Superior University Department of Computer Science Lahore54000 Pakistan Superior University Department of Software Engineering Lahore54000 Pakistan NCBA&E Lahore School of Computer Science Pakistan Applied Science Research Center Applied Science Private University Amman11937 Jordan University of Dubai College of Engineering and IT PO Box 14143 United Arab Emirates

ISBN: (纸本)9798350335644

The risks discussed in this document are related to smart printers/ fax/ photocopiers/ or e-mail-enabled smart devices, also known as "document centers,". These devices can be purchased outright or rented every month. When an item reaches its end of life, it is either donated to a non-profit organization or returned to the manufacturer (OEM) or supplier at the end of a lease arrangement. These network-interconnected devices have an inherent susceptibility that most IT operations teams or information security professionals are unaware of. This effort will raise awareness of the dangers of losing a company's data. This discussion will focus on reported vulnerabilities, previous discussions, and given the author's view on the potential danger. Create instructions for potential users on how to use and disassemble these devices safely to prevent losing sensitive data. Lastly, the technological structure of two potential attack vectors against a document center of an organization will be demonstrated in this study. The study was conducted in government as well as the private sector, medium-sized enterprises (300 employees of each entity) settings, outside of normal business hours, with special authorizations and engagement, limiting the scope to a minimal set of devices. © 2023 IEEE.

关键词： Sensitive data

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MANA Cryptocurrency: Transactional Dynamics & Financial Metrics Study

MANA Cryptocurrency: Transactional Dynamics & Financial Metr...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Kumar, Amit Sharma, Neha Chauhan, Rahul Joshi, Kireet Raj, Ankush Iqbal, Mohammed Ismail Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India Graphic Era Hill University Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Computer Science & Engineering Uttarakhand Dehradun248002 India University of Jammu Department of Computer Science and IT India University of Technology and Applied Sciences Petroleum Engineering Department Nizwa Oman

ISBN: (纸本)9798350394412

This research investigates the emerging domain of the Metaverse, a digital realm that is significantly influencing social interactions and economic landscapes. This study aims to investigate the complex variables that contribute to the economic valuation of virtual properties on the Decentraland platform, which is a famous Metaverse platform. Despite the growing significance of the Metaverse and its status as a prominent economic force, there is a noticeable lack of empirical research examining the distinct dynamics of land valuation within this context. The current corpus of scholarly work mostly focuses on valuations in real-world contexts, resulting in a significant knowledge gap about the specific factors that influence land prices in the Metaverse. This research seeks to address the existing vacuum in knowledge by doing a comprehensive literature review and platform analysis. The primary objective is to provide insights into the various elements that influence the financial prospects of Metaverse lands. The anticipated results of this study are expected to provide significant contributions to the comprehension of Metaverse economics. Additionally, these findings will serve as a foundation for future research endeavors that aim to explore the convergence of virtual and physical realms in relation to financial value. © 2024 IEEE.

关键词： Decentralized finance

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Deep Learning in Remote Sensing for Climate-Induced Disaster Resilience: A Comprehensive Interdisciplinary Approach

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Remote Sensing in Earth Systems sciences 2024年第1期8卷 145-160页

作者： Padmaja, S.M. Naveenkumar, R. Kumari, N. P. Lavanya Pimo, Er. S. John Bindhu, M. Konduri, Bhagavan Jangir, Pradeep Department of Electrical and Electronics Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India Department of Computer Science and Systems Engineering AUCE Andhra University Visakhapatnam India Department of Computer Science and Engineering St. Xavier’s Catholic College of Engineering Nagercoil India Department of ECE Sri Venkateswara College of Engineering Sriperumbudur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Climate-induced disasters pose significant threats to human lives, infrastructure, and ecosystems. Deep learning techniques, combined with remote sensing, offer powerful tools for predicting, detecting, and mitigating the impacts of such disasters. This study presents a comprehensive interdisciplinary approach using a hybrid cascaded CNN-RNN model to perform spatio-temporal analysis, enhanced image segmentation with U-Net and SegNet architectures, and a hybrid CNN-LSTM-XGBoost ensemble for multi-hazard prediction. The proposed CNN-RNN model achieves an accuracy of 96.78%, significantly outperforming traditional models in predicting flood-prone areas, wildfires, and droughts. Key metrics such as Intersection over Union (IoU), precision, recall, and F1-scores are used to evaluate model performance, highlighting the model’s ability to accurately identify disaster-prone areas and improve disaster resilience. This research not only demonstrates the efficacy of deep learning for climate-induced disaster prediction but also paves the way for future integration of socio-economic data to refine disaster risk assessment. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate-induced disasters CNN-RNN Deep learning Disaster resilience Hybrid ensemble Multi-hazard prediction Remote sensing SegNet Spatio-temporal analysis U-Net

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A UWB Based Tracking and Management System for Streamlining Steel Ladle Operations 10

A UWB Based Tracking and Management System for Streamlining ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Srinivasan, V. Vaishnavi, S. Aravindh, V. Ashwin, V. Nishok, M. Sri Ramakrishna Engineering College Department of Mechanical Engineering Tamil Nadu Coimbatore India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384369

Ladle monitoring systems are crucial for optimizing steel melting operations, enhancing material and equipment flow, reducing wait times, boosting production, facilitating rapid problem-solving, and ensuring steel quality. While GPS and RFID are common tracking methods, GPS's accuracy is indoor limited, and RFID's precision is constrained by range and proximity. However, a proposed Ultra-Wideband (UWB) system, offering precise tracking by utilizing radio waves. UWB translates pulses into data, operating at a high frequency. UWB anchors transmit signals to UWB tags, calculating roundtrip time for location determination (Time of Flight). These values are converted into location coordinates using a plane position algorithm and stored in a database. A web app in the centralized control system retrieves and displays tracked data, including the ladle's current position, ensuring efficient material flow and real-time monitoring of steel quality. © 2024 IEEE.

关键词： ESP 32 Indoor Positioning System Plane position algorithm Real Time monitoring RFID and GPS Time of Flight Turtle Graphics UWB

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Detection of Mango Leaves Disease using CSUBW based Residual Attention with EfficientNet 2

Detection of Mango Leaves Disease using CSUBW based Residual...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kumar V, Sunil Jyothi, A.P. Nagamani, H.S. Sumanth, S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Bengaluru India Department of Computer Science Bengaluru India Department of Computer Science and Computer Applications Kolar India

ISBN: (纸本)9798350382952

Mangoes are one of the most popular fruits worldwide due to their taste and nutrition. Plant diseases may reduce plant quantity and quality. Disease management to improve crop quality and productivity requires early plant illness detection. Automation of plant disease detection reduces large farm surveillance. Since plants feed us, leaf problems must be diagnosed quickly and accurately. Our advanced learning method automatically detects leaf disease in several mango plant varieties. This study creates the RAM-EfficientNet, a novel illness classification network. The RAM block in EfficientNet extracts additional features and handles slight leaf lesion changes. Transfer learning was used to train EfficientNet on ImageNet to overcome the mango leaf disease sample size issue. The Cat Swarm Updated Black Widow Model (CSUBW), a hybrid optimization model, changes RAM-EfficientNet's weights to improve classification accuracy. Black Widow Optimization Algorithm (BWO) and Cat Swarm Optimization Algorithm (CSO) are combined to create the hybrid optimization model. The model's performance is then assessed to confirm its efficacy. © 2024 IEEE.

关键词： Fruits

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Towards Realistic Vibration Testing of Large Floor Batteries for Battery Electric Vehicles (BEV)

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Sound & Vibration 2022年第1期56卷 1-19页

作者： Benedikt Plaumann Faculty of Engineering and Computer Science Department Automotive and Aeronautical EngineeringHamburg University of Applied Sciences(HAW Hamburg)Hamburg20099Germany

This contribution shows an analysis of vibration measurement on large floor-mounted traction batteries of Battery Electric Vehicles(BEV).The focus lies on the requirements for a realistic replication of the mechanical environments in a testing *** the analysis on global bending transfer functions and local corner bending coherence indicate that neither a fully stiff fixation of the battery nor a completely independent movement on the four corners yields a realistic and conservative test *** contribution will further show what implication these findings have on future vibration&shock testing equipment for large traction ***,it will cover an outlook on how vibration behavior of highly integrated approaches(cell2car)changes the mechanical loads on the cells.

关键词： Battery electric vehicle BEV shock and vibration vehicle floor bending rechargeable energy storage system RESS

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An Ensembled Real-Time Hand-Gesture Recognition using CNN 15

An Ensembled Real-Time Hand-Gesture Recognition using CNN

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kavitha, M.N. Saranya, S.S. Prasad, M. Kaviyarasu, S. Ragunath, N. Rahul, P. Kongu Engineering College Department of Computer Science and Design Tamilnadu Erode India PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Tamilnadu Coimbatore India Controller of Examinations Tamilnadu Namakkal India Kongu Engineering College Bachelor of Science - Information Systems Tamilnadu Erode India

ISBN: (纸本)9798350370249

Hand sign recognition is a vital technology in the human-computer interaction, enabling individuals to communicate with machines naturally and effectively. An innovative approach for real-time hand sign identification with the help of CNN and OpenCV is introduced with the fusion of computer vision and deep learning that can accurately interpret and classify an extensive range of hand signs and gestures. This research contributes significantly to the fields of computer vision and human-computer interaction, offering a practical and efficient solution for hand sign recognition. The combination of CNN and OpenCV presents a promising avenue for enhancing accessibility and communication, especially in environments where verbal communication is limited or non-existent. The model is trained with multiple data so that the system can recognize the hand gestures more precisely. Pre-trained architectures like ResNet and MobileNet are combined with the CNN model using ensemble learning and the performance is improved when compared to all the three CNN architectures individually. The ensemble model provides better accuracy of 96 %. The potential applications of this technology are vast, from assisting the hearing-impaired in understanding sign language to more immersive and intuitive interactions. Overall, the approach holds the promise of bridging the gap between human gestures and machine understanding, opening new doors for meaningful interactions between individuals and intelligent systems. © 2024 IEEE.

关键词： Intelligent systems

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Domain-Scalable Unpaired Image Translation via Latent Space Anchoring

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 11707-11719页

作者： Huang, Siyu An, Jie Wei, Donglai Lin, Zudi Luo, Jiebo Pfister, Hanspeter Harvard University The John A. Paulson School of Engineering and Applied Sciences BostonMA02134 United States University or Rochester The Department of Computer Science RochesterNY14627 United States Boston College The Computer Science Department BostonMA02116 United States

Unpaired image-to-image translation (UNIT) aims to map images between two visual domains without paired training data. However, given a UNIT model trained on certain domains, it is difficult for current methods to incorporate new domains because they often need to train the full model on both existing and new domains. To address this problem, we propose a new domain-scalable UNIT method, termed as latent space anchoring, which can be efficiently extended to new visual domains and does not need to fine-tune encoders and decoders of existing domains. Our method anchors images of different domains to the same latent space of frozen GANs by learning lightweight encoder and regressor models to reconstruct single-domain images. In the inference phase, the learned encoders and decoders of different domains can be arbitrarily combined to translate images between any two domains without fine-tuning. Experiments on various datasets show that the proposed method achieves superior performance on both standard and domain-scalable UNIT tasks in comparison with the state-of-the-art methods. © 1979-2012 IEEE.

关键词： Image reconstruction

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Chaotic Fuzzy Logic for Enhanced Educational Q&A System 6

Chaotic Fuzzy Logic for Enhanced Educational Q&A System

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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Chen, Haoyuan Shi, Nuobei Chen, Ling Lee, Raymond BNU-HKBU United International College Department of Computer Science Zhuhai China School of Applied Science and Civil Engineering Beijing Institute of Technology Zhuhai China

ISBN: (纸本)9798350374315

Online question-and-answer (Q&A) platforms are replete with extensive human resources support. This paper proposes a novel methodology of a customized Large Language Model (LLM) called Chaotic LLMs-based Educational Q&A System (CHAQS) to navigate the complexities associated with intelligent Q&A systems for educational sector. It uses an expansive dataset comprising over 383,000 educational data pairs, an intricated fine-tuning process encompassing p-tuning v2, Low Rank Adaptation (LRA), and strategies for parameter freezing at an open-source Large Language Model ChatGLM as baseline model. Also, Fuzzy Logic is implemented to regulate parameters, system's adaptability with Lee-Oscillator to refine model's response variability and precision. Experiment results showed that 5.12% improvement in Precision score, 11% increase in Recall metric, and 8% improvement in F1 score as compared to other models. © 2024 IEEE.

关键词： AI-based QA System Education Fuzzy Logic Large Language Model Lee-Oscillator

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