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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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Developing Real-World Applications with Algorithmic Intelligence in Reinforcement Learning for Robotics 2

Developing Real-World Applications with Algorithmic Intellig...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Bhanumathi, M. Sangeetha, M. Harin Fernandez, F. Mary Ganesh Ramachandran, A. Saravanan, S.K. Chennai India Department of Computer Engineering University of Technology and Applied Sciences Nizwa Oman Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Science and Engineering BigData and Network Security Saveetha School of Engineering Simats Tamil Nadu Chennai India

ISBN: (纸本)9798350384277

In the study, three leading reinforcement learning algorithms Deep Q-Network (DQN), Proximal Policy Optimization (PPO) and Asynchronous Advantage Actor - Critic (A3Cc are applied in a number of real-world robotics tasks. The focus of our analysis is on performance indicators, computational efficiencies across multiple environments as well with respect to customer satisfaction rating. The DQN demonstrated good performance, with the success rate of up to 85% in object manipulation tasks and confirmed its reliability under controlled lab conditions where it made only a 5 percent error. PPO excelled in autonomous navigation (82%) and was the most energy-efficient option, requiring just 10 hours for training and using only twelve KWH of power. Compared with A3C, the method performed to a higher level of adaptability and robustness, especially in object manipulation (90% success rate) and dynamic environments (10% error rates applicable for outdoor variable weather conditions). But A3C was also the most automatable, which required 15 hours to train and consumed an astonishing amount of electricity - 18 kWh. Regarding customer satisfaction, PPO performs the best in end-user rating on user interface;A3C shows a preference with engineers due to its advanced features and flexibility. The focus of this study is significant trade-offs between performance, efficiency and user preference in real robotics applications that advocate for individual tailoring selections as well as deployments based on task specifics nature. © 2024 IEEE.

关键词： Customer satisfaction

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Weak and Extremely Low Frequency Magnetic Fields Alter the Growth Rates of HT-1080 Fibrosarcoma

Weak and Extremely Low Frequency Magnetic Fields Alter the G...

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2025 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2025

作者： Dang, Nhat Keller, Jason Bajtos, Marek Gurhan, Hakki Barnes, Frank Department of Electrical Computer and Energy Engineering College of Engineering and Applied Science University of Colorado Boulder BoulderCO United States Department of Electromagnetics and Biomedical Engineering Faculty of Electrical Engineering and Information Technology University of Zilina Zilina Slovakia

ISBN: (纸本)9781946815200

This study presents experimental results showing that weak, extremely low frequency electromagnetic (EM) fields influence the growth rates of HT-1080 fibrosarcoma cells in vitro and offers a theoretical explanation for these effects. The results demonstrate that weak magnetic fields both increase and decrease the growth rates of fibrosarcoma cells by altering the mitochondrial superoxide (O2-) and calcium ions (Ca2+) concentrations in living cells. Over a 4-day exposure period, fibrosarcoma growth rates were modulated in the presence of low-level alternating current (AC) magnetic fields at a 10 μT amplitude at 13.5 Hz to 30 Hz frequency, superimposed on a 45 μT static background field. Effects due to changes in amplitude from 10 to 30 μT at selected frequencies are also investigated with the same superimposed static field. © 2025 USNC-URSI.

关键词： Electromagnetic field effects

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Enhancing Steel Melting Shop Operations by Object Detection Using Template Matching Algorithm 2

Enhancing Steel Melting Shop Operations by Object Detection ...

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2nd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2023

作者： Srinivasan, V. Parthasarathy, M. Kumar, M. Pavin Vaishnavi, S. Sri Ramakrishna Engineering College Department of Mechanical Engineering Coimbatore India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350381979

Ladle monitoring systems are essential for optimizing steel melting operations. They improve material and equipment flow, reduce wait times, increase overall production, enable quick problem response, and ensure steel quality. Traditional ladle tracking methods utilizing sensors require a wide frequency band for data transmission. However, sensor technology has limitations such as high cost, limited range, signal interference, poor integration, wear, and tear. Thus, an additional layer of security is needed. To address these issues, a proposed solution suggests implementing a machine vision system for ladle tracking and management in steel factories. This vision-based system uses a live video captured by a camera as input to detect the ladle. The template matching algorithm is employed to compare the ladle template with the image's covered window, allowing the system to identify the ladle's ID and the zone where it is located. Various techniques for template matching are compared, and the most suitable one for the application is selected based on the results. The matched templates are stored in a database using Python SQLite3. The ladle tracking positions can be accessed through a web application, which also provides the capability to track individual ladles. The results demonstrate that implementing a vision-based tracking system for ladles in steel factories offers optimal outcomes with minimal hardware expenses. © 2023 IEEE.

关键词： Object detection

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Deep Learning for Precision Medicine: Advancing Personalized Treatment Plans in Oncology

Deep Learning for Precision Medicine: Advancing Personalized...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Shrivastava, Anurag Kotiyal, Arnav Jain, Alok Devi, V.S Anusuya Rao, Bolleddu Devananda Bansal, Saloni Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India New Horizon College of Engineering Department of Applied Sciences Bangalore India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798331512965

A new approach to cancer care, precision medicine tailors treatments to each patient's specific genetic, molecular, and clinical characteristics. In order to analyze complicated, high-dimensional data and create individualized treatment plans, deep learning - a potent subfield of AI - has arisen as a game-changing tool in this area. Discover how deep learning methods are being used in precision oncology to improve cancer diagnosis, prognosis, and treatment planning in this groundbreaking article. A few examples of notable developments are the use of CNNs to medical image tumor identification and segmentation, RNNs to treatment outcome prediction, and LSTM models to patient survival. More effective and less harmful treatments are possible because too deep learning models that integrate multimodal data, such as genetic sequences, histopathological pictures, and electronic health records. These models can detect new biomarkers and forecast how different medications will affect individual patients. The clinical translation of deep learning models is hindered by a number of obstacles, despite these encouraging improvements. Problems including data scarcity, unequal distribution of classes, and unintelligibility are major roadblocks. Also, to make sure these models are accurate and reliable, they need to be standardized and validated thoroughly before they can be used in clinical processes. Fostering trust and acceptance in therapeutic contexts also requires addressing ethical problems, such as data security, possible biases in algorithmic predictions, and patient privacy. This work offers a thorough analysis of existing approaches, delves into the difficulties and moral concerns, and suggests avenues for further study to improve the use of deep learning in precision oncology. Personalized treatment regimens, made possible by deep learning, have the potential to revolutionize cancer care by vastly increasing patient outcomes. © 2025 IEEE.

关键词： Electronic health record

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IoDseC++: authenticated key exchange protocol for cloud-enable internet of drone communication

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9529-9542页

作者： Amin, Ruhul Jayaswal, Sakshita Sureshkumar, Venkatasamy Rathore, Balram Jha, Ananya Abdussami, Mohammad Department of Computer Science and Engineering IIIT Naya raipur Sector 24 Chhattisgarh Naya Raipur493661 India Department of Applied Mathematics and Computational Sciences PSG College of Technology Tamilnadu Coimbatore641004 India

In recent times, sensor node or smart camera can be embedded in the drones for the collection of important data with the help of Internet Technology. Several security threats/vulnerabilities may hamper the system while collecting data. Hence, strong protection against security attacks is essential in drone based data collection system. In this work, we have considered a drone based cloud enable architecture for collecting and sharing information. The aim of this paper is to design a lightweight security protocol which will provide secure communication and mutual authentication among the entities. The proposed protocol establishes temporary session key for each session in order to make the complete system security attacks free. We have simulated the proposed protocol for measuring security strength using Scyther simulation whose results show all the claimed parameters are private during protocol run. It also ensures the correctness of mutual authentication. The informal analysis confirms strong protection on all security threats related to proposed architecture. The performance analysis has also made which shows that the protocol is lightweight and achieves better efficiency in comparison with related published works. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Drones

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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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Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

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computer Systems science & engineering 2022年第9期42卷 1095-1107页

作者： Ayman Altameem Jaideep Singh Sachdev Vijander Singh Ramesh Chandra Poonia Sandeep Kumar Abdul Khader Jilani Saudagar Department of Computer Science College of Applied StudiesKing Saud UniversityRiyadh11495Saudi Arabia Department of Computer Science and Engineering Manipal University JaipurJaipur303007India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Information Systems Department Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals;these signals can berecorded, processed and classified into different hand movements, which can beused to control other IoT devices. Classification of hand movements will beone step closer to applying these algorithms in real-life situations using EEGheadsets. This paper uses different feature extraction techniques and sophisticatedmachine learning algorithms to classify hand movements from EEG brain signalsto control prosthetic hands for amputated persons. To achieve good classificationaccuracy, denoising and feature extraction of EEG signals is a significant step. Wesaw a considerable increase in all the machine learning models when the movingaverage filter was applied to the raw EEG data. Feature extraction techniques likea fast fourier transform (FFT) and continuous wave transform (CWT) were usedin this study;three types of features were extracted, i.e., FFT Features, CWTCoefficients and CWT scalogram images. We trained and compared differentmachine learning (ML) models like logistic regression, random forest, k-nearestneighbors (KNN), light gradient boosting machine (GBM) and XG boost onFFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFTfeatures gave the maximum accuracy of 88%.

关键词： Machine learning brain signal hand motion recognition braincomputer interface convolutional neural networks

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Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach

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Neutrosophic Sets and Systems 2025年 81卷 479-502页

作者： Abdo, Doaa A. Salama, A.A. Abdelmegaly, Alaa A. Mahmoud, Hanan Khadari Mahdi Applied statistics and insurance department faculty of commerce Mansoura university Mansoura Egypt Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Higher Institute of Advanced Management Sciences and Computers Al-Buhayrah Egypt Depatrment of applied statistics at the Nile Higher Institute of commercial sciences and Computer Technology in Mansoura Egypt

This study tackles the problem of missing data in migrant datasets by introducing a new framework that combines machine learning techniques with neutrosophic sets. These sets, which can represent uncertainty and ambiguity, are well-suited for managing the complex nature of missing information in sensitive fields like migration research. We test the effectiveness of KNN, SVM, decision tree, random forest, and Ada Boost algorithms on a migrant dataset, comparing their results using different imputation methods (mean/mode, model-based imputer (simple tree), and random values). Our research showed that our proposed approach, which used neutrosophic sets, improved imputation accuracy and strengthened model reliability. Our results underscored the potential of neutrosophic set-based machine learning for addressing missing data issues across various fields. © 2025, University of New Mexico. All rights reserved.

关键词： Adversarial machine learning

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