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A Survey on Evolutionary Computation Based Drug Discovery

IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Yu, Qiyuan Lin, Qiuzhen Ji, Junkai Zhou, Wei He, Shan Zhu, Zexuan Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science University of Birmingham Bir-mingham UK Department of Computing The Hong Kong Poly-technic University Hong Kong Hong Kong

Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniques. Evolutionary computation (EC) offers promise as most drug discovery problems are essentially complex optimization problems beyond conventional optimization algorithms. EC methods have been widely applied to solve these complex optimization problems especially in lead com-pound generation and molecular virtual evaluation, substantially speeding up the process of drug discovery and development. This article presents a comprehensive survey of EC-based drug discovery methods. Particularly, a new taxonomy of the methods is provided and the advantages and limitations of the methods are reviewed. In addition, the potential future directions of EC-based drug discovery are discussed and the publicly available resources including databases and computational tools are compiled for the convenience of researchers seeking to pursue this field. IEEE

关键词： Lead compounds

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Language Models over Large-Scale Knowledge Base: on Capacity, Flexibility and Reasoning for New Facts 31

Language Models over Large-Scale Knowledge Base: on Capacity...

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31st International Conference on computational Linguistics, COLING 2025

作者： He, Qiyuan Wang, Yizhong Yu, Jianfei Wang, Wenya College of Computing and Data Science Nanyang Technological University Singapore Paul G. Allen School of Computer Science & Engineering University of Washington United States School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798891761964

Advancements in language models (LMs) have sparked interest in exploring their potential as knowledge bases (KBs) due to their high capability for storing huge amounts of factual knowledge and semantic understanding. However, existing studies face challenges in quantifying the extent of large-scale knowledge packed into LMs and lack systematic studies on LMs' structured reasoning capabilities over the infused knowledge. Addressing these gaps, our research investigates whether LMs can effectively act as large-scale KBs after training over an expansive set of world knowledge triplets via addressing the following three crucial questions: (1) How do LMs of different sizes perform at storing world knowledge of different frequencies in a large-scale KB? (2) How flexible are these LMs in recalling the stored knowledge when prompted with natural language queries? (3) After training on the abundant world knowledge, can LMs additionally gain the ability to reason over such information to infer new facts? Our findings indicate that while medium-scaled LMs hold promise as world knowledge bases capable of storing and responding with flexibility, enhancements in their reasoning capabilities are necessary to fully realize their potential. © 2025 Association for computational Linguistics.

关键词： Structured Query Language

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Impact of "COVID-19 Pandemic" on Children Online Education: A Review and Bibliometric Analysis 4th

Impact of "COVID-19 Pandemic" on Children Online Education: ...

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4th International Conference on computing, Communications, and Cyber-Security, IC4S 2022

作者： Sawant, Rhea Wagle, Shivali Amit Harikrishnan, R. Srideviponmalar, P. Pune India Department of Computational Intelligence School of Computing Faculty of Engineering Technology SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9789819914784

The global "pandemic" of 2020, "COVID-19," left us all enclosed within our homes for over a year, with 3 to 4 waves adding up to a total of over six million deaths, forcing us to learn to operate from home. The elderly ditched their walks in the park, the adults realized to work and network from home, and the children quit going out to play at school. The school where they learned not only academically but also experienced holistic development. Adults have the patience and the maturity to understand the need to stay indoors to avoid a fatal viral infection. Still, children aged 2–5 years were compelled to remain home without correctly understanding "why." This paper is a bibliometric survey of these children’s challenges in the last two years. It analyzes the research by the geographical locations they belong to, domains, influential authors, organizations, and funding agencies. The material was obtained from the data from 2020 to 2022 from extensive Scopus databases. "Bibliometric analysis" is referred to as the analytical statistics of content that is widely published as articles, papers presented in renowned conferences, and reviews for a better understanding of the impact that the particular publication has had in the research domain all over the world. Further, for visualization analysis, freely available tools like "GPS Visualizer" are used alongside "Gephi," "VOS viewer," "scienceScape," and word cloud as well. The visualization enables an easier comprehension of holistic perspectives. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 111-136页

作者： SwornaKokila, M.L. Venkatarathinam, R. Rose Bindu, Joseph P. Manivasagam, M.A. Kishore, Kakarla Hari Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of Information Technology Sri BalajiChockalingam Engineering College A.C.S. Nagar Irumbedu Thiruvannamalai District Tamil Nadu Arni India Department of Mathematics Dayananda Sagar College of Engineering Bangalore India Computer Science and Engineering Siddartha Institute of Science and Technology Andhra Pradesh India Department of Electronics and Communication Engineering KoneruLakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India

Smart grids have developed as a potentially game-changing strategy for controlling the demand and supply of energy. Unfortunately, peak demand is a significant source of grid instability and rising energy prices, making it one of the most critical difficulties in smart grids. During times of high energy demand on the grid, demand response (DR) strategies incentivize consumers to change how they use energy. This study’s overarching goal is to learn how DR methods may be used to help smart grids make better use of their energy resources. The primary research is to develop a smart DR system that can predict times of high energy demand and proactively alter usage to reduce such periods. Machine learning strategies are utilized in the proposed system to estimate peak demand via past data, weather predictions, and other variables. The system will then alter energy use based on real-time data from smart meters along with other sensing devices to meet the projected demand. The simulation model will include several scenarios for testing the DR system’s flexibility, including a range of weather conditions, load profiles, and grid topologies. Several indicators, including peak demand reduction (80.04%), energy savings (38.09%), environmental consequences, and reaction time ( © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Smart power grids

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Implementation of Power Meter System Security in Smart Grids with QRNG Method 5

Implementation of Power Meter System Security in Smart Grids...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Patel, Aum Shrivastava, Simran Sujatha, G. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Faculty of Engineering and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350383522

Smart grids face emerging threats as connectivity expands attack surfaces. To strengthen smart meter data encryption through optimized integration of a cloud-based quantum random number generator (QRNG) service. QRNGs leverage intrinsic quantum properties to produce true randomness for cryptographic keys. An architecture is presented utilizing a QRNG service on the IBM quantum computing platform. Quantum entropy is remotely sampled and delivered on-demand via APIs to generate quantum random AES encryption keys for smart meter communications. Homomorphic encryption secures credential transmission. Compared to mathematical pseudorandom number generators, the quantum origin provides stronger security against brute force attacks and future quantum computing decryption. Tailoring QRNG generation and integration focuses on the performance, cost, and deployment constraints of grid-wide encryption hardening. The optimized approach aims to maximize randomness while minimizing complexity. By hardening cryptography via readily available quantum key generation, the QRNG integration delivers indispensable security enhancement. © 2024 IEEE.

关键词： Cryptography cryptography protocols cybersecurity encryption privacy QKD QRNG quantum entropy quantum key distribution quantum random number generator quantum uncertainty security smart grid smart meter true randomness

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Research on an Intelligent Evaluation System for Used Car Prices Based on Improved Machine Learning Algorithms

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第2期8卷 461-477页

作者： Lin, Xianning Hashim, Salwa School of Information Technology Guangdong Technology College Zhaoqing526100 China Faculty of Engineering Computing and Science Swinburne University of Technology Kuching93350 Malaysia

The advent of the big data era offers great opportunities for the development of online trading platforms for used cars. However, problems such as lack of appraisal data, asymmetric appraisal information and high cost of the appraisal process can limit the use of the three traditional used car price appraisal methods. Therefore, it is important to design an accurate and reasonable used car valuation method in the current immature market. As an advanced machine learning model, the Random Forest algorithm has strong advantages in terms of prediction accuracy, missing value handling and noise tolerance. Therefore, this paper introduces the Random Forest model into the study of used car value evaluation and improves the Random Forest model. Firstly, based on the characteristic price theory, 16 indicators were selected as characteristic variables among the factors influencing the value of used cars, and divided into 7 physical variables, 6 functional variables and 3 market variables. Secondly, aiming at the problems of poor training accuracy and relatively poor voting ability, an Inertia Bacterial Foraging Optimization Random Forest algorithm (IBFO-RF) is proposed. A multiplicative weight is set for all decision trees to match their training accuracy, and then the basic parameters of the Random Forest algorithm are optimally selected iteratively using inertia weighted bacterial foraging optimization to improve the output correctness. Finally, the data is imported into the random forest model, and according to the average decreasing degree of mean square error, an intelligent evaluation system for evaluating the value of used cars is established. It was validated against other algorithms on four datasets in the UCI dataset. The experimental results show that the validation of the proposed IBFO-RF algorithm can effectively improve the accuracy of the final classification and has significant advantages in handling the classification problem. The proposed intelligent used ca

关键词： Mean square error

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Deep Learning to Effectively Detect and Eliminate Real-Time Network Intrusions 3

Deep Learning to Effectively Detect and Eliminate Real-Time ...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Harikesh, S. Sujatha, G. Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai India

ISBN: (纸本)9798331527549

Network traffic has been growing exponentially for the past several years. As a result, it has become more and more difficult to detect cyber intrusions - especially in unbalanced environments, where the attacks are usually covered up by normal traffic. This investigation is set to deal with the problem by coming up with an intrusion detection system that uses a deep learning approach to effectively find and eliminate the unbalanced network intrusions. The process includes data preprocessing for balancing the classes, feature engineering for choosing optimal features, and comparison of CNN and RNN models for improving detection accuracy. The tests have shown precision of 93% and recall of 88% which in total brought 99.8% accuracy. Also, the real-time deployment has demonstrated the model’s ability to dynamically traffic patterns, which induced false positives to be reduced remarkably. The results help in the improvement of intrusion detection systems by providing a scalable, adaptive, and efficient way to handle constantly altering cyber threats. © 2025 IEEE.

关键词： Network intrusion

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Interpretation of Electroencephalogram using RNNs to Predict Cognitive Disorders 3

Interpretation of Electroencephalogram using RNNs to Predict...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Moovendran, Manikandan Surendran, S. Midhun, C. Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India SRM Institute of science and technology Chennai603203 India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532420

EEG signals are very useful in giving an insightful view of what's going on inside the brain. However, such readings are still tricky and challenging to interpret because of the inherent non-linearity and dynamic behavior of the data. The present paper introduces a new approach to the analysis of EEG signals using Recurrent Neural Networks, with a particular focus on recognizing temporal dependencies that exist in sequential data. By exploiting the RNN property of information retention at different time steps, we demonstrate enhanced accuracy in pattern recognition and classification of cognitive states. Our model is trained over a massive dataset of EEG recordings using the LSTM units to get rid of vanishing gradients frequently encountered in deep-learning models. The results from the experiments are given below and illustrate how the proposed method compares with traditional machine learning techniques in terms of accuracy and robustness. It is a promising tool for all applications in neurological diagnosis, cognitive research, and developing a brain-computer interface. © 2024 IEEE.

关键词： Recurrent neural networks

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From Posts to Personas: Classifying Users in the Social Media Landscape 5

From Posts to Personas: Classifying Users in the Social Medi...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Nair, Lekshmi S. Cheriyan, Jo Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering Saintgits College of Engineering Kerala Kottayam India

Social media is a digital environment where users openly share their opinions and engage in debates and discussions on various topics. Social media has amassed an enormous quantity of accessible data due to its constantly expanding and highly active user base. This data is a valuable resource for researching a variety of topics. An example of such a research problem is identifying user similarities by analyzing their data. This article explores combining comments, textual posts, and likes for comments to create keywords or tags and group the users. These tags are extracted from user data and utilized in constructing a tag network. The tag network facilitates the formation of communities consisting of users with similar interests. User grouping is achieved based on the tags extracted from the posts. The proposed methods employ TF-IDF (term frequency-inverse document frequency) and TextRank algorithms to extract the tags. Kernel diffusion determines the similarities between tags in the tag network. Finally, an aggregation-hierarchical clustering algorithm is employed to group social media users based on these tags. © 2024 The Authors. Published by Elsevier B.V.

关键词： Complex networks

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A new multi-stream approach using acoustic and visual features for robust speech recognition system

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Materials Today: Proceedings 2022年 62卷 4916-4924页

作者： Radha, N. Shahina, A. Nayeemulla Khan, A. Velusami, Jansi Rani Sella Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Chennai India School of Computing Science and Engineering Vellore Institute of Technology Chennai India Department of Computing Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India

Building a robust Automatic Speech Recognition (ASR) system and improving recognition accuracy in adverse conditions is still a challenging task. One way to improve the robustness of an ASR system is combining information from multiple sources (streams). A multi-stream approach which handles the multiple inputs at the model level is the key contribution of our work. Standard mic (Sm), Throat mic (Tm), and Lip reading (Lr) are the various source streams that have been used. This work explores a static weighted two stream HMM (TSH) and multi-stream HMM (MSH) model for the bimodal and multimodal systems. A syllabic units of the Hindi language database categorized into three categories – Vowel, Place of Articulation (POA), and Manner of Articulation (MOA) are used for training and testing. In this study, four types of TSH are proposed for the combination of bimodal ((Sm+Tm), (Tm+Lr), (Sm+Lr), (Lm+Lm)) systems and one type MSH is proposed for multimodal (Sm+Tm+Lr) system in a synchronous and asynchronous manner. Mel Frequency Cepstral Coefficient (MFCC) features are used for Sm and Tm signals. Combined pixel-motion based features (DCT/DWT-MHI) are used for Lr signals. Among these two features, DWT outperforms than DCT and used as a feature for visual speech. Experiments were conducted for bimodal and multimodal system. The proposed MSH approach shows improvements of 1.36%, 6.21%, and 5.8% in recognition accuracy for Vowel, POA and MOA category respectively, as compared to bimodal systems. © 2022

关键词： Discrete wavelet transforms

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