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检索条件"主题词=Software algorithms"

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Advancements in Drug Discovery Through Machine and Deep Learning Concepts in Early-Stage of Drug Discovery for Apoptosis

Advancements in Drug Discovery Through Machine and Deep Lear...

引用

Disruptive Technologies (ICDT), International Conference on

作者： Kushagra Saxena Ankita Srivastava Hemant Kumar Department of Computer Science and Engineering Indian Institute of Information Technology Ranchi Ranchi Jharkhand India Department of Computer Science Babasaheb Bhimrao Ambedkar University Lucknow India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Within the rapidly shifting field of drug discovery, ML and DL techniques have opened new directions for identifying potential therapeutic compounds with higher efficiency and accuracy. The present work explores the integration of ML and DL algorithms for drug discovery in apoptosis, by utilizing PaDEL and Lipinski descriptors for feature extraction. PaDEL is widely used open-source software that helps in generating the molecular descriptors and fingerprints of a chemical compound while Lipinski's descriptors depend on the “Rule of Five” which explores critical analysis of drug-likeness of molecules. The present article provides a comparative analysis of different ML and DL algorithms which include SVM, RF, GBM, KNN, ANN, and GNN. The performance of each one is evaluated based on key metrics such as adjustment of r2 score and mean square error. This presented work shall help in predicting the chemical compounds at the early stage of drug discovery using the QSAR method. Computed results are depicted graphically.

关键词： Drugs Deep learning Support vector machines Accuracy Computational modeling software algorithms Predictive models Drug discovery Compounds Chemical compounds

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Harnessing the Power of the LUMI Supercomputer for Advanced Trading Simulations

Harnessing the Power of the LUMI Supercomputer for Advanced ...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Ognjen Iković Nebojša Janović Milan Jovović Nikola Perović Dejan Radulović Nikola Žarić INVT Novi Sad Serbia PAID MNE Podgorica Montenegro Faculty of Electrical Engineering University of Montenegro Podgorica Montenegro

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

Supercomputers have significantly transformed capabilities in data processing and analysis across various sectors, including finance, where they facilitate complex trading simulations and predictive modeling. This paper examines the application of the LUMI supercomputer to enhance the performance of trading algorithms within the PAID-T framework. PAID-T, a sophisticated trading software, necessitates running extensive simulations to optimize trading strategies. Given the computational demands, executing approximately 1.2 million simulations was achieved through LUMI’s powerful infrastructure. The paper details the necessary adaptations for HPC execution, compares performance metrics between local PC and supercomputer, and discusses the challenges encountered during deployment. Our results highlight the remarkable efficiency gains realized by leveraging LUMI’s capabilities, reducing execution time significantly and achieving real-time processing of vast market data sets. The insights drawn underscore the potential of HPC systems in advancing algorithmic trading performance and strategy optimization.

关键词： Analytical models Adaptation models Computational modeling software algorithms Predictive models Prediction algorithms Supercomputers Data models software Real-time systems

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Quantum-Enhanced Machine Learning for Precision Breast Cancer Diagnosis

Quantum-Enhanced Machine Learning for Precision Breast Cance...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Agniva Banerjee Dept. Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Bhopal India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. This research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes.

关键词： Image segmentation Accuracy Machine learning algorithms software algorithms Artificial neural networks Machine learning Feature extraction Breast cancer software Tumors

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Modular Path-Following Control Architecture for Unmanned Ground Vehicles in ROS 2 Environment

Modular Path-Following Control Architecture for Unmanned Gro...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Ádám Szabó Dániel Károly Doba Árpád Fehér Szilárd Aradi Dept. of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

The number of highly automated vehicles and mobile robots has recently increased significantly. This trend is also present in the case of agricultural and military vehicles that operate in off-road conditions. These vehicles show a wide range of varieties in size, steering systems, and type of running gear. This research aims to present a modular software architecture with an easily adaptable control algorithm for the path-tracking control of unmanned ground vehicles. The developed system has been applied to an 8×8 test vehicle platform and tested on a predefined trajectory at different traveling speeds. The results show that the system can follow the desired trajectory accurately.

关键词： software architecture Navigation software algorithms Steering systems Computer architecture Robot sensing systems Land vehicles software Trajectory Vehicle dynamics

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Integrating Renewable Energy Systems: A Review of Economic Strategies and algorithms

Integrating Renewable Energy Systems: A Review of Economic S...

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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Zohreh Aghaie Joki Bijan Saina Foroughian Saeed Hasanzadeh Hamid Karimi Ali Mohammed Ridha Mustafa Habeeb Chyad Department of electrical and computer engineering Qom university of technology Qom Iran University of Al-Ameed Karbala Iraq University of Warith Al-Anbiyaa Karbala Iraq

ISBN: (数字)9798331525132

ISBN: (纸本)9798331525149

In recent years, growing environmental concerns and increasing energy demand have highlighted the importance of renewable energy sources as a sustainable and economically viable solution. One of the fundamental challenges in this field is the optimization of economic costs associated with the production, distribution, and consumption of renewable energy. This review paper examines various optimization methods for the efficient management of renewable energy-based systems. In this context, objective functions related to cost, efficiency, and energy sustainability are analyzed. Also, the methodology and techniques for solving optimization problems, including various algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and others, are explored. Additionally, recent studies on the integration of renewable energy sources such as solar, wind, hydrogen, and biomass, and their impact on reducing economic costs, are reviewed.

关键词： Renewable energy sources Wind Costs Reviews Wind energy software algorithms Simulated annealing Sustainable development software tools Genetic algorithms

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Toward Full GPU Acceleration of Agile Homomorphic Encryption Frameworks

Toward Full GPU Acceleration of Agile Homomorphic Encryption...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Emilio Quaggiotto Shiva Nejati Alexander J. Leigh Mitra Mirhassani Electrical and Computer Engineering University of Windsor Windsor Canada

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

CHIMERA and PEGASUS introduced new categories of Fully Homomorphic Encryption schemes, offering methods for converting between schemes and improving repacking algorithms for agile encryption frameworks. Using PEGASUS as a starting point, we aimed to speed up their novel repacking algorithm through the use of additional GPU computation. This work achieved a 50x speedup and 25% reduction in the overall repacking algorithm compared to CPU computation, focusing on unexplored parts and Homomorphic functions that are already GPU-amenable. In addition, We integrated our acceleration function into the Open-Source PEGASUS framework, enhancing its compatibility with complementary works on Homomorphic Encryption acceleration.

关键词： Privacy Machine learning algorithms software algorithms Graphics processing units Seals Machine learning Medical services software Homomorphic encryption Protection

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Development of a Resource to Automate the Process of Categorisation of Critical Information Infrastructure Objects

Development of a Resource to Automate the Process of Categor...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Alexandr M. Sadykov Anna A. Alekseeva Alsu I. Khabibrakhmanova Department Information Security Kazan National Research Technological University Faculty Kazan Russian Federation Department of Information Technologies and Intelligent Systems Kazan State Power Engineering University “KSPEU” Kazan Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The article deals with the automation of the process of categorisation of critical information infrastructure objects in the energy sector. A Python-based software resource has been developed to automate the creation of documentation and optimise data collection. The article analyses the existing approaches to categorisation of critical information infrastructure objects and proposes an algorithm that simplifies this procedure. The stages of programme development are described in detail, including creation of document templates, application of Python libraries for data processing and testing of the resource on the example of energy facilities. Special attention is given to compliance with legislative requirements and the practical significance of automation. The results confirm the effectiveness of the proposed approach, reducing time and labour costs and improving the accuracy of information processing. The paper concludes with a discussion of development prospects, such as the introduction of a graphical interface and expansion of functionality.

关键词： Automation Costs Accuracy software algorithms Information security Documentation software Testing Python Graphical user interfaces

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Methods of Using Artificial Intelligence algorithms in Human Resource Management

Methods of Using Artificial Intelligence Algorithms in Human...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Mekhriddin Nurmamatov Shokhrukh Sariyev Izhar Uddin Department of Artificial intelligence and Information systems Samarkand state university Samarkand Uzbekistan Department of Artificial intelligence and Information systems Samarkand state university New Delhi India

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The study examined the issue of applying artificial intelligence (AI) technologies to job search processes in employment systems. Based on the literature analysis, the main concepts of using AI in human resource management were presented. It was found that in recruitment, there is a relationship between AI replicating human cognitive processes and imitating human learning mechanisms. As a result, 3 main skills for mastering and using AI were presented. The general algorithm and description of the application of AI technologies to the labor market were presented, and the algorithms and methods of the Bat algorithm, the Gaussian distribution approach used in its implementation were studied. A method was proposed in which the optimization process was improved by adding an approach based on the Gaussian distribution to the Bat algorithm. This provides effective training data for vector machines and has been proven to give good results in predicting labor force employment.

关键词： Employment software algorithms Training data Optimization methods Gaussian distribution Prediction algorithms Vectors software Artificial intelligence Recruitment

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Behavior-Based Malware Detection in Enterprise Networks Using Artificial Intelligence

Behavior-Based Malware Detection in Enterprise Networks Usin...

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Automation and Computation (AUTOCOM), International Conference on

作者： Jatin Khurana Sambit Pattanaik Pradeep Marwaha K P Premalatha M Subramoniam Neural Das Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Centre for Internet of Things Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Malware attacks have always been regarded as a concern, but today, we should be far more worried about them gaining ground, especially on enterprise networks. Although signature-based techniques have witnessed much success in identifying malware, they can only partially encompass these changing threats. This calls for novel malware detection techniques to identify and neutralize such attacks accurately. In the study, we present a behavior-oriented AI-based malware detection system to identify and prevent malware attacks in enterprise networks. We use artificial intelligence algorithms that analyze the actions of network traffic, applications and devices to identify and classify malicious activity. Large datasets of previous malware behaviors are used to train the AI algorithms to determine new threats, even if the malware has never been seen before. In addition, it can also learn and update the algorithm completely by discovering the latest malware behavior. Our behavior-based detection allows our system to see zero-day attacks and malware variants that standard signature-based detection systems have not seen yet. This significantly improves the security of enterprise networks while minimizing data breaches and financial losses. Our behavior-based malware detection system provides a tailored protection capability against the emerging threat landscape to secure enterprise networks, which is a delegated preventive plan against emergent threats instead of reactive measures. It harnesses the power of AI and adapts to new data to offer a comprehensive layer of protection against malware.

关键词： Training Standards organizations software algorithms Telecommunication traffic Malware Real-time systems Classification algorithms Trojan horses Security Protection

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Super Twisting Control Strategy Implemented on a Bidirectional DISO DC-DC Converter

Super Twisting Control Strategy Implemented on a Bidirection...

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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Amirhossein Hosseini Saeed Hosseinnataj Majid Mehrasa Jafar Adabi Dept. of Electrical Engineering Noshirvani University of Technology Babol IRAN Univ. Lille Arts et Metiers Institute of Technology Centrale Lille Junia ULR France Dept. of Electrical Engineering University of New Orleans New Orleans USA

ISBN: (数字)9798331525132

ISBN: (纸本)9798331525149

In this paper, a fast dynamic and chattering-free control strategy called super-twisting controller (STC) is introduced. The STC controller generates a proper input signal to the bidirectional dual input single output (BDISO) DC-DC converter by means of pulse width modulation (PWM) technique. There are several ports in this BDISO converter, including, a DC power source port, an energy storage port, and an output port. The particular consideration about the design of the STC controller is to force the controlled states to seek their desired values with the fastest dynamic possible along with the lowest over/undershoot in various modes, including buck, boost, and bidirectional mode. Through simulation outputs, the effectiveness of the introduced control methodology is evaluated, and its performance is compared to that of a conventionally well-tuned proportional-integral (PI) and a fixed-frequency sliding mode (FSMC) controller by means of MATLAB/SIMULINK software.

关键词： Renewable energy sources software algorithms DC-DC power converters Pulse width modulation Stability analysis software Robustness MATLAB Sliding mode control Energy storage

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