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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： M S P Durgarao Dinesh Valluru S. Saraswathi P. Malathi K. Thilagam S. Loganathan Department of Computer Science & Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Information Technology MLRITM Engineering CollegeHyderabad Dept of CSE - AIMLVel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamilnadu India Department of ECE Velammal Engineering College Chennai Tamil Nadu India Department of ECE Paavai Engineering College Namakkal Tamilnadu India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

A novel blockchain-based security architecture is presented in this study as a solution to the difficulties associated with securing 5 G -enabled WSNs. To manage the uncertain and limited resources inherent in WSNs, the system employs cryptographic techniques, intelligent contracts, and decentralized consensus protocols. The findings of this exhaustive analysis indicate that the proposed approach significantly enhances the overall security stance of 5 G -enabled WSNs. This solution to reduce security risks and vulnerabilities in WSNs is practical and contributes to the growing body of knowledge regarding secure communication in the 5 G era. Subsequent investigations and practical implementations that integrate blockchain technology with 5G-enabled WSNs are made possible by these results.

关键词： Knowledge engineering Wireless sensor networks Scalability data integrity Network security Security Resource management

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Integration of AI and Cloud-Native Technologies for Personalized Mobile Banking Experiences

Integration of AI and Cloud-Native Technologies for Personal...

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Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： N. Legapriyadharshini T. Thirumalaikumari K. Senbagam R. Sarasu H. Anwer Basha P. Malathi Department of Computer Application Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical And Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

In today's rapidly evolving digital landscape, using Artificial Intelligence (AI) and Cloud-Native Technologies is essential for improving personalized mobile banking experiences. The limitations and errors of existing financial systems necessitate such integration. Existing systems often struggle to adapt to modern consumers dynamic tastes and expectations, resulting in unsatisfactory customer experiences. Identifying the gap, the proposed system revolutionizes mobile banking applications by leveraging AI algorithms such as machine learning and natural language processing within a microservices-based cloud infrastructure. As a result, the proposed system solves the limitations of outdated architectures by providing personalized and efficient services while addressing security issues. The study indicates considerable gains in terms of user pleasure, system scalability, and security robustness through an in-depth evaluation of the results. The results highlight AI's and Cloud-Native Technologies transformational potential in revolutionizing the landscape of tailored mobile banking experiences. The study provides helpful information to financial institutions looking to adopt innovation and fulfill the evolving needs of modern consumers.

关键词： Technological innovation Scalability Microservice architectures Banking Predictive models User experience Robustness Natural language processing Security Artificial intelligence

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Parallelized bayesian optimization for problems with expensive evaluation functions 20

Parallelized bayesian optimization for problems with expensi...

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2020 Genetic and Evolutionary Computation Conference, GECCO 2020

作者： Rebolledo, Margarita Rehbach, Frederik Eiben, A.E. Bartz-Beielstein, Thomas Institute for Data Science Engineering and Analytics TH Köln Gummersbach Germany Department of Computer Science Vrije Universiteit Amsterdam Netherlands

ISBN: (纸本)9781450371278

Many black-box optimization problems rely on simulations to evaluate the quality of candidate solutions. These evaluations can be computationally expensive and very time-consuming. We present and approach to mitigate this problem by taking into consideration two factors: The number of evaluations and the execution time. We aim to keep the number of evaluations low by using Bayesian optimization (BO) - known to be sample efficient - and to reduce wall-clock times by executing parallel evaluations. Four parallelization methods using BO as optimizer are compared against the inherently parallel CMA-ES. Each method is evaluated on all the 24 objective functions of the Black-Box-Optimization-Benchmarking test suite in their 20-dimensional versions. The results show that parallelized BO outperforms the state-of-the-art CMA-ES on most of the test functions, also on higher dimensions. © 2020 Owner/Author.

关键词： Benchmarking

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Machine Learning Approaches for Proactive Phishing Attack Detection

Machine Learning Approaches for Proactive Phishing Attack De...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： B. Geetha P. Malathi T. Thirumalaikumari V. Janakiraman H. Anwer Basha S. Rukmani Devi Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Phishing attacks remain a serious problem in cybersecurity, necessitating advances in detection methods. The study describes a novel method for detecting phishing attacks that use advanced machine learning (ML) techniques, notably convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs and RNNs were chosen because of their ability to detect spatial and sequential patterns in phishing data. The system achieves improved performance by iterative model refinement and ensemble learning, beating existing rule-based systems in terms of precision (96%), recall (94%), and F1-score (95%). Furthermore, the proposed strategy has significantly lower false positive (3.8%) and false negative (5.7%) rates than existing systems, indicating greater dependability in distinguishing between legitimate and malicious emails. Cross-validation validates that the proposed system is stable and ready for real-world use. As a result, the proposed ML-driven strategy represents a substantial leap in phishing detection, providing increased accuracy (F1-score: 95%) and resilience to emerging cybersecurity threats.

关键词： Training Recurrent neural networks Accuracy Systematics Phishing Computational modeling Training data

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Automation Through Machine Learning: Reinvigorating Cyber Security

Automation Through Machine Learning: Reinvigorating Cyber Se...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Jayashree V. Bagade Sukhvinder Singh Dari Rekha Dhivrani Santosh Chowhan G. Ravivarman V Janakiraman Department of Information Technology Vishwakarma Institute of Technology Pune india Symbiosis Law School Nagpur Symbiosis International Pune India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Data Analytics and Mathematical Sciences Jain (Deemed to be University) Bangalore Karnataka India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cyber security is predicated heavily on manual efforts to fight malicious assaults, but such processes are increasingly turning into overwhelmed by means of the quantity of threats - a hassle worsened in the course of the global COVID-19 pandemic. This has highlighted the importance of leveraging device studying (ML) to automate cyber protection methods. In this article, we speak how ML may be used to optimize the detection, prevention, and remediations of malicious assaults in an extensive kind of applications and offerings. We discover the various procedures being evolved that may assist make the identity of threats greater accurate and greater effortlessly to be had. We additionally recollect the capacity challenges associated with the software of ML strategies, along with the potential for unfair bias in opposition to positive instructions of customers, and become aware of methods for addressing those issues. Our end emphasizes the significance of leveraging ML in order to enable automation in cyber safety, as well as to make sure the detection of state-of-the-art new threats.

关键词： Automation Pandemics Prevention and mitigation Manuals Machine learning Turning Software Security Protection computer crime

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

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log-RRIM: Yield Prediction via Local-to-global Reaction Representation Learning and Interaction Modeling

arXiv

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arXiv 2024年

作者： Hu, Xiao Chen, Ziqi Peng, Bo Adu-Ampratwum, Daniel Ning, Xia Computer Science and Engineering The Ohio State University ColumbusOH43210 United States Division of Medicinal Chemistry and Pharmacognosy College of Pharmacy The Ohio State University ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States Biomedical Informatics The Ohio State University ColumbusOH43210 United States

Accurate prediction of chemical reaction yields is crucial for optimizing organic synthesis, potentially reducing time and resources spent on experimentation. With the rise of artificial intelligence (AI), there is growing interest in leveraging AI-based methods to accelerate yield predictions without conducting in vitro experiments. We present log-RRIM, an innovative graph transformer-based framework designed for predicting chemical reaction yields. A key feature of log-RRIM is its integration of a cross-attention mechanism that focuses on the interplay between reagents and reaction centers. This design reflects a fundamental principle in chemical reactions: the crucial role of reagents in influencing bond-breaking and formation processes, which ultimately affect reaction yields. log-RRIM also implements a local-to-global reaction representation learning strategy. This approach initially captures detailed molecule-level information and then models and aggregates intermolecular interactions. Through this hierarchical process, log-RRIM effectively captures how different molecular fragments contribute to and influence the overall reaction yield. log-RRIM shows superior performance in our experiments, especially for medium to high-yielding reactions, proving its reliability as a predictor. The framework’s sophisticated modeling of reactant-reagent interactions and precise capture of molecular fragment contributions make it a valuable tool for reaction planning and optimization in chemical synthesis. The data and codes of log-RRIM are accessible through https://***/ninglab/Yield_log_RRIM. © 2024, CC BY.

关键词： Reagents

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Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization

arXiv

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arXiv 2023年

作者： Lawrence, Patrick J. Ning, Xia Biomedical Informatics Department The Ohio State University 1800 Cannon Drive Lincoln Tower 250 ColumbusOH43210 United States Computer Science and Engineering Department The Ohio State University 2015 Neil Avenue ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University 1760 Neil Avenue ColumbusOH43210 United States

Due to cancer’s complex nature and variable response to therapy, precision oncology informed by omics sequence analysis has become the current standard of care. However, the amount of data produced for each patients makes it difficult to quickly identify the best treatment regimen. Moreover, limited data availability has hindered computational methods’ abilities to learn patterns associated with effective drug-cell line pairs. In this work, we propose the use of contrastive learning to improve learned drug and cell line representations by preserving relationship structures associated with drug mechanism of action and cell line cancer types. In addition to achieving enhanced performance relative to a state-of-the-art method, we find that classifiers using our learned representations exhibit a more balances reliance on drug- and cell line-derived features when making predictions. This facilitates more personalized drug prioritizations that are informed by signals related to drug resistance. © 2023, CC BY.

关键词： Cell culture

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Beyond Reality: Crafting Logical Systems for Autonomous Vehicles through Virtual Reality

Beyond Reality: Crafting Logical Systems for Autonomous Vehi...

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Communication & Computing (WCONF), World Conference on

作者： N.K. Senthil Kumar Sumalatha A Dhivyabharathi R K. Suresh Kumar A. Hency Juliet S. Rajarajeswari Department of CSE School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Kristu Jayanti College Bengaluru Karnataka India Department of Computer Science and Engineering S.A. Engineering College Chennai Tamil Nadu India Department of MBA Panimalar Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

The current paradigm for advancing autonomous driving functionalities on public roads predominantly operates within the tangible realm. Autonomous vehicles (AVs) are equipped with an array of sensors, including Lidar, cameras, radar, GPS, and on-board-unit (OBU) modems, each actively collecting data about the surrounding environment. This data is then processed through sophisticated algorithms, enhancing the vehicle's situational awareness. In our pursuit of refining autonomous driving capabilities, we leverage the AVTSC Simulator as our chosen Autonomous Vehicle (AV) simulation environment. Notably, this simulator stands out for its ability to replicate realistic urban settings and seamlessly integrate with widely used platforms such as Apollo and ROS. By harnessing the capabilities of this simulation environment, we aim to bridge the gap between real-world testing and simulated scenarios. This abstract encapsulates the essence of our strategy, emphasizing the integration of advanced sensor technologies and simulation environments for the development of autonomous driving features. Preliminary results indicate a promising 94% success rate for the proposed methodology, showcasing the efficacy of our approach in enhancing the robustness and reliability of autonomous systems.

关键词： Laser radar Roads Virtual reality Modems Cameras Robustness Autonomous vehicles Testing Global Positioning System Sensor arrays

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Industrial Internet of Things (IIoT): Transforming Industrial Operations through Advanced Connectivity

Industrial Internet of Things (IIoT): Transforming Industria...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： S. Viveka Vivek Justus Sangeetha J Desidi Narsimha Reddy V. Samuthira Pandi S. Banumathi Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of CSE S. A. Engineering College Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The Industrial Internet of Things (IIoT) seamlessly integrates operational technology with modern connectivity technologies, ushering in a new age in industrial operations. This paper examines IIoT's revolutionary potential in changing industrial landscapes through smart sensors, actuators, and intelligent devices that enable real-time data sharing and process automation. This paper uses case studies from numerous sectors to demonstrate how IIoT may improve operational efficiency, downtime, and decision-making. Manufacturing, energy, and logistics are discussed. The report also examines IIoT technologies like cloud, edge, and machine learning techniques. These help enterprises exploit enormous data sets for predictive maintenance, supply chain optimization, and product quality. The article discusses IIoT's risks and challenges, including data privacy, cybersecurity, and the need for skilled personnel. A detailed research shows IIoT's strategic importance in the fourth industrial revolution. It also discusses how industrial networking will enable smart and sustainable operations.

关键词： Actuators Supply chains Real-time systems Product design Telecommunication computing Quality assessment Personnel Intelligent sensors Industrial Internet of Things Predictive maintenance

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