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Optimization computing and Wireless Communication (ICOCWC), International Conference on

作者： Aaditya Jain Sanjeev Kumar Mandal Monika Abrol Department of Computing Sciences and Information Technology Teetthanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Applied Science Sanskriti University Mathura Uttar Pradesh India

Recurrent Neural Networks (RNNs) are a modern-day state-of-the-art algorithm that is brand new modern getting used for clinical picture segmentation. RNNs are, in particular, nicely applicable for this undertaking due to the fact they can be skilled to bear in mind patterns over long sequences brand new information. This enables them to perceive structural patterns in an image and carry out sophisticated segmentation obligations together with tumor or organ boundary identification. similarly, RNNs have the ability to contain earlier know-how from different pics and medical data, as well as contextual know-how from external resources such as electronic fitness information. This paper critiques the contemporary in RNNs for medical picture segmentation, outlining the key methods and programs contemporary RNNs inside the field. We discuss both the successes and demanding situations of trendy RNN-based procedures and provide destiny studies directions for the improvement of modern-day extra correct and efficient segmentation equipment.

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Enhancing Medical Image Segmentation Through Knowledge Transfer

Enhancing Medical Image Segmentation Through Knowledge Trans...

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Optimization computing and Wireless Communication (ICOCWC), International Conference on

作者： R Raghavendra Monika Abrol Ramesh Chandra Tripathi Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Applied Science Sanskriti University Mathura Uttar Pradesh India Department of Computing Sciences and Information Technology Teetthanker Mahaveer University Moradabad Uttar Pradesh India

Clinical image segmentation is a challenge that identifies a selected organ or anatomical structure in a given scientific image which can be of radiographic or other modality. Expertise switch is an essential side of clinical image segmentation as medical practitioners may additionally possess information about the patient's anatomy or physiology that could contribute to the segmentation process. Understanding transfer to a deep mastering version, such as a convolution neural community, can allow the model to perform correct segmentation compared to other tactics. In this paper, we explore strategies and packages of expertise switches for scientific photograph segmentation, discuss its blessings and challenges, and eventually advise a singular method based totally on understanding fusion. We increase a two-step framework to fuse photo-degree features and segmentation labels with medical data before segmentation. The proposed approach demonstrates advanced segmentation accuracy in assessing the present strategies…

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Effective Surrogate Models for Docking Scores Prediction of Candidate Drug Molecules on SARS-CoV-2 Protein Targets

Effective Surrogate Models for Docking Scores Prediction of ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Shi, Wenqi Murakoso, Mio Guo, Xiaoyan Xiong, Linxi Chen, Matthew Wang, May D. Georgia Institute of Technology Department of Electrical and Computer Engineering Atlanta United States Kyoto University Department of Applied Life Science Kyoto Japan Georgia Institute of Technology College of Computing Atlanta United States Tsinghua University Tanwei College Beijing China Georgia Institute of Technology Wallace H. Coulter Department of Biomedical Engineering Atlanta United States

ISBN: (纸本)9798350337488

Emerging infectious diseases, such as coronavirus disease 2019 (COVID-19), pose a major threat to public health and present a critical challenge for drug discovery. Due to the cost- and time-consuming process of new drug development, virtual pre-screening methods such as protein-ligand docking prediction have become essential tools in enhancing drug refurbishment and repurposing. In this study, we propose a machine learning-based surrogate model for docking score prediction of drug candidates on SARS-CoV-2 protein targets via deep feature concatenation. We investigate 14 different combinations of rule-based and data-driven fingerprinting methods to identify the optimal representation of candidate drug molecules. Extensive experiments on docking scores of 270,000 molecules across 18 different SARS-CoV-2 protein targets demonstrate the effectiveness of the proposed surrogate models. In addition to unseen drugs, we further investigate the generalization of the proposed framework for unseen protein targets. This study may provide an instrumental and generalizable framework for exploring ligand-protein interaction, serving as a useful tool to facilitate rapid drug pre-screening during emerging public health crises. © 2023 IEEE.

关键词： COVID-19 drug target explainable artificial intelligence feature concatenation interaction prediction neural networks

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Informed Machine Learning for Optimizing Melt Spinning Processes

Informed Machine Learning for Optimizing Melt Spinning Proce...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Viny Saajan Victor Manuel Ettmüller Andre Schmeißer Heike Leitte Simone Gramsch Transport Processes Department Fraunhofer ITWM Kaiserslautern Germany Department of Visualization and Scientific Computing Rhineland-Palatinate Technical University Kaiserslautern Germany Department of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt Germany

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Industrial textiles have increasingly become a major part of our day-to-day lives owing to their various desirable properties. Melt spinning processes are a primary and integral part of the production of these textiles. Optimizing the spinning process while maintaining desirable quality is one of the key challenges for the textile industry. Although numerical models, which are digital twins of physical processes, are often used in optimization, they tend to be computationally expensive for complex scenarios. Hence, in this paper, we utilize machine learning to facilitate the optimization of melt spinning processes. We present a novel, reliable, and informed machine-learning model that is both data-and physics-driven. We further demonstrate the capability of this model to accelerate the optimization and analysis of melt spinning processes.

关键词： Computational modeling Machine learning Differential equations Numerical models Spinning Reliability Isothermal processes

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Deriving a Power-Law Model for Aggregating Data with Head Based Clustering

Deriving a Power-Law Model for Aggregating Data with Head Ba...

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Optimization computing and Wireless Communication (ICOCWC), International Conference on

作者： Vipin Solanki Ajay Rastogi R Raghavendra Department of Applied Science Sanskriti University Mathura Uttar Pradesh India Department of Computing Sciences and Information Technology Teetthanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

Head-based Totally Clustering is a technique of grouping facts and factors with similar traits using a rooted tree structure. The research paper Deriving an electricity-law version for Aggregating facts with Head primarily based Clustering outlines an energy-law model for creating clusters of facts points in a head-based totally hierarchy that may be used to efficiently mixture groups of related information. The authors used a topological distance technique to generate the clusters and implemented the method on an objective global dataset of web pages. The proposed model is designed to lessen the complexity of incorporating records from a pre-described tree shape with electricity regulation. The effects confirmed a widespread discount in computational time and aid utilization. Additionally, the approach could increase aggregated facts' accuracy by incorporating numerous information points in a head-based hierarchy. The look gives a similar understanding of the potential application of a strength-regulation model for aggregating statistics with head-primarily based Clustering.

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Automating Weed Detection Through Hyper Spectral Image Analysis

Automating Weed Detection Through Hyper Spectral Image Analy...

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Optimization computing and Wireless Communication (ICOCWC), International Conference on

作者： Shambhu Bharadwaj Prabhu A Vipin Solanki Department of Computing Sciences and Information Technology Teetthanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Applied Science Sanskriti University Mathura Uttar Pradesh India

Weed detection is an essential assignment in agricultural settings. Negative environmental results, crop yield loss, and mechanical weeding hard work prices related to weed management have necessitated the improvement of automation solutions to discover and treat weeds. Hyperspectral imaging (HSI) is a promising method that can produce abundant statistics about the spectral residences of flowers. This generation has been used in weed detection packages to classify weeds from crops, and these days deep mastering has been used to provide high accuracy prices in this discipline. In this abstract, we explore the utility of HSI for weed detection. We define current demanding situations that require further research earlier than automatic weed detection structures using HSI grow to be widely available. In particular, the presently available algorithms lack robustness and scalability, and further improvements in gadget-gaining knowledge of algorithms and techniques are wished to conquer those constraints, in addition to advancing the computational capabilities of these structures. Moreover, we discuss the potential of HSI as a weed detection answer in various contexts, including agroforestry and precision farming. In conclusion, we advise that the software of HSI for automatic weed detection has the massive capability to reduce labor fees related to weed manipulation, improve farming performance, and in the end, boom crop yields. Weed detection is a first-rate challenge inside the agricultural enterprise, as guide weed control is costly, time-consuming, and the correct identity of weeds is rigid. Hyper Spectral photo evaluation (HSIA) offers an alternative to guide weed detection, considering the rapid and effective mapping of weed-infested land without the need for guide labor. HSIA may be used to routinely detect the spectral signature of weed species, allowing for correct identity and brief remedy. This method uses hyperspectral scanners to gather spectral records, whic

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Development of Automated Techniques for Object-Oriented Image Analysis in Hyper Spectral Images

Development of Automated Techniques for Object-Oriented Imag...

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Optimization computing and Wireless Communication (ICOCWC), International Conference on

作者： Monika Abrol Rajendra P. Pandey Rahul Pawar Department of Applied Science Sanskriti University Mathura Uttar Pradesh India Department of Computing Sciences and Information Technology Teetthanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

the development of computerized strategies for item-orientated image evaluation in Hyper Spectral photos (HSI), an emerging field of applying machine-gaining knowledge of synthetic intelligence, has ended up an increasing number of crucial in a selection of domain names. This kind of analysis calls for a particular and correct illustration of the gadgets of interest from the hyperspectral photos. For this reason, characteristic extraction, classifiers, and clustering techniques have been proposed if you want to come across and classify them greenly. The maximum, not unusual feature extraction techniques used to extract statistics from HSI consist of radiometry, spectral band shapes, and spectral correlation. These function extraction strategies produce specific characteristic descriptors that can be utilized in aggregate with item classifiers and clustering solutions to detect and classify the objects gift in the HSI. Characteristic extraction strategies, together with Radiometric Normalized distinction flora Index (NDVI) and significant components analysis (PCA), have been observed to achieve success in numerous scenarios. Classifiers, linear and nonlinear SVM, neural networks, and choice bushes are the most famous strategies for reading HSI. Using a single this kind of strategy has been seen to offer the most straightforward restricted outcomes; however, using a combination of those strategies has been visible to enhance the classification performance.

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Exploring the Role of Internet of Things in Wireless Sensor Networks for Industry 4.0 Applications

Exploring the Role of Internet of Things in Wireless Sensor ...

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Pioneering Developments in computer science & Digital Technologies (IC2SDT), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N.R T. Sujatha S. Renuga Chithra R.S College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Application Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Ponjesly College of Engineering Kanyakumari India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

Recent developments in computer networking have made it possible to access information from a faraway location via either wireless or wired networks. The recent advancements in wireless infrastructure are directly responsible for the advent of wireless sensor networks, often known as WSNs. These networks make it easier to keep track of environmental happenings and activities, as well as document them and manage them. In WSNs, data relaying is accomplished through the use of a variety of routing strategies. Industry 4.0, also known as the fourth industrial revolution, is characterized by the incorporation of highly developed physical automation systems. These systems are made up of several pieces of machinery and gadgets that are linked together by sensors and are operated by software. The goal of the fourth industrial revolution is to improve the effectiveness and dependability of business processes. The Internet of Things (IoT) can be leveraged in manufacturing, which gives a means of linking engines, power grids, and sensors to the cloud in an industrial context. This is accomplished through the use of Industrial IoT. The purpose of this paper is to gain an understanding of how the Internet of Things functions in wireless sensor networks and its possible applications in a variety of settings.

关键词： Wireless sensor networks Protocols Wireless networks Sensor phenomena and characterization Routing Software Sensor systems Power grids Fourth Industrial Revolution Machinery

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Non-Classical Quantum State Generation using Superconducting-Nanostrip Photon-Number Resolving Detector with Optical Sampling

Non-Classical Quantum State Generation using Superconducting...

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2024 Frontiers in Optics, FiO 2024

作者： Endo, Mamoru Takahashi, Kazuma Nomura, Takefumi Sonoyama, Tatsuki Miki, Shigehito Yabuno, Masahiro Terai, Hirotaka Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst AmherstMA01003 United States

Superconducting-nanostrip photon detectors with optical sampling method now function as true photon-number resolving detectors in real-time without multiplexing. We applied this technique for quantum state generation ... 详细信息

关键词： Quantum computers

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Classification of Retroviruses Based on Genomic Data Using RVGC

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computers, Materials & Continua 2021年第12期69卷 3829-3844页

作者： Khalid Mahmood Aamir Muhammad Bilal Muhammad Ramzan Muhammad Attique Khan Yunyoung Nam Seifedine Kadry Department of CS&IT University of SargodhaSargodha40100Pakistan Department of CS&IT University of MianwaliMianwali42200Pakistan School of Systems and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

Retroviruses are a large group of infectious agents with similar virion structures and replication ***,cancer,neurologic disorders,and other clinical conditions can all be fatal due to retrovirus *** of retroviruses by genome sequence is a biological problem that benefits from computational *** National Center for Biotechnology Information(NCBI)promotes science and health by making biomedical and genomic data available to the *** research aims to classify the different types of rotavirus genome sequences available at the ***,nucleotide pattern occurrences are counted in the given genome sequences at the preprocessing *** on some significant results,the number of features used for classification is reduced to *** classification shall be carried out in two *** first phase of classification shall select only two *** data in the first phase is transferred to the next phase,where the final decision is taken with the remaining three *** data sets of animals and human retroviruses are selected;the training data set is used to minimize the classifier’s number and training;the validation data set is used to validate the *** performance of the classifier is analyzed using the test data ***,we use decision tree,naive Bayes,knearest neighbors,and vector support machines to compare *** results show that the proposed approach performs better than the existing methods for the retrovirus’s imbalanced genome-sequence dataset.

关键词： Retroviruses machine learning bioinformatics classification

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