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DMSS:An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction

Big Data Mining and Analytics 2024年第3期7卷 577-589页

作者： Yihui Ren Yu Wang Wenkai Han Yikang Huang Xiaoyang Hou Chunming Zhang Dongbo Bu Xin Gao Shiwei Sun Key Lab of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190Chinaand with University of Chinese Academy of SciencesBeijing 100049China Syneron Technology Guangzhou 510000China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Insitute of Computing Technology Chinese Academy of SciencesBeijing 100190China with Western Institute of Computing Technology Chongqing 400000China

Accurate prediction of peptide spectra is crucial for improving the efficiency and reliability of proteomic analysis,as well as for gaining insight into various biological *** this study,we introduce Deep MS Simulator(DMSS),a novel attention-based model tailored for forecasting theoretical spectra in mass *** has undergone rigorous validation through a series of experiments,consistently demonstrating superior performance compared to current methods in forecasting theoretical *** superior ability of DMSS to distinguish extremely similar peptides highlights the potential application of incorporating our predicted intensity information into mass spectrometry search engines to enhance the accuracy of protein *** findings contribute to the advancement of proteomics analysis and highlight the potential of the DMSS as a valuable tool in the field.

关键词： mass spectrometry proteomics machine learning deep learning

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The Necessary Requirements for Utilizing AI in Data Access Setting 15

The Necessary Requirements for Utilizing AI in Data Access S...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Swapnil, K. Sharma, Bhuvnesh Singh, Manpreet Yadav, Rakesh Kumar Devaraj Verma, C. Umarani, K. Vishwakarma Institute of Technology Department of Artificial Intelligence &data Science Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi University of Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

AI has been an increasing number of adopted in records get admission to settings to improve the overall performance of statistics get admission to and extract significant insights, using extended productiveness and value-effectiveness. However, the success implementation of AI calls for positive essential requirements to be met, together with facts accuracy, connectivity, security, and scalability. First, facts accuracy is critical for AI applications in facts get entry to settings. Information gathered need to be of excessive quality and accuracy, as AI working on erroneous facts can yield unreliable and probably harmful effects. Records should be of suitable granularity, taking pictures the most relevant elements, to make certain a complete and steady training performance.2nd, linked statistics assets, networks, and services are had to leverage the capability of AI in information get entry to settings. This gives an interconnected and up to date dataset at scale, while requiring included answers for uninterrupted get entry to. Moreover, single sign-on and alertness orchestration abilities are vital to decorate user revel in. © 2024 IEEE.

关键词： Connectivity Gathered Increasing Productiveness Security

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Software Re-engineering using Cloud Computing Platform 5

Software Re-Engineering using Cloud Computing Platform

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5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Raja, A. Ravi Raghunandan, K.R. Dodmane, Radhakrishna Dixit, Asmita Kumar, M. Sunil Kapila, Dhiraj Velagapudi Ramakrishna Siddhartha Engineering College Ece Department Vijayawada India Department of Computer Science and Engineering Nitte India Abes Engineering College Department of Informaton Technology Ghaziabad India Mohan Babu University School of Computing Department of Computer Science and Engineering Vijayawada India Lovely Professional University Department of Computer Science and Engineering Phagwara India

ISBN: (纸本)9781665474672

Economic and technological progress in the cloud are the main topics of this article. The essay examines the question of whether or not it makes financial sense to build software in the cloud, vs in-house. This research work provides an overview of the current model-based re-engineering practises. A novel and more thorough method is outlined in Business Process Model and Notation (BPMN) with some example activities, inputs, and outputs. As an integral element of the primary re-engineering process, reverse engineering is also something to be considered. Therefore, it expands on the concept of 'SBSE for the cloud,' providing actionable formulations of problems associated with cloud computing. The system has been built in the cloud to track the health of analytical services while they are being called into use. As an example of analytical services, a number of implementations of data mining methods are hosted from a number of packages. Their Quality of Service (QoS) values are also sampled by running them on certain real datasets. Based on the analysis of the KSEA National Math Competition event management system, this project will suggest some guidelines for software reengineering using AWS cloud computing services and web application technologies. Using these best practises, the informational gaps in software reengineering are bridged for web applications built in with cloud computing frameworks. © 2023 IEEE.

关键词： Cloud computing

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Design a multi-objective optimization with dynamic and global filter pruning strategy for convolutional neural network

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Multimedia Tools and Applications 2025年第3期84卷 1345-1358页

作者： Singh, Divya Rao, T. Prabhakara Veeranjaneyulu, N. Reddy, T Sunil Kumar Electronics and Communication Engineering GLA University Mathura India Andhra Pradesh Surampalem53343 India Department of Information Technology VFSTR Deemed to Be University Guntur Dist. Andhra Pradesh Vadlamudi India Department of Computer Science and Engineering Sri Venkatasa Perumal College of Engineering and Technology K.N Road Andhra Pradesh Cherlopalle517583 India

In recent years, the Deep Neural Networks (DNN) have managed tremendous growth and widespread applications. However, their applicability in embedded and mobile devices has been constrained by its complex arrangement, maximal computation, and the storage requirements. This paper designed an Evolutionary Multi-Objective Optimization (EMO) through a Dynamic and Global Filter Pruning (DG-FP) approach (EMO-DGFP) for an effective Convolutional Neural Network (CNN). Initially, Train the CNN in the Python system, then use the DG-FP approach to remove unnecessary filters. Furthermore, the CNN filters were adjusted using an EMO technique. The filter pruning issue has been resolved using an EMO based on non-dominated sorting. Then fine-tuning process was performed to regain its accuracy. The developed model efficiency has been tested against other widely used models, and the results showed 99.96% accuracy, 2% mistake rate, 2.6% compression ratio, and 80.67% pruning rate. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolution

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An Operative Expectation of Parkinson’s Ailment Using a Hybrid Machine Learning and Artificial Intelligence Systems

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SN computer science 2024年第8期5卷 1146页

作者： Athithan, Senthil Sachi, Savya Singh, Ajay Kumar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of information technology Lalit Narayan Mishra College of Business Management Bihar Muzaffarpur 842001 India Computer Science and Engineering Department Chandigarh University Uttar Pradesh Unnao India

Human biosignals can be used to detect early signs of Parkinson’s disease (PD). Electroencephalography (EEG) measures brain signals, while electromyography (EMG) measures muscle signals, both of which are useful in studying functional and neuronal changes in Parkinson’s patients. Researchers extracted various time and frequency domain features from EEG and EMG signals of early-stage PD patients and control subjects (CS) to identify motor symptoms. A diagnostic tool based on a mathematical model utilizing an artificial neural network (ANN) and a graphical user interface was developed. The ANN-based classifier, using the EEG and EMG features as inputs, accurately identified early-stage PD patients from control subjects with a classification rate of approximately 100%. The combination of EEG and EMG characteristics resulted in the highest recognition rate of 99.1%. This model demonstrated near-perfect accuracy, with an R-value of 0.9976, indicating its precision. Additionally, it achieved a low validation mean square error of 0.0068 and an elapsed time of 1.32 s. Studies show that combining EEG and EMG features as input parameters effectively distinguishes PD patients from control subjects. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： EEG Frontal Brain Neurological Disorder Parkinson’s Disease Temporal Brain

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Commdre: Document-Level Relation Extraction with Self-Supervised Commonsense Learning 48

Commdre: Document-Level Relation Extraction with Self-Superv...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Rongzhen Zhong, Jiang Xue, Zhongxuan Dai, Qizhu Wang, Chen Li, Xue Chongqing University College of Computer Science China The University of Queensland School of Information Technology and Electrical Engineering Austria

ISBN: (纸本)9781728163277

Document-level relation extraction (DocRE) is a more challenging task for which multi-label and multi-entity problems need to be resolved effectively than its sentence-level counterpart. It aims at extracting relationships between two entities at once while taking into account significant cross-sentence features and long-distance semantic representation. In this paper, we propose a self-supervised commonsense-enhanced DocRE model, called CommDRE, without external knowledge. First, we introduce self-supervised learning to represent the commonsense knowledge of each entity in an entity pair. Second, we convert the cross-sentence entity pairs into anonymous entity pairs with a coreference commonsense alternative. Finally, we perform semantic relation representation learning on the anonymous entity pairs and automatically convert them into target entity pairs. Experimental results show that it performs significantly better than strong baselines by 2.76% F1, and commonsense knowledge has an important contribution to the DocRE through the ablation study. © 2023 IEEE.

关键词： Extraction

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Marine Ecosystem Monitoring: Applying Remote Sensing and AI to Track and Predict Coral Reef Health

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 486-499页

作者： Veeranjaneyulu, Rayavarapu Govindarajan, Dinesh Subramanian, Chandramohan Devi, Deva Uma Banerjee, Sudipta Edpuganti, Sai Krishna Upadhyay, Shrikant Department of Computer Science & amp Engineering PACE Institute of Technology and Sciences Andhra Pradesh Ongole India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of ECE SCSVMV Deemed University Tamil Nadu Kanchipuram India Department of Computer Science and Engineering Gayatri Vidya Parishad College of Engineering (A) Andhra Pradhesh Visakhapatnam India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune Campus Pune India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur Dist. India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

Coral reefs are essential ecosystems, supporting a diverse range of marine life and offering considerable ecological, economic, and cultural benefits. However, they face increasing threats from climate change, pollution, and other human activities. Thus, effective monitoring and management of coral reefs are crucial for their conservation and sustainability. In this study, we employed a hybrid model, HCNN-SVM, which combines convolutional neural networks (CNNs) for feature extraction and support vector machines (SVMs) for classification. We utilized a coral-reef dataset from Kaggle, containing images from CoralNet and the Moorea Coral Reef Long Term Ecological Research (MCR LTER). To enhance image quality and ensure accurate reflectance values, we applied radiometric, geometric, and water column corrections during preprocessing. Feature extraction involved the use of spectral indices such as the Coral Bleaching Index (CBI) and Normalized Difference Vegetation Index (NDVI) to detect healthy and stressed corals, along with texture analysis to differentiate substrates. The model was trained on this diverse dataset, which captures various environmental conditions and reef types, achieving an accuracy of 98.55%. Cross-validation methods were employed to evaluate model performance, ensuring robustness and generalizability. The HCNN-SVM model demonstrated high accuracy in identifying and mapping coral reef components, making it a powerful tool for monitoring coral reef health and supporting conservation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks (CNNs) Coral Bleaching Index (CBI) Coral reef monitoring Feature extraction HCNN-SVM Moorea Coral Reef Long Term Ecological Research (MCR LTER) Normalized Difference Vegetation Index (NDVI) Support vector machines (SVMs)

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Cyber Security Tool for Combat Remote Work Vulnerability 5

Cyber Security Tool for Combat Remote Work Vulnerability

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Menaka, S. Navaneethakrishna, K. Blesswin Geo Sam, J.A. Hariharasuthan, V.K. Murugesan, S. Bharathiraja, N. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India Tagore Engineering College Department of IT Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665493604

Cyber security aims to prevent the illegal use of such things. By leveraging existing software weaknesses and bugs, hackers break into networks using software systems and human tactics. Offensive tactics, vulnerability ideas, and defensive methods must be carefully studied to ensure adequate protection.0 Good results could be achieved by creating custom updates for each organization and repairing technological flaws by being informed of the safety software of each system. The study also found that some other intrusion protection measures, such as Windows Defender, virus protection, and routers, were insufficient. Therefore, applying the Nmap and Metasploit technologies, this research examines several penetration assessment techniques to identify sensitive endpoints and applications. A virtualized system with various Windows and Linux OS versions was used as a testing phase © 2023 IEEE.

关键词： Cybersecurity

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DUFuse: Deep U-Net for visual and infrared images fusion

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12549-12561页

作者： Pan, Yue Pi, Dechang Khan, Izhar Ahmed Meng, Han School of Information Science and Engineering University of Jinan Jinan250022 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210000 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100000 China

The vision sensor is capable of capturing image detail features suitable for human observation, while the infrared sensor is capable of capturing the thermal characteristics of the target object. Therefore, the vision and infrared image fusion aim to retain both the rich detail features in the visual image and the thermal characteristics of the infrared image. This study proposes a novel deep U-Net network model to solve the fusion task. First, an improved deep model is proposed for better feature extraction by borrowing the process of decomposition, fusion and reconstruction in the multiscale decomposition process. Second, structural similarity is introduced into the loss function, which enables the network to enhance the quality of the detailed features of the generated images. Third, we propose a new hierarchical fusion strategy as well as average fusion and weighted fusion rules. Extensive experiments demonstrate that the proposed algorithm is superior to state-of-the-art algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Image fusion

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Educational Intelligent Chat Bot for Von Willebrand Disease Among Senegalese People 4

Educational Intelligent Chat Bot for Von Willebrand Disease ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Akuthota, Keerthi Sai Kishor Kumar Reddy, C. Kyatham, Aashritha Reddy Ganjam, Amrutha Sriya Fatima, Shugufta Akuthota, Sai Vamshi Stanley College of Engineering and Technology for Women Department of Computer Science and Engineering Hyderabad India Design Tech Homes Oklahoma State University WoodlandsTX United States

ISBN: (纸本)9798331540364

Von Willebrand disease (VWD) is a blood disorder caused by a protein deficiency that prevents blood from clotting properly. The challenge faced by people with Von Willebrand disease is having access to limited information, users frequently believe they do not have access to all the information they require to manage their condition. The lack of technological literacy could make it difficult for them to interact efficiently and the language barrier is the biggest concern. The model of an educational intelligent chatbot for patients with Von Willebrand seeks to close information gaps and literacy, especially among Senegalese people. This chatbot uses voice recognition and machine learning to deliver accurate, culturally appropriate information in an approachable manner. This cutting-edge solution, which provides trustworthy instructional resources in a safe digital setting, is expected to greatly improve hemophilia sufferers' quality of life and ability to manage their condition with an emphasis on Senegalese people in particular, the creation of an Educational Intelligent Chatbot for Von Willebrand Patients tackles the urgent need for improved knowledge and education regarding Von- Willebrand. This sophisticated chatbot uses the most recent advancements in machine learning and speech recognition to provide accurate, culturally appropriate information in an interesting and approachable manner. © 2024 IEEE.

关键词： Bot (Internet)

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