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A novel deep learning approach for investigating liquid fuel injection in combustion system

Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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Application of Artificial Intelligence Technique to Detect Alzheimer's Disease

Application of Artificial Intelligence Technique to Detect A...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Arulprakash, A. Ponmaniraj, S. Gokul Rajan, V. Divya, A. Sathyamaba Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Simats University Department of Computational Intelligence Thandalam602 105 India School of Computing Science and Engineering Galgotias University Greater Noida India Vandayar Engineering College Department of Computer Science and Engineering Pulavar Naththam Thanjavur India

ISBN: (纸本)9798350394580

Aeras of investigational and clinical study have demonstrated several pathways in the progression of Alzheimer's disease (AD), but no treatments have been developed. Patient lifestyle, clinical, and biological data are increasingly being collected through open data-sharing programmes, and the resulting data set may be infinite in size, far exceeding the capacity of humans to make sense of it. Recent advances in artificial intelligence have opened the door to the possibility of using analysis of medical imagery to aid in the analysis of Alzheimer's disease. A variety of methods, including those based on artificial intelligence, have been devised and used in several research to aid in the CAD of AD identification. The use of AI methods to solve Alzheimer's disease diagnosis challenges in brain imaging will be a major focus of future research in this field. This article delivers an impression of the many images processing, feature extraction, and machine learning approaches that are used to solve the AD detection problem. The final section of the study will include a synopsis of the existing body of knowledge as well as a critical appraisal of potential future research avenues and open questions. © 2023 IEEE.

关键词： Alzheimer's disease detection Artificial intelligence diagnosis machine learning U-Net

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Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning 23

Mutually-paced Knowledge Distillation for Cross-lingual Temp...

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2023 World Wide Web Conference, WWW 2023

作者： Wang, Ruijie Li, Zheng Yang, Jingfeng Cao, Tianyu Zhang, Chao Yin, Bing Abdelzaher, Tarek Department of Computer Science University of Illinois Champaign Urbana United States *** Inc United States School of Computational Science and Engineering Georgia Institute of Technology United States

ISBN: (纸本)9781450394161

This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages by transfering knowledge from TKGs in high-resource ones. The cross-lingual distillation ability across TKGs becomes increasingly crucial, in light of the unsatisfying performance of existing reasoning methods on those severely incomplete TKGs, especially in low-resource languages. However, it poses tremendous challenges in two aspects. First, the cross-lingual alignments, which serve as bridges for knowledge transfer, are usually too scarce to transfer sufficient knowledge between two TKGs. Second, temporal knowledge discrepancy of the aligned entities, especially when alignments are unreliable, can mislead the knowledge distillation process. We correspondingly propose a mutually-paced knowledge distillation model MP-KD, where a teacher network trained on a source TKG can guide the training of a student network on target TKGs with an alignment module. Concretely, to deal with the scarcity issue, MP-KD generates pseudo alignments between TKGs based on the temporal information extracted by our representation module. To maximize the efficacy of knowledge transfer and control the noise caused by the temporal knowledge discrepancy, we enhance MP-KD with a temporal cross-lingual attention mechanism to dynamically estimate the alignment strength. The two procedures are mutually paced along with model training. Extensive experiments on twelve cross-lingual TKG transfer tasks in the EventKG benchmark demonstrate the effectiveness of the proposed MP-KD method. © 2023 ACM.

关键词： Distillation

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An Effective Hand Gesture Recognition using Convolutional Neural Network with Long Short-Term Memory

An Effective Hand Gesture Recognition using Convolutional Ne...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Sunil, G. Saranya, A. Duraimurugan, N. Dineshkumar, R. Ramachandra, A.C. Sr University School of Computer Science & Artificial Intelligence Warangal India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Department of Ece Thandalam India Engineering Nitte Meenakshi Institute of Technology Department of Electronics and Communication Bengaluru India

ISBN: (纸本)9798350313413

A Hand Gesture (HG) constitutes a mode of nonverbal communication or non-vocal interaction, where discernible bodily movements convey specific messages, either independently or in combination with spoken language. Gesture recognition techniques are employed to create systems capable of transmitting information among individuals with disabilities for operating devices. In this paper, we propose the use of Electromyography (EMG) signals for classifying hand gestures through Employing both a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in combination is being used. The main problem lies in the noise signals produced by the EMG sensor. In this work, we employ a Low Pass Filter for preprocessing the signal images, followed by feature extraction using the CNN model. To reduce the high-dimensional signal and normalize the features for the classification process, we utilize LSTM. This experiment was conducted using the Ninapro DB1 dataset. This model successfully addresses the previous issues and attains an overall accuracy of 95.56%, Precision 91.90%, Recall 89.50%, and F1 score 85.50% 91.90%, then the accurate classification compares to other existing models like Bidirectional Convolutional Gated Recurrent Unit, and Deformable convolutional network models. © 2023 IEEE.

关键词： Convolutional Neural Network Electromyography Hand Gesture Long Short-Term Memory Low Pass Filter

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Securing Neural Network in the Cloud: A Systematic Approach of Cloud Security Based on ANN-SVM Model 4

Securing Neural Network in the Cloud: A Systematic Approach ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Arunarani, Ar Vijayagopal, V. Kartheesan, L. Dwivedi, Vijay Kumar Rajesh Kumar, A. Nishant, Neerav School of Computing College of Engineering Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Chennai India Dr. M.G.R. Educational and Research Institute Faculty of Computer Applications Tamilnadu Chennai India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Vishwavidyalaya Engineering College Department of Mathematics Chhattisgarh Surguja India N.S.N. College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Karur India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798331540364

Many services have made the transition to the cloud because of the increased reliability and efficiency of cloud computing. Due to their capacity to ease service access while also securing communications in the public network, the three-factor Mutual Authentication and Key Agreement (MAKA) protocols are seeing increased use in multi-server designs. A lack of formal security proof or high communication and computation costs makes the limited three-factor MAKA protocols that are currently available susceptible to a wide range of attacks. The proposed approach consists of three phases, which are preprocessing, feature selection, and model training. Text scheduling and normalization make up preprocessing. In feature selection, the PSO is an optimization method that uses a population-based approach to find optimal solutions. An ANN-SVM was employed throughout the model's training phase. With an average accuracy of 93.30%, the proposed technique surpasses ANN and SVM. © 2024 IEEE.

关键词： Cloud security

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Bayesian Network-based Active Learning for Ontology-based Video Retrieval using Hidden Markov Logic Model with Edge Histogram Descriptor

Bayesian Network-based Active Learning for Ontology-based Vi...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Sathiyaprasad, B. Sreeji, S. Satheeshkumar, B. Menaka, D. Babu, K. Jones, G. Maria Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600 119 India Galgotias University School of Computing Science and Engineering UP Greater Noida India SRM Institute of Science and Technology Katankulathur Department of Computational Intelligence Chennai603203 India

ISBN: (纸本)9798350331790

A large amount of data is collected, analysed, and used on a regular basis by a large number of people for web pages, books, journals, and files which make up a significant portion of the data on the Internet. The burden on significant content-based retrieval and other applications is increasing as the volume of surveillance video data grows. Therefore to reduce the burden during retrieval and to solve the problem of video surveillance an ontology based video retrieval by feature extraction and classification techniques is proposed. In the Initial step the examined form of description files in OWL (Web Ontology Language is fed as input. Then the video pre-processed and segmented before extracting the features. The features of video has been extracted using "Hidden Markov Logic Model With Edge Histogram Descriptor (HMLM-EHD)"in which the spatio based features are extracted with Shape-based features that define object shapes in image is extracted from object contours or regions. After the extraction of features, classification is done using Bayesian network based active learning. The retrieval subsystem evaluates the provided query, performs similarity matching operations (which can be done using the correlation Distance Algorithm), and finally provides the result to the end user for these categorised video frames. The experimental results shows retrieved video frames and their parametric analysis in terms of accuracy of 94%, precision of 85%, recall of 71 % and F-1 score of 73%. © 2023 IEEE.

关键词： Feature extraction

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Cognitive Computing's Role in Shaping the Future Landscape of Healthcare

Cognitive Computing's Role in Shaping the Future Landscape o...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Jain, Anunay Jain, Neelesh Verma, Ashish Jain, Anupama Shri G. S. Institute of Technology Department of Information Technology Indore India Trinity Institute of Technology and Research Department of Computer Science Engineering Bhopal India Dr. Harisingh Gour Central University School of Mathematical & Physical Sciences Madhya Pradesh Sagar India Sagar Institute of Research & Technology Department of Computer Science Engineering Bhopal India

ISBN: (纸本)9798350348460

As the healthcare industry undergoes a paradigm shift towards data-driven and patient-centric models, cognitive computing emerges as a transformative force. This research paper delves into the profound impact of cognitive computing on the future landscape of healthcare. Through an extensive review of literature, case studies and emerging trends, this paper explores the applications, benefits, challenges, and ethical considerations associated with integrating cognitive computing into healthcare practices. Drawing on interdisciplinary perspectives, we examine how cognitive computing enhances diagnostic precision, optimizes treatment plans, facilitates personalized medicine, and improves overall healthcare delivery. The paper also scrutinizes the ethical implications of leveraging cognitive computing in healthcare, considering issues such as data privacy, bias, and the human-machine interface. By synthesizing existing knowledge and proposing future research directions, this paper contributes to the ongoing discourse on the role of cognitive computing in shaping the trajectory of healthcare. © 2024 IEEE.

关键词： Cognitive systems Digital healthcare Healthcare system

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Storage Solution and Security Transmission in Image Sensing Using Blockchain Technology in Internet of Things

Storage Solution and Security Transmission in Image Sensing ...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Gunturu, Vijaya Renu, Nekkanti Dhaliwal, Navdeep Sumalatha, Ippa Anandhi, R.J. Dutta, Gourab School Of Engineering Sr University Department Of Ece Telangana Warangal India Department Of Computer Science And Engineering Visakhapatnam Duvvada India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Information Science Engineering Bangalore India Brainware University Department Of Computational Sciences West Bengal Kolkata700125 India University Of Kalyani Department Of Computer Science & Engineering West Bengal Kalyani741245 India

ISBN: (纸本)9798350366846

The number of Internet of Things (IoT) devices has grown dramatically with the technology's rapid development. Higher security standards have so been proposed for the administration, transfer, and archiving of vast amounts of IoT data. But security problems like data theft and forgeries are likely to happen while IoT data is being transmitted. Furthermore, the majority of data storage options now in use are centralized, meaning that a centralized server handles both data maintenance and storage. The confidentiality of IoT data would be seriously jeopardized once a hostile assault targets the server. Given the aforementioned security concerns, a secure transmission as well as storage solution for blockchain sensing images in the Internet of Things is put forth. Therefore, to enable effective secure data storage in Internet of Things-related smart computing systems, develop and build a novel blockchain-based artificial intelligence model. We also demonstrated the operation of the system framework. Upon conducting a thorough security study, we have determined that our suggested solution possesses a strong potential to address the majority of security issues that conventional systems encounter. Furthermore, our suggested method can be used for any file-changing wireless Internet of things network that requires the exchange of multimedia data, including traffic data from smart cities, wearable device data, healthcare data, etc. © 2024 IEEE.

关键词： Internet of things

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Breast Cancer Detection Using Breastnet-18 Augmentation with Fine Tuned Vgg-16

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Intelligent Automation & Soft Computing 2023年第5期36卷 2363-2378页

作者： S.J.K.Jagadeesh Kumar P.Parthasarathi Mofreh A.Hogo Mehedi Masud Jehad F.Al-Amri Mohamed Abouhawwash Department of Computer Science and Engineering Kathir College of EngineeringCoimbatore641062India Department of Computer Science and Engineering Bannari Amman Institute of TechnologySathyamangalam638401TamilnaduIndia Electrical Engineering Department Faculty of EngineeringBenha UniversityBenha13518Egypt Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Women from middle age to old age are mostly screened positive for Breast cancer which leads to *** over the past decades,the overall sur-vival rate in breast cancer has improved due to advancements in early-stage diag-nosis and tailored *** all hospital brings high awareness and early detection technologies for breast *** increases the survival rate of *** traditional breast cancer treatment takes so long,early cancer techniques require an automation *** research provides a new methodol-ogy for classifying breast cancer using ultrasound pictures that use deep learning and the combination of the best ***,after successful learning of Convolutional Neural Network(CNN)algorithms,data augmentation is used to enhance the representation of the feature *** it uses BreastNet18 withﬁne-tuned VGG-16 model for pre-training the augmented *** feature classiﬁcation,Entropy controlled Whale Optimization Algorithm(EWOA)is *** features that have been optimized using the EWOA were utilized to fuse and optimize the *** identify the breast cancer pictures,training classiﬁers are *** using the novel probability-based serial technique,the best-chosen characteristics are fused and categorized by machine learning *** main objective behind the research is to increase tumor prediction accuracy for saving human *** testing was performed using a dataset of enhanced Breast Ultrasound Images(BUSI).The proposed method improves the accuracy com-pared with the existing methods.

关键词： Deep learning classiﬁcation data augmentation feature extraction the fusion of features breast cancer optimization classiﬁcation

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