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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Sunil Kumar Biswajit Bhowmik Department of Computer Science and Engineering Maharshi Patanjali CPS Lab BRICS Laboratory National Institute of Technology Karnataka Surathkal Mangalore Bharat

With rapid technological advancement, cyber-physical systems (CPS) become an emerging era of engineered systems based on computing, networking, and control technologies that revolutionize human lives. New and smart CPS drives innovation and competition in industrial automation, transportation, healthcare applications, etc. Therefore, it is essential to explore the area. The paper presents the emergence of CPS. It includes CPS’s evolution, its necessity, and its importance. Then, we explore the diverse applications of CPS in a smart society. Next, we explore various challenges and recent state-of-the-art to overcome these challenges. CPS connects strongly to the currently prevalent terms Internet of Things, the Industrial Internet, Industry 4.0, and Internet of Everything. All of these illustrate a technology that fundamentally connects the physical and digital worlds. CPS is more foundational and durable than all of these. Thus, the work presented here provides a view of substantial social consequences, making its trend progressive.

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ICEL: Learning with Inconsistent Explanations 48

ICEL: Learning with Inconsistent Explanations

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

作者： Liu, Biao Wu, Xiaoyu Yuan, Bo Southern University of Science and Technology Department of Computer Science & Engineering China Rams Lab Huawei China

ISBN: (纸本)9781728163277

Generating the heatmaps is one of the explanation methods to show what regions the model use to predict in vision tasks. GradCAM is a popular approach to provide such heatmaps. However, GradCAM is post-hoc, and its heatmaps are not always meeting human annotations. Inspired by CGC (Contrastive GradCAM consistency), we propose ICEL (InConsistent Explanation Learning) method which introduces inconsistent explanation loss measured by cosine similarity on heatmaps. We show that our method can preserve classification accuracy, while the heatmaps generated by explanation methods are more consistent with human annotations, and the computational complexity is reduced from O(n2) to O(n), compared with CGC. © 2023 IEEE.

关键词： computer vision

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Unleashing Future Donors Navigating Blood Contribution Propensity with Machine Learning Expertise 1

Unleashing Future Donors Navigating Blood Contribution Prope...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Dhami, Kavindra Singh Gusain, Raj Shekhar, Sushant Vidyarth, Anurag Prakash, Rishi Gowri, R. Propagation Research Lab Department of Civil Engineering Dehradun India Propagation Research Lab Department of Electronics & Communication Engineering Dehradun India Graphic Era Hill University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350377002

The study used machine learning techniques, which are an essential component of blood donation and health care. Data details of blood donation and service centers have been included in this study. Some of these variables include the number of times a person donates, the frequency of donations, and the time since the last donation. Modern technology machine learning methods analyze and consider key factors in the model. This is valuable information for physicians and blood transfusion centers because here we can predict when a donor may be able to donate blood again. Furthermore, our study aims to examine not only donor behavior but also whether blood donation will provide a continued flow of blood products to health systems in the future. The study of blood donation system is enhanced by this analysis method, which also provides an indication to take care of the health care of the society. We have taken our extensive analysis into account and used cross-validation and performance F1 scores. Donor confidentiality, equity and ethics are equally important to us in the development of our studies. Our approach can play an important role in the technological development sector. Our aim is to draw attention to how donor contact strategies will work, and to improve this, with a view to making the blood supply more reliable. © 2024 IEEE.

关键词： Blood Donation Donor Propensity Healthcare Optimization Machine Learning Stable Blood Supply

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NeuroBooster: A Robust Classifier for the Discovery of Neuropeptide Sequences based on Meta-learning Approach

NeuroBooster: A Robust Classifier for the Discovery of Neuro...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Hossain Shaon, Md. Shazzad Fahim Sultan, Md. Karim, Tasmin Hossain Alshan, Md. Shoaib Cuzzocrea, Alfredo Akter, Mst Shapna Oakland University Department of Computer Science and Engineering RochesterMI48309 United States Shanto-Mariam University of Creative Technology Department of Computer Science and Engineering Uttara Dhaka1230 Bangladesh University of Calabria IDEA Lab Rende Italy University of Paris City Department of Computer Science Paris France

ISBN: (纸本)9798350362480

Neuropeptides (NPs) are fragile proteins that serve as essential signaling molecules in the neurological system, playing a key role in modulating various physiological processes. Identifying particular neuropeptide sequences relevant to specific disorders would be beneficial for accelerating the development of diagnostic tools. The study proposed another approach to detecting NPs with multi-layer perception (MLP) and a bagging classifier-based meta-learning method called NeuroBooster. This investigation initially focused on five feature extractions based on composition, such as AAC, PAAC, physicochemical properties, QSO, and transfer-learning, such as Bert, and F2V strategies. Subsequently, we used the XGB feature selection method in the Bert and F2V methods to obtain the most 100D crucial features. The predicted probabilistic outcomes of NPs from the 8 preliminary models merged and derived a two-stage dataset with 40 dimensions of features and transmitted them into three classic models and two meta-models, through rigorous criteria for evaluation. Compared with the existing predictor, our proposed model NeuroBooster achieved a higher accuracy of 91.91% in the independent test method. Consequently, we discovered important features in these five models underscoring that physicochemical properties are potential targets for identification, thereby revealing new avenues for therapies. © 2024 IEEE.

关键词： Feature Selection

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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Dynamic Checkpointing: Fault Tolerance in High-Performance Computing

Dynamic Checkpointing: Fault Tolerance in High-Performance C...

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Communication, Control and Intelligent Systems (CCIS)

作者： Biswajit Bhowmik Tushar Verma Navdeep Dadhania Dineshbhai Manideep Raya VSRS Reddy Girish K K Maharshi Sushrut CAS Lab BRICS Laboratory Dept. of Computer Science and Engineering National Institute of Technology Karnataka Mangalore Bharat Maharshi Kanad QC Lab BRICS Laboratory Dept. of Computer Science and Engineering National Institute of Technology Karnataka Mangalore Bharat Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Dept. of Computer Science and Engineering National Institute of Technology Karnataka Mangalore Bharat

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

Parallel computing has become a cornerstone of modern computational systems, enabling the rapid processing of complex tasks by utilizing multiple processors simultaneously. However, the efficiency and reliability of these systems can be significantly compromised by inherent challenges such as hardware failures, communication delays, and uneven workload distribution. These issues not only slow down computations but also threaten the dependability of applications reliant on parallel processing. To address these challenges, researchers have developed strategies like dynamic checkpointing and load balancing, which are crucial for enhancing fault tolerance and optimizing performance. Dynamic checkpointing periodically saves the computational state, allowing for recovery from failures without significant data loss, while load balancing ensures that tasks are evenly distributed across processors, preventing bottlenecks and underutilization of resources. By integrating these mechanisms, this paper proposes a robust framework that improves the reliability and efficiency of parallel systems, particularly in high-performance computing environments where the ability to handle large-scale data processing with minimal downtime is critical.

关键词： Checkpointing Fault tolerance Program processors High performance computing Fault tolerant systems Parallel processing Load management Hardware Intelligent systems Resilience

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Auxiliary-Variable Adaptive Control Lyapunov Barrier Functions for Spatio-Temporally Constrained Safety-Critical Applications 63

Auxiliary-Variable Adaptive Control Lyapunov Barrier Functio...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Liu, Shuo Xiao, Wei Belta, Calin A. Boston University Department of Mechanical Engineering BrooklineMA United States Massachusetts Institute of Technology Computer Science and Artificial Intelligence Lab CambridgeMA United States University of Maryland College Park Department of Electrical and Computer Engineering and the Department of Computer Science MD United States

ISBN: (纸本)9798350316339

Recent work has shown that stabilizing an affine control system while optimizing a quadratic cost subject to state and control constraints can be mapped to a sequence of Quadratic Programs (QPs) using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). One of the main challenges in this method is that the QPs could easily become infeasible under safety and spatio-temporal constraints with tight control bounds. In our own recent work, we defined Auxiliary-Variable Adaptive CBFs (AVCBFs) to improve the feasibility of the CBF-based QP, while avoiding extensive parameter tuning. In this paper, we consider spatio-temporal constraints as finite-time reachability requirements. In order to satisfy these requirements, we generalize AVCBFs to AuxiliaryVariable Adaptive Control Lyapunov Barrier Functions (AVCLBFs) that work for systems and constraints with arbitrary relative degrees. We show that our method has fewer conflicts with safety and input constraints, and outperforms the state of the art in term of adaptivity and feasibility in solving the QP. We illustrate our approach on an optimal control problem for a unicycle. © 2024 IEEE.

关键词： Lyapunov functions

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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Spike Sorting and Event Detection in Neuromorphic Computing

Spike Sorting and Event Detection in Neuromorphic Computing

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Vakada G Sai Sree Vaishnavi Biswajit Bhowmik Department of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory National Institute of Technology Karnataka Surathkal Mangalore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Neuromorphic computing is a cutting-edge field of research that focuses on designing and developing computer systems and hardware architectures inspired by the structure and functioning of the human brain. The main objective of neuromorphic computing is to create computational systems that imitate the parallel processing, learning, and energy efficiency observed in biological neural networks. Two essential tasks in the analysis of brain data from extracellular recordings are spike sorting and event detection. Spike sorting involves identifying and categorizing discrete neural spikes, while event detection focuses on recognizing specific patterns or occurrences within the spiking activity of neuromorphic networks. Typically, each neuron generates spikes with a characteristic shape, and these clusters correspond to the activity of different neurons. Here, the outcome of spike sorting is the identification of which spike corresponds to which neuron. In-depth analysis of the spike sorting and event detection which are two critical processes in interpreting the intricate neuronal activity that underpins brain function are provided.

关键词： Event detection Neuromorphic engineering Shape Neurons Robot sensing systems Hardware Energy efficiency Recording Signal analysis Sorting

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On Evaluation of Bangla Word Analogies

On Evaluation of Bangla Word Analogies

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2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023

作者： Akter, Mousumi Sarkar, Souvika Karmaker, Shubhra Kanti Lab Department of Computer Science & Software Engineering Auburn University AL United States

ISBN: (纸本)9798891760608

This paper presents a benchmark dataset of Bangla word analogies for evaluating the quality of existing Bangla word embeddings. Despite being the 7th largest spoken language in the world, Bangla is still a low-resource language and popular NLP models often struggle to perform well on Bangla data sets. Therefore, developing a robust evaluation set is crucial for benchmarking and guiding future research on improving Bangla word embeddings, which is currently missing. To address this issue, we introduce a new evaluation set of 16,678 unique word analogies in Bangla as well as a translated and curated version of the original Mikolov dataset (10,594 samples) in Bangla. Our experiments with different state-of-the-art embedding models reveal that current Bangla word embeddings struggle to achieve high accuracy on both data sets, demonstrating a significant gap in multilingual NLP research. ©2023 Association for Computational Linguistics.

关键词： Embeddings

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