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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Priya, K. James, Sathish Kumar Babu, A. Aalan Sumalatha, A. Sharanyaa, S. Prasad, S.V.S. Ramco Institute of Technology Department of Cse North Venganallur Village Viruthunagar District Tamilnadu Rajapalayam India Bny Bny Mellon Center 500 Grant St Suite 3700 PittsburghPA15219 United States Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Tamil Nadu Chennai India Kristu Jayanti College Autonomous Department of Computer Science Bengaluru India Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad India

ISBN: (纸本)9798350376685

According to advancements in AI and ML, a new era of data-driven decision-making has begun, changing the face of many businesses around the globe. This research aims to delve into the intricate ways that ML and AI are changing the data consumption, analysis, and strategic decision-making landscape. This study aims to examine how AI and ML systems can handle massive volumes of complex data, spot patterns, and make unprecedented predictions. This is accomplished by analyzing a wide variety of case studies from a variety of industries, including retail, healthcare, transportation, and finance. In addition, the research examines the difficulties and ethical concerns that are brought about by the integration of these technologies. These concerns include concerns over the privacy of data, the potential for algorithmic prejudice, and the possibility of workforce displacement. This study offers insights into the future trajectory of artificial intelligence and machine learning technologies, as well as their impact on decision-making processes, by conducting a detailed review of the current status of machine learning and artificial intelligence applications. In conclusion, it provides advice for policymakers, industry leaders, and stakeholders on how to leverage the benefits of artificial intelligence and machine learning while limiting the risks associated with them. This will ensure that the revolution in data-driven decision-making is both responsible and sustainable. © 2024 IEEE.

关键词： Differential privacy

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Robust Feature engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems

arXiv

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arXiv 2024年

作者： Gardezi, Syed Saim Jawed, Soyiba Khan, Mahnoor Bukhari, Muneeba Khan, Rizwan Ahmed Department of Biomedical Engineering Faculty of Engineering Salim Habib University Karachi Pakistan Department of Computer Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan Department of Physics Faculty of Applied Sciences Riphah International University Islamabad Pakistan Department of Biomedical Engineering Faculty of Engineering NED University Karachi Pakistan Department of Computer Science School of Mathematics and Computer Science Institute of Business Administration Karachi Pakistan

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor and motor imagery tasks has been unveiled. Despite this, the productivity of the Machine Learning (ML) Models that were trained on this dataset was alarmingly deficient, and the evaluation framework seemed insufficient. To enhance outcomes, robust feature engineering (signal processing) methodologies are implemented. A collection of time domain, frequency domain, and wavelet-derived features was obtained from 16-channel EEG signals, and the Maximum Relevance Minimum Redundancy (MRMR) approach was employed to identify the four most significant features. For classification K Nearest Neighbors, Support Vector Machine, Decision Tree, and Naïve Bayes models were implemented with these selected features, evaluating their effectiveness through metrics such as testing accuracy, precision, recall, and F1 Score. By leveraging SVM with a Gaussian Kernel, a remarkable maximum testing accuracy of 92.50% for motor activities and 95.48% for imagery activities is achieved. These results are notably more dependable and gratifying compared to the previous study, where the peak accuracy was recorded at 74.36%. This research work provides an in-depth analysis of the MI Limb EEG dataset and it will help in designing and developing simple, cost-effective and reliable BCI systems for neuro-rehabilitation. © 2024, CC BY-NC-ND.

关键词： Support vector machines

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Hydrogen and biomethane pathways to achieve sustainable transportation in circular economic concept: A review

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International Journal of Hydrogen Energy 2025年 143卷 403-419页

作者： Saravanakumar, Ayyadurai Sudha, M.R. Chen, Wei-Hsin Pradeshwaran, Vijayakumar Centre for Research in Environment Sustainability Advocacy and Climate Change Directorate of Research and Virtual Education SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur603 203 India Department of Computer Applications College of Science and Humanities SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur603 203 India Department of Aeronautics and Astronautics National Cheng Kung University Tainan701 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung407 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Chemistry SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur603 203 India

Amidst the imperative need to reduce emissions and the rising demand for transportation, the logistics sector is at a crossroads. On a global scale, where road freight plays a significant role in the economy, the search for environmentally friendly fuel alternatives has led to the emergence of biomethane as a potential solution to assist fleets in decarbonizing their operations in the present day. The reliance of the transportation industry on fossil fuels is a cause for concern due to its environmental consequences and the depletion of resources. This study investigates the feasibility of hydrogen and biomethane as an eco-friendly transportation fuel in a circular economy. The analysis focuses on the production method, environmental advantages, and the role of biomethane in closing resource loops. The study examines constraints such as infrastructural development, feedstock availability, and economic viability. The significant findings on hydrogen suggest its strong potential as a sustainable fuel for long-haul heavy vehicles, showing better climate performance even when fossil electricity is used in production. Furthermore, in a circular economy, hydrogen-powered heavy-duty vehicles could become more advantageous than diesel, particularly as diesel faces reduced acceptance due to environmental issues. © 2025 Hydrogen Energy Publications LLC

关键词： Circular economy

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Identifying Underrepresented Driving Scenarios in Training Datasets with SafeML Robustness Monitoring for Autonomous Driving Vehicles

Identifying Underrepresented Driving Scenarios in Training D...

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International Conference on System Reliability and science (ICSRS)

作者： Bergler Matthias Tavakoli-Kolagari Ramin Lundqvist Kristina Department for Computer Science Nuremberg Institute of Technology Nuremberg Germany Dependable Software Engineering Mälardalens University Västerås Sweden

ISBN: (数字)9798350354508

ISBN: (纸本)9798350354515

This study investigates the evaluation of the representativeness of datasets in autonomous driving systems using statistical distance metrics. Eight different datasets, created in the Carla simulation environment, cover various driving conditions, including normal and impaired lighting scenarios, alternative routes, and challenging conditions such as power outages. The datasets were designed to represent similar routes under different conditions. Various distance metrics- Wasserstein, Kuiper, Anderson-Darling, Chernoff, DTS, and CVM-were applied to measure pairwise dataset distances. We anticipated that the dataset for a given route under ideal conditions would exhibit a large distance measure (of any of the listed distance measures) compared to the same route under impaired conditions (e.g., a power failure at the streetlights). However, we were particularly interested in whether a measurable jump at a (potential threshold) value could be recognized even with a smaller drop in dataset condition quality. The results of the study show that a normalization of these distance measures enables precise divergence comparisons and the determination of meaningful threshold values. This in turn means that normalized deviation measures can effectively identify deviations in real time, hence contributing to the development and monitoring of more reliable autonomous driving models.

关键词： Training Power measurement Lighting Time measurement Robustness Real-time systems Safety Power system reliability Autonomous vehicles Monitoring

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Brain Sight: A Novel system for substituting Vision Sensor 3

Brain Sight: A Novel system for substituting Vision Sensor

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3rd International Conference on Distributed Sensing and Intelligent Systems, ICDSIS 2022

作者： Elsaeed, Nadine Y. Alshaarawy, Mariam M. Esmaiel, Abd Elrhman S. El-Deen Elmahdy, Seif Abdelhameed, Omar Fakhry, Mariam M. Ogabih, Naglaa A. Ali, Nouran M. El-Deeb, Reem Software Engineering Department Faculty of Computers and Information Mansoura University Mansoura35516 Egypt Computer Science Department Faculty of Computers and Information Mansoura University Mansoura35516 Egypt The Department of Computer Science Faculty of Computers and Information Mansoura University P.O. Box: 35516 Mansoura Egypt

ISBN: (纸本)9781839538186

In a world where nearly everything we do depends on sight;it is ever more challenging for the unsighted to cope with it and lead a normal life without being reliant on the presence of a companion. Finding a mechanism to convey visual information to the brain is the most difficult part of the research as well as the development of efficient sensory replacement and augmentation systems. In this paper, we aim to propose Brain Sight as an innovative system to help blind people identifying and discovering things in front of them using vibro-tactile display for vision sensory substitution. Brain Sight is a wearable vest-like fabric containing small vibration motors that displays pulses to map the seen images on the Lower-back of the visually impaired. A deep labeling segmentation and edge detection techniques are applied to the captured image after preprocessing. A simple prototype is implemented and tested with volunteers. Thanks to neuroplasticity and the cognitive cortex inside humans' brain, the implemented vest showed promising results. Without the need for intensive training, Brain Sight enables blind to recognize different shapes and numbers. © IET Conference Proceedings 2022.

关键词： Semantics

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Towards Grand Unified Representation Learning for Unsupervised Visible-Infrared Person Re-Identification

Towards Grand Unified Representation Learning for Unsupervis...

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International Conference on computer Vision (ICCV)

作者： Bin Yang Jun Chen Mang Ye National Engineering Research Center for Multimedia Software School of Computer Science Hubei Luojia Laboratory Wuhan University Wuhan China

Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) is an extremely important and challenging task, which can alleviate the issue of expensive cross-modality annotations. Existing works focus on handling the cross-modality discrepancy under unsupervised conditions. However, they ignore the fact that USL-VI-ReID is a cross-modality retrieval task with the hierarchical discrepancy, i.e., camera variation and modality discrepancy, resulting in clustering inconsistencies and ambiguous cross-modality label association. To address these issues, we propose a hierarchical framework to learn grand unified representation (GUR) for USL-VI-ReID. The grand unified representation lies in two aspects: 1) GUR adopts a bottom-up domain learning strategy with a cross-memory association embedding module to explore the information of hierarchical domains, i.e., intra-camera, inter-camera, and inter-modality domains, learning a unified and robust representation against hierarchical discrepancy. 2) To unify the identities of the two modalities, we develop a cross-modality label unification module that constructs a cross-modality affinity matrix as a bridge for propagating labels between two modalities. Then, we utilize the homogeneous structure matrix to smooth the propagated labels, ensuring that the label structure within one modality remains unchanged. Extensive experiments demonstrate that our GUR framework significantly outperforms existing USL-VI-ReID methods, and even surpasses some supervised counterparts.

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IoT for Water Quality Categorization 5

IoT for Water Quality Categorization

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

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ExPDrug: Integration of an interpretable neural network and knowledge graph for pathway-based drug repurposing

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computers in Biology and Medicine 2025年 187卷 109729-109729页

作者： Kim, Junku Jang, Hojoong Park, Youngjun Jung, Inuk Jo, Kyuri Department of Computer Engineering Chungbuk National University Cheongju Korea Republic of Department of Medical Informatics University Medical Center Göttingen Göttingen Germany School of Computer Science and Engineering Kyungpook National University Daegu Korea Republic of

Precision medicine aims to provide personalized therapies by analyzing patient molecular profiles, often focusing on gene expression data. However, effectively linking these data to actionable drug discovery for clinical application remains challenging. In this paper, we introduce ExPDrug, a neural network (NN) model that integrates biological pathways from transcriptomic data with a biomedical knowledge graph to facilitate pathway-based drug repurposing. ExPDrug enhances disease phenotype prediction by capturing the complex relationships between genes and pathways. Using layer-wise relevance propagation (LRP), the model interprets the contribution of each pathway using relevance scores applied in a random walk-with-restart (RWR) algorithm to prioritize potential drug candidates in the biomedical network. ExPDrug outperforms existing methods in predicting phenotypes for the three diseases and identifying drug candidates, as supported by the literature. This model offers a transformative approach for advancing precision medicine by linking transcriptomic insights directly to clinical drug repurposing, thereby potentially improving treatment strategies for complex diseases. © 2025 Elsevier Ltd

关键词： Knowledge graph

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Learning Shape-Preserving Autoencoder for the Reconstruction of Functional Data from Noisy Observations 23rd

Learning Shape-Preserving Autoencoder for the Reconstructi...

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23rd International Conference on Computational science, ICCS 2023

作者： Krzyżak, Adam Rafajlowicz, Wojciech Rafajlowicz, Ewaryst Department of Computer Science and Software Engineering Concordia University 1455 De Maisonneuve Blvd. West MontrealQCH3G 1M8 Canada Szczecin Poland Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783031360206

We propose a new autoencoder preserving input functions’ general shape (monotonicity, convexity) after their reconstruction without imposing a priori constraints. These properties are inherent to the coefficients of the Bernstein-Durrmeyer polynomials that serve here as theoretical descriptors. Their estimates, computed from noisy observations by the coder, play the role of latent variables. The approach is purely nonparametric, i.e., no prior finite-dimensional model is assumed. The answer to the question of how many latent variables should be used for an acceptable reconstruction accuracy of a family of functional data is inferred from learning based on the proposed approximation of the Akaike Information Criterion. A distinguishing feature of this autoencoder is that the coder and encoder are designed as precomputed and stored matrices with the Bernstein polynomial entries. Thus, after selecting the number of latent variables, the autoencoder usage has a low computational complexity since it is linear with respect to observations. The proposed computational algorithms are tested on real data arising in mechanical engineering when control of damping vibrations is necessary. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Polynomials

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Linear time approximation algorithm for column subset selection with local search 24

Linear time approximation algorithm for column subset select...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yuanbin Zou Ziyun Huang Jinhui Xu Jianxin Wang Qilong Feng School of Computer Science and Engineering Central South University Changsha China and Xiangjiang Laboratory Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College Department of Computer Science and Engineering State University of New York at Buffalo NY School of Computer Science and Engineering Central South University Changsha China and Xiangjiang Laboratory Changsha China and The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha China

ISBN: (纸本)9798331314385

The Column Subset Selection (CSS) problem has been widely studied in dimensionality reduction and feature selection. The goal of the CSS problem is to output a submatrix S, consisting of k columns from an n × d input matrix A that minimizes the residual error ||A – SS† A||2F where S† is the Moore-Penrose inverse matrix of S. Many previous approximation algorithms have non-linear running times in both n and d, while the existing linear-time algorithms have a relatively larger approximation ratios. Additionally, the local search algorithms in existing results for solving the CSS problem are heuristic. To achieve linear running time while maintaining better approximation using a local search strategy, we propose a local search-based approximation algorithm for the CSS problem with exactly k columns selected. A key challenge in achieving linear running time with the local search strategy is how to avoid exhaustive enumerations of candidate columns for constructing swap pairs in each local search step. To address this issue, we propose a two-step mixed sampling method that reduces the number of enumerations for swap pair construction from O(dk) to k in linear time. Although the two-step mixed sampling method reduces the search space of local search strategy, bounding the residual error after swaps is a non-trivial task. To estimate the changes in residual error after swaps, we propose a matched swap pair construction method to bound the approximation loss, ensuring a constant probability of loss reduction in each local search step. In expectation, these techniques enable us to obtain the local search algorithm for the CSS problem with theoretical guarantees, where a 53(k + 1)-approximate solution can be obtained in linear running time O(ndk4 log k). Empirical experiments show that our proposed algorithm achieves better quality and time compared to previous algorithms on both small and large datasets. Moreover, it is at least 10 times faster than state-of-the-art algorith

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