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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Kafila Swetha, S. Mittal, Shikha Vijaya Lakshmi, V. Lourens, Melanie Soni, Mukesh Sr University School of Business Telangana Warangal India Sri Shakthi Institute of Engineering and Technology Department of Computer Science Engineering India Raj Kumar Goel Institute of Technology Ghaziabad India G. Narayanamma Institute of Technology and Science Humanities and Mathematics Department Telangana Hyderabad India Durban University of Technology Deputy Dean Faculty of Management Sciences South Africa Dr. D. Y. Patil Vidyapeeth Pune India Dr. D. Y. Patil School of Science and Technology Tathawade Pune India

ISBN: (纸本)9798350384277

Interest in AI applications has significantly increased as a result of the realization that industrial machine learning (ML) applications are essential in today's fast-paced, fiercely competitive industry. In recent years, there has been significant advancement in the business sector in the fields of ML and artificial intelligence (AI). Artificial intelligence (AI) has transformed decision-making procedures in the past few years by giving businesses access to sophisticated analytical tools that let them glean insightful information from massive volumes of data. Examining how AI, ML, and decision-making are used in business is the study's main objective. Additionally, it seeks to examine how AI is influencing business models and decision-making procedures. The paper claims that machine learning and AI can have a transformative effect on business decision-making, offering numerous advantages linked to efficacy, accuracy, and originality. Businesses can quickly collect and analyze large amounts of data with the help of AI-powered systems, which facilitates speedier and better-informed decision-making. All things considered, incorporating AI and ML into business decision making can improve organizational performance and influence how businesses operate in the future. © 2024 IEEE.

关键词： Adversarial machine learning

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Future Trends in HRM: The Role of Artificial Intelligence and Automation in Shaping the Workplace

Future Trends in HRM: The Role of Artificial Intelligence an...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Panicker, Aneesya Krishna, Somanchi Hari Garg, Shaifali Biswas, Prasanta Chatterjee Lourens, Melanie Kumar, K. Siva Gla University Institute of Business Management Uttar Pradesh Mathura India Vignana Bharathi Institute of Technology Department of Business Management Telangana State 501301 India Amity University Amity Business School Madhya Pradesh India Parul University Cdoe Vadodara India Durban University of Technology Deputy Dean Faculty of Management Sciences South Africa Bapatla Engineering College Computer Science and Engineering Andhra Pradesh Bapatla India

ISBN: (纸本)9798331508685

Artificial intelligence-based applications are integrated in the human resource management of firms for managing people within the region as well as international organisations. In the past years the growth of AI based applications proliferates the function of HR M that triggers to exciting new stream to study the effect of AI adoption on business level and individual outcomes. It evaluates the AI enabled HRM practises. The technology examines the firms with problem solving, decision making and utilisation of resources and opportunities for employees. The growing interest on AI based technologies for HRM for diffusion of innovation in large multinational enterprises. AI has the capability for reduction of administrative burden experienced by the HR Professional that allows them for precise decision making based on the data patterns. Adoption of artificial intelligence helps in effective employee management and decision making. So that employee of HR management can overcome the challenges. The study aims to analyse the future trends in HRM and the role of artificial intelligence and automation in shaping the workplace. © 2025 IEEE.

关键词： Resource allocation

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Enhancing Personalized Learning in E-commerce Platforms with Collaborative Filtering and C Techniques

Enhancing Personalized Learning in E-commerce Platforms with...

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2024 International Conference on science, Technology, engineering and Management, ICSTEM 2024

作者： Srinivas, D. Vijai, C. Pravallika, B. Dutt, Amit Mohan, Chinnem Rama Lourens, Melanie School of Business SR University Telangana Warangal India Department of Commerce and Business Administration Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai62 India Institute of AeronauticalEngineering Dundigal Telangana Hyderabad India Lovely Professional University Phagwara India Department of Computer Science and Engineering Narayana Engineering College Andhra Pradesh Nellore524004 India Faculty of Management Sciences Durban University of Technology South Africa

ISBN: (纸本)9798350376913

To enhance substantiated literacy within e-commerce platforms, this exploratory study investigates the possibility of integrating cooperative filtering with deep literacy using several methods. Because of the complexities of individual preferences and the ever-changing trends, traditional recommendation systems typically have difficulty properly predicting the preferences of stoners. The combination of cooperative filtering, which makes use of stoner-item relations, and deep literacy, which processes massive volumes of data to discover detailed patterns, work together to provide a stoner experience that is more refined and tailored to the individual customer. The suggested method seeks to overcome the constraints of traditional recommendation systems by providing product recommendations that are more accurate and appropriate. This is accomplished through the integration of several methodologies. This study demonstrates the efficacy of cooperative filtering and deep literacy emulsion in perfecting substantiated literacy issues in e-commerce settings. As a result, this study contributes to advancements in stoner satisfaction and engagement within online retail environments. The study was conducted through trial and evaluation. © 2024 IEEE.

关键词： Electronic commerce

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Resilient Event-Triggered Control for Lur'E Systems Against Hybrid Cyber-Attacks

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SSRN 2023年

作者： Ji, Yuxiang Zhang, Xiaoqing Jiang, Taiping Zhou, Jianping Faydasicok, Ozlem Arik, Sabri School of Computer Science & Technology Anhui University of Technology Ma’anshan243032 China Department of Mathematics Faculty of Science Istanbul University Istanbul34134 Turkey Department of Computer Engineering Faculty of Engineering Istanbul University-Cerrahpasa Istanbul34320 Turkey

This paper considers resilient event-triggered control for Lur'e systems under hybrid cyber-attacks, encompassing both deceptive and denial-of-service attacks (DoSAs). We introduce a resilient switched event-triggered scheme (RSETS) to harness the characteristics of hybrid cyber-attacks effectively. This scheme incorporates terms related to the last triggering moment and deceptive attacks into the triggering condition, optimizing communication resource utilization. Building upon this scheme, we formulate a three-mode switching-gain controller. Compared to commonly used fixed-gain control methods, the switching-gain approach enable us to achieve less conservative results. We establish a criterion for ensuring the mean-square $\mathcal{H}_{\infty}$ stability of the closed-loop Lur'e system, leveraging a piecewise Lyapunov function. When this criterion is met, the required trigger matrix and switching gains are able to be specified by explicit numerical expressions. Finally, we take Chua's circuit model as an example to show the usefulness of our control method and illustrate the superiority of the presented RSETS and switching-gain design over existing schemes. © 2023, The Authors. All rights reserved.

关键词： Denial-of-service attack

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Delay-Beat Differential Phase Demodulation for Laser Phase Noise Immunity in Phase-Sensitive OTDR

Delay-Beat Differential Phase Demodulation for Laser Phase N...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Qian, Heng Li, Chuan Li, Chengli Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Faculty of Information Engineering and Automation Kunming China School of Physics and Electronic Information Yunnan Normal University Yunnan Key Laboratory of Opo-electronic Information Technology Kunming China

ISBN: (纸本)9798350312614

A phase demodulation method immune to laser phase noise based on time-delay beat-frequency coherent Φ-OTDR is proposed. In this method, the two Rayleigh backscattered light beams with time delay are beat with local light beams respectively, and the phase difference of the two beat signals is used instead of the traditional distance-difference phase to obtain the local disturbance information. In this way, the impact of laser phase noise introduce by the distance-difference phase is avoid, and the constraint of laser linewidth on phase demodulation performance is weakened. In the experiment, a demodulation accuracy of -63.5dB rad2/Hz is achieved under the condition of a laser with a linewidth of 100kHz, which is improve by 11dB compared to traditional phase demodulation method. © 2023 IEEE.

关键词： distributed optical fiber sensing laser phase noise phase demodulation phase-sensitive optical time-domain reflectometry

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LowResIR-Net: Lightweight Residual Network for ADL Recognition Using Low-Resolution Infrared Sensor Data 16

LowResIR-Net: Lightweight Residual Network for ADL Recogniti...

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16th International Conference on ENTERprise Information Systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand

Detecting activities of daily living (ADL) is crucial for supported living and medical monitoring. Traditional approaches rely on high-resolution sensors and computationally intensive algorithms, limiting their scalability and practical use in real-world settings. This research introduces LowResIR-Net, a compact deep residual network architecture specifically tailored for recognizing ADL utilizing low-resolution infrared sensor data. LowResIR-Net efficiently extracts key features from low-dimensional data by leveraging the advantages of infrared sensing, such as privacy protection and light resilience. Its architecture combines residual connections with depth-wise convolutions, enhancing feature learning while maintaining a compact model size. The model’s performance is evaluated using the Coventry-2018 dataset, containing low-resolution infrared data from three residential sensor locations. Our tests have shown that in all three cases, LowResIR-Net consistently achieves superior performance compared to other cutting-edge deep learning models, such as CNN, LSTM, BiLSTM, GRU, and BiGRU. LowResIR-Net attains the most excellent accuracies of 90.18%, 93.75%, and 93.30%, and F1-scores of 81.81%, 89.55%, and 88.92% for scenarios I, II, and III, respectively. Furthermore, it achieves these results with fewer parameters than most other models. LowResIR-Net’s strong performance highlights its ability to accurately capture complex patterns in low-resolution infrared data for ADL detection. This study demonstrates the potential of lightweight deep learning and low-resolution infrared sensors in developing privacy-focused and computationally efficient wellness monitoring systems. © 2025 The Author(s).

关键词： Infrared detectors

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Efficient Privacy-Preserving Edge-based Dynamic Aggregation Query Over Crowdsensed Data

Efficient Privacy-Preserving Edge-based Dynamic Aggregation ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yu, Yantao Guan, Yunguo Xue, Xiaoping Lu, Rongxing Tongji University College of Electronic and Information Engineering Shanghai201804 China Eastern Michigan University School of Information Security and Applied Computing YpsilantiMI48197 United States University of New Brunswick Faculty of Computer Science FrederictionNBE3B 5A3 Canada

ISBN: (纸本)9798350351255

Aggregation query, which is one of the most crucial data analysis tools applied to vehicular crowdsensed data, is expected to extract valuable insights and support smart decision-making. Meanwhile, the edge servers are deployed to cope with the escalation of service scale, which however raises privacy concerns about the query requests and reported data. Previously reported privacy-preserving aggregation query schemes are either tailored for static datasets or lack an index structure for deployed queries, which makes them impractical in vehicular crowdsensing (VCS) scenarios characterized by large volumes of real-time data and high data update frequencies. To tackle these challenges, we propose an efficient privacy-preserving edge-based dynamic aggregation query scheme with an encrypted tree-based index. Concretely, we first design two building blocks, namely the balanced spatial encoding quadtree (BSEQtree) and a predicate encryption scheme for membership testing (PEMT), which enable the edge servers to obliviously match data and queries by traversal over the encrypted BSEQtree. Based on the above blocks and arithmetic secret sharing (ASS), we construct our scheme, in which the edge servers can efficiently and securely aggregate newly reported data to related query results. Our security analysis shows the privacy preservation of our scheme, and the experiment results on a real dataset validate the efficiency of our scheme. © 2024 IEEE.

关键词： Data aggregation

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Local Information Guided Global Integration for Infrared Small Target Detection

Local Information Guided Global Integration for Infrared Sma...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qiang Li Qianchen Mao Wenjie Liu Jinbao Wang Wenmin Wang Bingshu Wang School of Software Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Faculty of Innovation Engineering Macau University of Science and Technology Macau China

Infrared small targets often exhibit small scale and weak semantic features, which makes it a great challenge to their detection. To address this situation, we propose a novel network for infrared small target detection that combines local details information and global contextual information. To preserve the local and high-frequency details present in infrared images, we introduce a High-frequency Aware Encoder. To extract contextual information from multi-scale feature maps, we propose a Multi-scale Context Learning Bottleneck that incorporates contextual information repeatedly and performs cross-level fusion, which enables the recognition of small targets based on their surroundings. Finally, a lightweight Transformer Decoder is employed to restore the feature map, while placing attention on the target pixels. Experimental results on the IRSTD-1k dataset demonstrate that our method outperforms other state-of-the-art approaches.

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Spformer: Shape-Aware Medical Image Segmentation Via Partitioning Frequency Bands

SSRN

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SSRN 2024年

作者： Zhou, Ke Chen, Tianxiang Huang, Jiayuan Fu, Dongmei Qi, Chuanjiang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China University of Science and Technology of China Hefei China Faculty of Computer Science Universiti Putra Malaysia Serdang Malaysia

Precise medical image segmentation is crucial for computer-aided diagnosis, but current methods struggle with subtle areas in endoscopic views where lesions and normal tissue look similar. We propose SPFormer (Shape-aware Partitioning Former), a dual-branch image segmentation model to address this issue. The high-frequency branch captures edges and fine details, while the low-frequency branch focuses on overall shape and color distribution. SPFormer includes a hybrid decoder and a chimeric wavelet block, merging dual-branch information through continuous bilateral interaction. We also designed a dual-domain loss function to comprehensively evaluate the differences between the model output and the ground truth, especially when pixel value differences are small but frequency domain differences are *** enhances the accuracy and efficiency of computer-aided diagnosis, particularly in polyp and skin disease segmentation. Accurately understanding the shape and volume of lesions is crucial for disease grading and treatment planning. SPFormer outperforms existing CNN-Transformer (fusion of Convolutional Neural Network and Transformer) models, providing a more robust and reliable tool for medical image analysis. On the Kvasir-SEG (an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks), SPFormer achieves a 15.29% higher IoU (Intersection over Union) than TransUNet (Transformer-based U-shaped Network), and a reduction of 33.75 GFLOPs (Giga Floating Point Operations per Second). Compared to H2Former (Hierarchical Hybrid Transformer), SPFormer demonstrates an improvement of 5.93% in IoU, and a reduction of 11.74 GFLOPs. © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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DMNER: Biomedical Named Entity Recognition by Detection and Matching

DMNER: Biomedical Named Entity Recognition by Detection and ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bian, Junyi Jiang, Rongze Zhai, Weiqi Huang, Tianyang Huang, Xiaodi Zhou, Hong Zhu, Shanfeng Fudan University School of Computer Science Shanghai China Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Shanghai China Charles Sturt University School of Computing Mathematics and Engineering Nsw Australia Atypon Systems Llc United Kingdom Zhangjiang Fudan International Innovation Center Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Moe Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Shanghai China

ISBN: (纸本)9798350386226

Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of synonyms pose significant challenges. Traditional methods that rely on sequence labeling training datasets often struggle to handle these complexities. To address this, we introduce a novel framework for BioNER, termed DMNER, which leverages external knowledge and operates in two steps: entity boundary detection and entity category identification through matching. The core of DMNER is its second step, which determines the entity category by retrieving similar entities and their categories from a knowledge dictionary using semantic similarity matching. Our experiments on 10 biomedical datasets demonstrate that DMNER outperforms baselines across these tasks, proving its effectiveness and adaptability. DMNER is versatile and can be applied to various NER tasks, including supervised NER, distantly supervised NER, and NER on multiple datasets with disjoint label sets. The DMNER code is publicly available. © 2024 IEEE.

关键词： Semantics

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