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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： M Meenakshi S. Ruban T J Nandhini S Loganayagi Raad Muhammed Sayed Enas Hassan Imad Al-Din Hussein Al-Saidi Saveetha School of Engineering Saveetha University Thandalam Chennai department of software technology St aloysius deemed to be university Department of computer science and engineering saveetha school of engineering simats saveetha university chennai india department of computer science and engineering saveetha school of engineering(simats) thandalam chennai tamil nadu india collage of nursing national university of science and tqechnology dhi qar iraq Al-Farahidi University Baghdad Iraq Computer Engineer Department al-kunooze university college basra iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Every day, pharmaceutical firms have to do extensive product analyses. The same product is frequently registered more than once in several systems, each using unique properties. For these businesses, precise and high-quality information is crucial, especially considering the nature of the pharmaceuticals these goods contain. The hypothesis of this research project is that machine learning provides a workable way to effectively combine different data sources and match records pertaining to the same product. It is no longer feasible to match records by hand because of the extraordinarily large volume that has to be processed. In a large data context, this paper presents a framework for matching pharmacological records using machine learning approaches. The framework trains machine learning by utilising specified criteria for record matching. These models are then tested by forecasting matches between records that deviate from these preset guidelines. Lastly, by creating a huge variety of record combinations and forecasting matches based on them, the system mimics the production environment. The findings show that although the training datasets produce good results—the top model has an average accuracy of about $85 \%$—the production environment has its own set of difficulties. In spite of this, matches that depart from established norms are effectively predicted by the framework. These results highlight how machine learning can be used to perform record matching tasks, as manual processing at this size is impossible.

关键词： Training Machine learning algorithms Soft sensors Machine learning Production Manuals Predictive models

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3D Object Detection with Attention:Shell-Based Modeling

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computer Systems Science & engineering 2023年第7期46卷 537-550页

作者： Xiaorui Zhang Ziquan Zhao Wei Sun Qi Cui School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorN9B 3P4Canada

LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box(BBox).However,under the three-dimensional space of autonomous driving scenes,the previous object detection methods,due to the pre-processing of the original LIDAR point cloud into voxels or pillars,lose the coordinate information of the original point cloud,slow detection speed,and gain inaccurate bounding box *** address the issues above,this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++,which effectively preserves the original point cloud coordinate *** improve the detection accuracy,a shell-based modeling method is *** roughly determines which spherical shell the coordinates belong ***,the results are refined to ground truth,thereby narrowing the localization range and improving the detection *** improve the recall of 3D object detection with bounding boxes,this paper designs a self-attention module for 3D object detection with a skip connection *** of these features are highlighted by weighting them on the feature *** training,it makes the feature weights that are favorable for object detection get ***,the extracted features are more adapted to the object detection *** comparison experiments and ablation experiments conducted on the KITTI dataset verify the effectiveness of our proposed method in improving recall and precision.

关键词： 3D object detection autonomous driving point cloud shell-based modeling self-attention mechanism

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Probabilistic-Search and Neighbor-Density Based Doppler-Shift Acquisition in Space Communications

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IEEE Transactions on Communications 2025年

作者： Zhang, Zhaowei Du, Shuai Liu, Lin Liu, Hui Zuo, Jiakuo Miao, Wang Zhao, Haitao Nanjing University of Posts and Telecommunications School of Internet of Things China Nanjing University State Key Laboratory for Novel Software Technology China Jinling Institute of Technology School of Software Engineering China University of Exeter Department of Computer Science United Kingdom

In space communications, the signal's long-distance transmission between a flying transmitter-receiver pair encounters two obstacles: a huge path-loss and a high-speed movement. The huge path-loss results in a low signal-to-noise ratio (SNR) and the high-speed movement brings a dynamic Doppler-shift, thus posing a great challenge for Doppler-shift acquisition. Under the low SNR, the Doppler-shift acquisition has to accumulate many symbols in a long-time period. However, during this period, the dynamic Doppler-shift disperses all these symbols' total energy over a wide range, thus causing the energy dispersion problem. To address this problem, we propose a probabilistic-search and neighbor-density (PSND) scheme, where the probabilistic-search considers the element's amplitude and sacrifices some redundant signal elements for a narrower search-range, while the neighbordensity sums the signal element's all neighbors for a larger signal energy. The PSND scheme includes two algorithms: the Generic PSND and Iterative PSND. The Generic PSND algorithm first selects some search-elements with their probabilities to construct a probabilistic-search-range, then selects some density-elements with their neighbor-elements to derive the neighbor-densities, and finally searches the largest neighbor-density to obtain the acquisition result. Built upon the Generic PSND algorithm above, the Iterative PSND algorithm iteratively updates the searchelements to further strengthen the neighbor-density in a narrower probabilistic-search-range. Moreover, the simulations results have demonstrated our PSND scheme's higher acquisition probability, as compared with the existing schemes. © 1972-2012 IEEE.

关键词： Signal to noise ratio

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Investigating the Use of Email Application in Illiterate and Semi-Illiterate Population

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computers, Materials & Continua 2020年第3期62卷 1473-1486页

作者： Sadeeq Jan Imran Maqsood Salman Ahmed Zahid Wadud Iftikhar Ahmad National Center for Cyber Security-UETP Department of CS&ITUniversity of Engineering&TechnologyPeshawarPakistan Department of Computer Software Engineering University of Engineering&TechnologyMardanPakistan Department of Computer Systems Engineering University of Engineering&TechnologyPeshawarPakistan 不详

The use of electronic communication has been significantly increased over the last few *** is one of the most well-known means of electronic *** email applications are widely used by a large population;however,illiterate and semi-illiterate people face challenges in using them.A major population of Pakistan is illiterate that has little or no practice of computer *** this paper,we investigate the challenges of using email applications by illiterate and semi-illiterate *** addition,we also propose a solution by developing an application tailored to the needs of illiterate/semi-illiterate *** shows that illiterate people are good at learning the designs that convey information with pictures instead of text-only,and focus more on one object/action at a *** proposed solution is based on designing user interfaces that consist of icons and vocal/audio instructions instead of ***,we use background voice/audio which is more helpful than flooding a picture with a lot of *** tested our application using a large number of users with various skill levels(from no computer knowledge to experts).Our results of the usability tests indicate that the application can be used by illiterate people without any training or third-party’s help.

关键词： Illiterate semi-illiterate email usability user interfaces

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Interactive and Explainable Optimized Learning for DDoS Detection in Consumer IoT Networks

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IEEE Transactions on Consumer Electronics 2024年

作者： Saiyed, Makhduma F Al-Anbagi, Irfan Hossain, M. Shamim University of Regina Faculty of Engineering and Applied Science ReginaSKS4S 0A2 Canada King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh12372 Saudi Arabia

The integration of Internet of Things (IoT) in consumer environments enhances convenience and security while increasing Human-computer Interaction (HCI). However,this increased interactivity has also increased the vulnerability of Consumer IoT (CIoT) networks to cyber threats,mainly Distributed Denial of Service (DDoS) attacks. The DDoS attacks,which vary in volume,present substantial challenges to these networks' operational integrity and customer trust. This paper introduces the Artificial Intelligence (AI)-driven (ADEPT) system that utilizes explainable and optimized deep-ensemble learning with pruning for DDoS detection. The system uses attention-based ensemble DL for DDoS detection,combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. To address the resource constraints of edge devices in CIoT networks,the system uses Differential Evolution (DE)-based pruning and quantization techniques,optimizing the model for efficient deployment on edge nodes while preserving high performance. An HCI interface is designed to allow network administrators and researchers to engage with the system through dynamic visualizations,facilitating complex data interpretation and empowering administrators to refine detection strategies. The interface,integrating SHapley Additive exPlanations (SHAP) and risk assessment,enhances model transparency and interpretability,highlighting the synergy of HCI and AI. The ADEPT system is evaluated using an experimental testbed and CIoT datasets and has demonstrated over 90% accuracy in detecting high-and low-volume DDoS attacks. © 1975-2011 IEEE.

关键词： Risk assessment

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A Smote-Based Churn Prediction System Using Machine Learning Techniques

A Smote-Based Churn Prediction System Using Machine Learning...

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2023 International Conference on Science, engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Akinrotimi, Akinyemi Omololu Ogundokun, Roseline Oluwaseun Mabayoje, Modinat Abolore Oyekunle, Rafiat Ajibade Adebiyi, Marion Olubunmi Augustine University Department of Mathematics Sciences Computer Science Program Ilara-Epe Epe Nigeria Kaunas University of Technology Department of Multimedia Engineering Lithuania Landmark University Omu Aran Sdg 4. Quality Education Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria University of Ilorin Department of Information and Communication Ilorin Nigeria Landmark University Omu-Aran Department of Computer Science Omu Aran Nigeria

ISBN: (纸本)9798350324785

The financial backbone of every telecommunications company is strictly made up of the number of customers patronizing the organization. Due to the high level of competition amongst existing telecommunication companies, customers sometimes leave, dropping the services of a particular one for the other. Predicting which subscribers may want to leave a telecommunications company and providing solutions to keep them from doing so, are the main objectives of customer churn prediction. Churn prediction helps classify consumers who are likely to move from one business to another. The ability to foresee churners before they leave has been increasingly useful in recent years, especially in light of increased competition among communications carriers. To compute churners for telecommunications providers, the study described in this paper used oversampling techniques to balance churn data and applied the dimensionality reduction technique to discover optimal features with a strong predictive ability for detecting would-be churners and non-churners. The model used Logistic Regression and the Naïve Bayes Classification Algorithm to implement comparative classification strategies. The performance levels of these strategies were compared using a Telecommunications Customer Churn data set, as well as performance metrics such as accuracy, sensitivity, and specificity. The Naïve Bayes Algorithm proves more efficient than its counterpart giving higher positive rate detection and a lower negative rate detection. © 2023 IEEE.

关键词： Forecasting

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Energy-Efficient Tactile-Driven Rule Configuration and Anomaly Detection in Industrial IoT Systems

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IEEE Internet of Things Journal 2025年

作者： Tan, Lizhuang Singh, Amritpal Zhang, Wei Pei, Hongjuan Zhang, Peiying Chahal, Prabhjot Kaur Singh, Maninderpal Ji'nan250014 China Pohang University of Science and Technology Department of Computer Science and Engineering Pohang37673 Korea Republic of Northumbria University Department of Computer and Information Sciences United Kingdom University of Chinese Academy of Sciences School of Engineering Science Beijing100049 China China University of Petroleum Qingdao Institute of Software College of Computer Science and Technology East China Qingdao266580 China Chandigarh Group of Colleges CEC Punjab Mohali India Southern Methodist University Electrical and Computer Engineering Department Dallas United States

The Industrial Internet of Things (IIoT) enables communication among automation systems, machinery, and sensors in an industrial setting. To optimize critical industrial operations, a substantial volume of data concerning diverse in-factory activities and automation services is generated by IoT devices and sensors. This data is subsequently transferred to distant processing systems for analysis and decision-making. Nevertheless, a substantial latency in data transmission or any abnormality in the generated data may result in delayed or erroneous decisions, consequently impacting the efficacy of essential industrial systems. To address these challenges, we established an intelligent network architecture utilizing software-defined networking that achieves tactile latencies efficiently while handling industrial data traffic in an energy-efficient manner. To address the initial challenge, the suggested architecture utilizes the Self-Organized Maps approach to distinguish between industrial traffic requiring tactile latencies and non-tactile traffic. We utilize a binary tree-based flow table mapping method to enhance flow table matching and decrease lookup times. To address the second challenge, we employ the Support Vector Machine technique to identify anomalies in real-time industrial data traffic. The Hadoop system and Mininet emulator are utilized to evaluate the proposed architecture using the UNSW dataset. The results demonstrate the effectiveness of the suggested solution in providing energy-efficient tactile assurances and identifying anomalies in traffic. © 2014 IEEE.

关键词： Table lookup

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PointDGMamba: Domain Generalization of Point Cloud Classification via Generalized State Space Model

arXiv

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arXiv 2024年

作者： Yang, Hao Zhou, Qianyu Sun, Haijia Li, Xiangtai Liu, Fengqi Lu, Xuequan Ma, Lizhuang Yan, Shuicheng Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Information Management Nanjing University Nanjing China Skywork AI Singapore Department of Computer Science and Software Engineering The University of Western Australia Australia Nanyang Technological University Singapore

Domain Generalization (DG) has been recently explored to improve the generalizability of point cloud classification (PCC) models toward unseen domains. However, they often suffer from limited receptive fields or quadratic complexity due to using convolution neural networks or vision Transformers. In this paper, we present the first work that studies the generalizability of state space models (SSMs) in DG PCC and find that directly applying SSMs into DG PCC will encounter several challenges: the inherent topology of the point cloud tends to be disrupted and leads to noise accumulation during the serialization stage. Besides, the lack of designs in domain-agnostic feature learning and data scanning will introduce unanticipated domain-specific information into the 3D sequence data. To this end, we propose a novel framework, PointDGMamba, that excels in strong generalizability toward unseen domains and has the advantages of global receptive fields and efficient linear complexity. PointDGMamba consists of three innovative components: Masked Sequence Denoising (MSD), Sequence-wise Cross-domain Feature Aggregation (SCFA), and Dual-level Domain Scanning (DDS). In particular, MSD selectively masks out the noised point tokens of the point cloud sequences, SCFA introduces cross-domain but same-class point cloud features to encourage the model to learn how to extract more generalized features. DDS includes intra-domain scanning and cross-domain scanning to facilitate information exchange between features. In addition, we propose a new and more challenging benchmark PointDG-3to1 for multi-domain generalization. Extensive experiments demonstrate the effectiveness and state-of-the-art performance of PointDGMamba. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Privacy Preserving Electronic Health Record with Consortium Blockchain 6

Privacy Preserving Electronic Health Record with Consortium ...

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6th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2022

作者： Gautama, Adri Rochim, Adian Fatchur Bayuaji, Luhur Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Department of Computer Engineering Semarang Indonesia Computer Science Budi Luhur University Jakarta Indonesia

ISBN: (纸本)9798350399615

Every patient has health record, it was written as statement of patient's conditions, treatments, and medications, and nowadays it is become digitalized, it can be copied and shared easily, but the nature of EHR is private, not for public, private between patients and healthcare provider. Privacy preserving can be done by implementing Blockchain network for storing EHR, only authorized users or nodes can access and write data to the network. New block in blockchain created as per consensus among nodes, every node can join a blockchain network if it is public, to meet a consensus required resources intensive cryptographic challenge, in private blockchain, only selected can join and involved in the network or specific transaction. In healthcare, data privacy is the main concerned, need a secure, scalable, and efficient blockchain hence implementing consortium blockchain is perfect match for EHR where multiple healthcare provider can operate in a single platform to conduct transaction or sharing EHR among members. This study analyzes 15 articles derived from IEEE Explore, Science Direct and Scopus by implementing screening with keyword Consortium Blockchain and EHR and proposing new blockchain model accommodating solution for challenges on access, storage, sharing, privacy, and performance. © 2022 IEEE.

关键词： Consortium Blockchain e-health EHR Privacy

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Arabic text sentiment analysis and emotion classification using transformers

Arabic text sentiment analysis and emotion classification us...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Nada Mossad Youssef Mohamed A Fares Ahmed B. Zaky Egypt-Japan University of Science and Technology (E-JUST) Alexandria Egypt The Department of Electrical Engineering The Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University

Sentiment analysis and emotion classification are two crucial components of natural language processing (NLP), which have been widely explored in recent years due to their broad applications. Sentiment analysis aims to identify the polarity of written texts, ranging from positive to negative. Meanwhile, emotion classification is focused on recognizing and categorizing the emotional states expressed in the text. To achieve a deeper understanding of sentiments and emotions, it's essential to utilize models like BERT transformers that can effectively interpret the context. The process begins with data preprocessing, including tokenization and noise removal, followed by fine-tuning techniques to adapt the BERT model to the proposed tasks. We employed the BERT model on four datasets obtained from various sources, including Twitter, news websites, and restaurant reviews, where each dataset represents a distinct Arabic dialect. Our proposed model outperforms commonly used techniques like LSTM and CNN, yielding superior results. Despite the progress made, there are still challenges to overcome, such as dealing with Arabic diacritics, the new Arabic Arabizi, which uses Latin characters, and handling Arabic idioms. Further research is required to address these challenges adequately.

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