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GAN-Loc: Empowering Indoor Localization for Unknown Areas via Generative Fingerprint Map

GAN-Loc: Empowering Indoor Localization for Unknown Areas vi...

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IEEE Communications Society conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： JinYi Yoon Yeawon You Dayeon Kang Jeewoon Kim HyungJune Lee Department of Computer Science Virginia Tech Blacksburg VA USA Department of Cyber Security Ewha Womans University Seoul South Korea Manning College of Information & Computer Sciences University of Massachusetts Amherst MA USA Department of Computer Science University of California Irvine CA USA Department of Computer Science and Engineering Ewha Womans University Seoul South Korea

ISBN: (数字)9798331519186

ISBN: (纸本)9798331519193

As indoor localization becomes a necessity to provide intelligent location-based services for in-building users, fingerprint-based positioning has been widely adopted in numerous Wi-Fi-equipped devices. However, its reliance on extensive offline site surveys limits its application in unexplored environments without prior fingerprint sampling. To address the challenge, we propose GAN-Loc, a novel generative fingerprint-based frame-work. We extract the underlying correlation between location and radio signal features, empowering indoor localization in un/under-explored areas, including unknown data points, newly deployed APs, or unexplored sites. It involves: 1) decomposing into a signal feature map for each AP perspective; 2) learning with a set of points and their associated signal strength data; 3) generating and integrating synthetic radio fingerprints; and 4) employing them into some existing localization algorithms. We evaluated GAN-Loc with extensive real-world RSSI measurements in seven different indoor places. GAN-Loc achieves the localization accuracy of up to 1.21m, 1.29m, and 1.28m for Wi-Fi, Zigbee, and BLE, respectively, compared to the accuracy of 1.47m, 1.58m, and 1.22m using an ideal ground-truth map, which is unachievable without site survey in unknown sites.

关键词： Location awareness Surveys Accuracy Three-dimensional displays Zigbee Fingerprint recognition Feature extraction Sensors data mining Wireless fidelity

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Identification of Unstable Linear Systems using data-driven Koopman Analysis

Identification of Unstable Linear Systems using Data-driven ...

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international conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Patinya Ketthong Jirayu Samkunta Nghia Thi Mai Kotaro Hashikura Md Abdus Samad Kamal Iwanori Murakami Kou Yamada Graduate School of Science and Technology Gunma University Kiryu Japan Faculty of Engineering Thai-Nichi-Institute of Technology Bangkok Thailand Department of Electrical and Electronic Posts and Telecommunications Institute of Technology Hanoi Viet Num Division of Mechanical Science and Technology Gunma University Kiryu Japan

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

System identification plays a crucial role in modern control techniques, enabling the data-driven learning of input-output maps or mathematical models. However, practical applications face challenges as the actual number of states is often unknown, and observed variables may be limited. Additionally, unstable systems present further difficulties, as their outputs rapidly diverge or saturate, hindering long-term measurement. This paper addresses these challenges by proposing a novel input-aware modeling method for unstable linear systems using data-driven Koopman analysis. Unlike traditional Koopman analysis which focuses solely on state dynamics, our method explicitly incorporates the influence of the input function $u(t)$ . This enables us to accurately capture the complete behavior of the system, even under the influence of external control signals. By leveraging Koopman operator theory on augmented state-input data, we capture both the intrinsic dynamics and the sensitivity to external control, crucial for accurate prediction and control of unstable systems. This input-aware approach extends the capabilities of data-driven Koopman analysis to improve modeling and control of complex unstable systems in various applications.

关键词： Linear systems Analytical models Sensitivity Accuracy Computational modeling Predictive models Control systems

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Prediction Chronic Kidney Disease Progression in Diabetic Patients Using Machine learning Models

Prediction Chronic Kidney Disease Progression in Diabetic Pa...

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international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Wasawat Apiromrakl Chanavee Toh Pornpen Sangthawan Thammasin Ingviya College of Digital Science Prince of Songkla University Songkhla Thailand Clinical Research and Medical Data Science Faculty of Medicine Prince of Songkla University Songkhla Thailand Department of Internal Medicine Faculty of Medicine Prince of Songkla University Songkhla Thailand Family and Preventive Medicine Faculty of Medicine Prince of Songkla University Songkhla Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Chronic kidney disease (CKD) significantly impacts healthcare system worldwide. Prediction suggesting intervention to possibly delay the CKD progression is a leading public health challenge especially in diabetes (DM) patients whose risk for CKD progression is relatively high. In this paper, a model for prognostication of kidney disease progression from stage 1 to stage 5 was determined by comparing the predictive accuracy among machine learning models including Cox regression (CoxPH), parametric survival models and random survival forest (RSF). The models were developed using a dataset of DM type 2 patients from a triary hospital in southern Thailand. The predictive accuracy parameters including time dependent concordance index (C-index) and area under the curve (AUC) were calculated and compared. The associated factors that influence renal disease progression were also assessed. From the data of 8037 DM patients, the cox regression and weibull was the best model for predicting CKD progression, with AUC of 0.934-0.965 and a C-index of 0.907 at time 6 to 60 months after DM diagnosis. The gompertz had relatively lower predictive accuracy with the C-index of 0.568 and AUC of 0.610-0.719. Based on the CoxPH model, the associated factors for CKD progression were demographic factors, comorbidities, laboratory parameters and medication received. Conclusion: CoxPH exhibit the best predictive accuracy which if implemented at the time of DM diagnosis, the results can provide insights for physician on the CKD prognosis leading to prescription adjustment and personalized recommendations to delay CKD progression, especially in DM patients with high CKD risk.

关键词： Accuracy Computational modeling Machine learning Forestry Predictive models Chronic kidney disease Diabetes

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EX-DFL: An Explainable Deep Federated-based Intrusion Detection System for Industrial IoT

EX-DFL: An Explainable Deep Federated-based Intrusion Detect...

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international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Danish Attique Wang Hao Wang Ping Danish Javeed Muhammad Adil College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Department of Automation Chongqing University of Posts and Telecommunications Chongqing China Software College Northeastern University Shenyang China College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

With the escalating volume of sensitive data trans-mitted in Industrial Internet of Things (1IoT) infrastructures, securing data produced by various IloT devices has become paramount for safeguarding critical industrial processes. In particular, the field of Industrial loT (1IoT) is witnessing an increasing threat landscape, necessitating robust Intrusion Detection Systems (IDS) to establish a secure environment. This paper addresses the security concerns associated with IloT by proposing a Federated learning (FL)-based IDS. The focus is on preserving data privacy, a critical consideration in industrial settings. The proposed explainable approach employs a Deep Neural Network in FL (EX-DFL) to detect anomalies in IloT traffic, mitigating potential security threats. A key contribution of this work is the incorporation of Explainable AI (XAI) techniques, specifically leveraging the SHapley Additive exPlanations (SHAP) library, to enhance the interpretability of the IDS decisions. This addresses the challenge of understanding the rationale behind the model's predictions in complex industrial environments. The proposed EX-DFL performance is assessed using various standard met-rics and evaluated on the CICIDS2017 dataset, demonstrating promising results by achieving over 99 % accuracy in anomaly detection.

关键词： data privacy Accuracy Image edge detection Intrusion detection Libraries Security Anomaly detection

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SensEmo: Enabling Affective learning through Real-time Emotion Recognition with Smartwatches

SensEmo: Enabling Affective Learning through Real-time Emoti...

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IEEE Internatonal conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Kushan Choksi Hongkai Chen Karan Joshi Sukrutha Jade Shahriar Nirjon Shan Lin Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY USA Department of Information Engineering The Chinese University of Hong Kong Hong Kong SAR China Department of Computer Science University of North Carolina Chapel Hill NC USA

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Recent research has demonstrated the capability of physiological signals to infer both user emotional and attention responses. This presents an opportunity for leveraging widely available physiological sensors in smartwatches, to detect real-time emotional cues in users, such as stress and excitement. In this paper, we introduce SensEmo, a smartwatch-based system designed for affective learning. SensEmo utilizes multiple physiological sensor data, including heart rate and galvanic skin response, to recognize a student's motivation and concentration levels during class. This recognition is facilitated by a personalized emotion recognition model that predicts emotional states based on degrees of valence and arousal. With real-time emotion and attention feedback from students, we design a Markov decision process-based algorithm to enhance student learning effectiveness and experience by offering suggestions to the teacher regarding teaching content and pacing. We evaluate SensEmo with 22 participants in real-world classroom environments. Evaluation results show that SensEmo recognizes student emotion with an average of 88.9% accuracy. More importantly, SensEmo assists students to achieve better online learning outcomes, e.g., an average of 40.0% higher grades in quizzes, over the traditional learning without student emotional feedback.

关键词： learning systems Emotion recognition Accuracy Education Smart systems Real-time systems Physiology Skin Sensor systems Sensors

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automated Road anomaly detector in VANET by using Deep learning

Automated Road anomaly detector in VANET by using Deep Learn...

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Information Technology (OCIT), OITS international conference on

作者： Shweta V. Bondre Vipin D. Bondre Uma Yadav Bhakti Thakare Jaya Nanwani Information Technology Department Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Electronics & Telecommunication Yeshwantrao Chavan College of Engineering Nagpur India DataScience i-Nurture Education Solutions Limited RTM Nagpur University Nagpur India Department of Computer Science & Engineeing(Cyber Security) St. Vincent Pallotti College of Engineeing Nagpur India

Road surface anomalies are a serious issue for safe and effective traffic flow; they are becoming more common due to factors like poor materials for construction, heavy traffic, climate change, and high traffic numbers. The vehicular ad hoc network (VANET), a developing research area, provides the network's vehicles with essential data. Finding and fixing these defects is essential to the mechanical safety of drivers, passengers, and automobiles. Based on the integration of artificial intelligence into automobiles, autonomous cars may find it useful to use a combination of sensors and DNN techniques to sense their environment and identify tracks and obstacles for safe and easy navigation. One of the primary problems for autonomous vehicles is avoiding dangerous situations on the road caused by serious road defects. The intelligent Transportation System (ITS) developed the concept of a vehicle network known as a "vehicular ad hoc network (VANET)" for assuring security and safety in a traffic flow in order to handle accident issues and relay emergency information. Using Edge AI and VANET, a novel method has been put proposed allowing autonomous automobiles to recognise defects on the road automatically and notify oncoming traffic of them. The methods "Residual Convolution Neural Network (ResNet-18) and Visual Geometry Group (VGG-16)" are used to automatically detect and classify the roads with irregularities, like a bump, crack or pothole, and plain roads with no anomaly using data from several online sources. The results show that the models that were utilized performed better at identifying road abnormalities than other methods.

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LoRa Network Enhancement of Oil Pipelines Leak Detection System Based on Artificial Intelligence

LoRa Network Enhancement of Oil Pipelines Leak Detection Sys...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Abbas Rushdi Kubba Hafedh Trabelsi Enetcom Sfax University Sfax province Tunisia CES _Lab ENIS Sfax University Sfax province Tunisia

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

Detection of minor leaks in oil and gas pipelines is a critical and persistent problem in the oil and gas industry. Many organizations have long relied on fixed hardware or manual assessments to monitor leaks. With rapid industrialization and technological advancements, innovative engineering technologies that are cost-effective, faster, and easier to implement efficient oil pipelines leak detection system. In this context, a Wireless Sensor Network (WSN) is increasingly required to optimize the reliability of the inspection and improve the accuracy of the real time oil pipelines leak detection and localization monitoring system. A LoRa WAN solution is proposed in this paper to detecting and locating leaks for long distance oil pipelines. This research will focus on applying several machine learning techniques to enhance the network parameters such as power consumption, packet loss and delay in order to maximize the durability of individual nodes. The system implementation will involve gathering data from $OMNeT++ {\it network}$ simulator which integrated with python script and several libraries and frameworks such as Flora, Inet and tensor flow in order to run machine learning over the Flora network. The hybrid machine learning algorithm, which consists from, random forest tree, support vector machine, k-mean clustering algorithms were used with an accuracy 89% to develop the model and enhance the LoRa network parameters such as power consumption, packet delay and packet loss and reached as example for power consumption to enhancement ratio around 48% when compared with default LoRa network.

关键词： Wireless sensor networks Power demand Machine learning algorithms Oils Pipelines Flora Packet loss

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Named Entity Recognition for Thai Historical data

Named Entity Recognition for Thai Historical Data

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international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Nasith Laosen Kanjana Laosen Thummarat Paklao Department of Digital Technology Faculty of Science and Technology Phuket Rajabhat University Phuket Thailand Andaman Intelligent Tourism and Service Informatics Center College of Computing Prince of Songkla University Phuket Thailand Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP), enabling various advanced NLP applications like information extraction, question answering, and text summarization. Thai NER presents unique challenges due to the absence of capitalization, explicit word boundaries, and sentence-ending punctuation. While NER on general domain Thai datasets has been explored, its application to historical text remains an underexplored area. Historical texts contain specialized terminology, necessitating domain-specific NER solutions for optimal performance. This study investigates Thai historical NER, utilizing historical textual data collected from the Wikipedia website. Our goal is to identify suitable word segmentation and NER methods. We evaluate the performance of the Attacut word segmentation algorithm against Deepcut and Newmm. Our findings demonstrate Attacut's superiority, achieving an F1-score of 0.9557. Furthermore, our proposed SBC model (Sentence Transformer + BiLstM + CRF) outperforms pre-trained LLMs (BERT-th1, XLM-R, WangchanBERTa) in NER, achieving an average F1-score of 0.97. The overall performance of the Attacut algorithm and the SBC model highlights their suitability for developing advanced NLP applications within the Thai historical domain.

关键词： Terminology Software algorithms Text summarization Named entity recognition Transformers Question answering (information retrieval) Internet

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Application of MOOCs in China's Vocational Education and Training: Feasibilities and Difficulties 19

Application of MOOCs in China's Vocational Education and Tra...

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Proceedings of the 11th international conference on Education Technology and Computers

作者： Jianzhen Zhang Yuyan Niu Jiehong Long Shanxi Institute of Mechanical & Electrical Engineering Changzhi China Agricultural Bank of China Changzhi Branch Office Changzhi China

ISBN: (纸本)9781450372541

Massive Open Online Courses (MOOCs) are one of the most important events in the history of education in the 21st century, which not only tearing down the walls of universities, but also achieving greatly the sharing of high-quality educational resources and providing opportunities for everyone who wants to improve themselves. This paper explored the feasibilities of MOOCs in China's vocational education and training from three aspects, including national economic strategy, enterprise market competition and individual career planning. After analyzing the difficulties, such as how to select the typical work tasks, how to draw private professional MOOC plan, how to develop professional ability, and how to get social recognition for MOOCs, four approaches were proposed, including that MOOC platform and enterprises build and share the professional MOOCs collectively, getting private MOOCs plan by big data analysis technology and data mining technology, learning professional MOOCs with online and offline, adding online virtual simulation to MOOCs learning. Approaches above will enhance MOOCs feasibility in China's Vocational education and training.

关键词： National Economic strategy Vocational MOOCs Practical Skills Vocational Education and Training Career Development Training Cost Typical Work Tasks

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Enhancing data Collection for Market Basket Analysis Through CNN Object Detection

Enhancing Data Collection for Market Basket Analysis Through...

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international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Thandar Phyo Surapong Uttama School of Information Technology Mae Fah Luang University Chiang Rai Thailand Center of Excellence in AI and Emerging Technologies School of Information Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Analyzing data from night markets, particularly for market basket analysis, poses significant challenges. Previous studies collected images of products from customers at these markets, yet these images often depicted food items and products in plastic bags or boxes, complicating identification. To address this issue, our study utilizes object detection techniques to automatically label food items and products, thereby enhancing the accuracy and efficiency of market basket analysis. We evaluate the effectiveness of these techniques using standard evaluation metrics such as Precision-Recall curves and Average Precision (AP). Our results demonstrate commendable performance, with a mean Average Precision (mAP) score of 99.4% for food items without plastic bags and 99.3% for those with plastic bags. Notably, the combined model, capable of detecting both types of food items, achieves an mAP of 84.4%. Additionally, we utilized three association rule learning algorithms-Apriori, FP-Growth, and Eclat for market basket analysis to uncover meaningful associations among food categories. Among these algorithms, the Apriori algorithm produced the highest support value of 20% and confidence of 50%, generating a total of 8 rules. The accuracy of these association rules on a new dataset, comprising 20 transactions, is calculated to be 84%. These findings offer actionable insights for businesses in the food industry, empowering them to tailor marketing strategies and product offerings to better align with consumer needs and preferences. Ultimately, our study contributes to a deeper understanding of consumer behavior and product associations in the food industry, paving the way for future research endeavors in this domain.

关键词： Consumer behavior Accuracy Food industry Object detection Association rule learning Plastics Object recognition

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