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7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023

作者： Jenab, Kouroush Ward, Tyler Isaza, Cesar Ortega-Moody, Jorge Anaya, Karina School of Engineering & Computer Science Morehead State University MoreheadKY United States Networking and Telecommunication Academic Program Polytechnique University of Queretaro Queretaro Mexico

ISBN: (纸本)9783031396182

Condition-Based Maintenance (CBM) is a maintenance strategy that reduces equipment downtime, production loss, and maintenance cost based on changes in equipment condition (e.g., changes in vibration, changes in power usage, changes in operating performance, changes in temperatures, changes in noise levels, changes in chemical composition, increase in debris content and changes in the volume of material). In this study, we present the newly developed Condition Monitoring Model (CMM) based on an ensemble machine-learning model that utilizes the random forest, support vector machine, and artificial neural network classifiers, to classify data points from the normal state of a rotating machine. The efficacy of the model in adequately detecting and diagnosing faults in the rotating machine for maintenance planning is discussed in this paper. The developed model can efficiently avoid unnecessary maintenance and make timely actions by analyzing the received vibration signals from the rotating machine. An illustrative example is demonstrated to present the application of the model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision making

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Deep Learning based Human Activity Recognition in Smart Home 4

Deep Learning based Human Activity Recognition in Smart Home

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Anbazhagan, K. Swamy, G. Janani, R. Farakte, Amarsinh Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Vignana Bharathi Institute of Technology Department of Computer Science and Engineering Andhra Pradesh India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Sant Gajanan Maharaj College of Engineering Department of Electronics and Telecommunication Engineering Maharashtra India

ISBN: (纸本)9798350393354

Human activity recognition (HAR) in the context of smart homes has attracted considerable interest because to its potential to increase residents' quality of life, safety, and energy efficiency. This study dives deeper into the use of deep learning (DL) algorithms for human activity recognition in a smart home scenario. A realistic approach for distinguishing distinct human behaviors like as walking, sitting, cooking, and sleeping is provided by the combination of artificial intelligence and sensor technology. This study offers FCN-LSTM, a revolutionary technique for HAR in smart homes that combines a Fully Convolutional Network (FCN) architecture with a Long Short-Term Memory (LSTM). The proposed FCN-LSTM model capitalises on the strengths of both architectures, combining the FCN's ability to capture specific information spatially with the LSTM's experience modelling temporal correlations. This hybrid method efficiently captures both spatial and temporal variability inherent in human activities, overcoming the HAR issues. The FCN-LSTM model is trained on a huge dataset acquired from sensors deployed in a smart home setting, which includes a wide range of actions encompassing ordinary daily routines. This method is used to the Wireless Sensor Data Mining (WISDM) dataset, which was produced from data collected from a large number of people who engaged in six different activities: walking, sitting, downstairs, running, standing, and upstairs. According on the experimental data, the FCN-LSTM model outperforms established approaches and standalone systems. The results show that the FCN-LSTM algorithm performs well in recognizing human actions. The FCN-LSTM algorithm achieves a testing precision of 95.99%, with recall and f-measure of 97.11% and 99.99%, respectively. © 2024 IEEE.

关键词： Long short-term memory

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A Study on Adversarial Machine Learning in Wireless Communication Systems 8th

A Study on Adversarial Machine Learning in Wireless Communic...

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8th International Conference on Computing, Control and Industrial engineering, CCIE 2024

作者： Ajayi, Oluwaseun T. Onidare, Samuel O. Tajudeen, Habeeb Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago60616 United States Department of Telecommunication Science University of Ilorin Ilorin Nigeria

ISBN: (纸本)9789819769360

Machine learning (ML) has since been a powerful tool in advancing the modeling of many wireless communication systems, e.g., cellular networks, WiFi, vehicular networks, and space-air-ground networks. The benefits of ML have allowed wireless networks to become self-organized, adaptive, and more resilient to random behaviors. However, the availability of synthetic and/or real wireless network data and trained ML model parameters have continued to motivate attackers to exploit their vulnerabilities. Many recent works have shown that ML models lack the ability to be robust against adversarial attacks when an attacker crafts an adversarial example to fool the ML model. One of the key factors is the susceptibility of the air interface of wireless systems to adversarial attacks. The problem, thus, becomes important to be timely studied as adversarial threats may subvert ML models, making their roles in wireless communication insignificant. To clearly understand the ongoing research in this area, this paper explores existing articles that describe vulnerabilities of ML-based models in wireless communication systems, and the defense mechanisms against adversarial attacks. The experiments and results in this paper show an attacker’s exploitation of spectrum sensing data from 100 kHz to 6 GHz. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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An Advanced IoT and GPS-GSM Based Real-Time Automated Data Monitoring Robot with Image Processing and Live Streaming for Human Life and Land Mine Detection Systems

An Advanced IoT and GPS-GSM Based Real-Time Automated Data M...

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2024 International Conference on computer, Electrical and Communication engineering, ICCECE 2024

作者： Islam, Md. Muhaiminul Akter, Farzana Hossain, Md. Rakib Faruk Hossain, Md. Mahedy Hasan, S.M. Yakin Srizon, Md. Azmain Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Department of Electrical & Electronic Engineering Rajshahi6204 Bangladesh Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350386479

An era of automation is currently being experienced, where everything is becoming more automated day by day. Automation technology has been applied everywhere, from smaller to larger scales. Moreover, real-time communication between complicated human-machine interfaces is one of the difficulties of the fourth industrial revolution. That is why robots are essential in this field because they improve human lifestyles, lighten workloads, and carry out several dangerous tasks. This research presents a groundbreaking advancement in the form of an automated real-time IoT-based data monitoring robot in an era characterized by the proliferation of automation and the Internet of Things (IoT). This ground-breaking system combines GPS-GSM functionality, human life detection, land mine detection, image processing, and live streaming in an efficient manner. The robot utilizes its IoT framework and a variety of embedded sensors and technology to gather, process, and transmit realtime data from its surroundings. Notably, it supports disaster response, military operations, and safety measures by performing the vital tasks of detecting human life and land mines. The ability to categorize and process visual data is improved by the image processing feature, and situational awareness is provided by live streaming © 2024 IEEE.

关键词： Global positioning system

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Link Adaptation Performance in the Narrow Band Internet of Things 2nd

Link Adaptation Performance in the Narrow Band Internet of T...

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2nd International Conference on Global Economic Revolutions, ICGER 2023

作者： Daraghma, Raed S. M. Fouchal, Hacene Daraghmi, Yousef-Awwad Ayaida, Marwane Daraghmi, Eman Department of Telecommunication Technology Engineering Faculty of Engineering and Technology Palestine Technical Tulkarm Palestine Department of Mathematics Mechanics and Computer Science University de Reims Reims France

ISBN: (纸本)9783031505171

The aim of the paper was to explore the effectiveness of NB-IOT Narrowband Physical Downlink Shared Channel (NPDSCH), which the 3GPP released in version 13 to serve as a mobile communications feature. When compared to previous cellular technologies, NB-expanded IOT's coverage, data rate, latency, and battery lifetime are its key characteristics. These NB-IOT capabilities make it extremely useful for IOT manufacturing and enable the upcoming technology that may be applied in a variety of situations of deployments, including Agriculture, smart cities, and health, and WSNs. The primary goal of this survey is to evaluate the execution of various NB-IOT grid characteristics with acceptable rates of error in uplink and downlink connections. The effectiveness of the distinct approaches was estimated based on how well they met the requirements of IOT sector. In order to evaluate the various criterion sets and determine which options offer the optimal cost-efficiency trade-offs for building an NB-IOT grid, software simulations were employed. According to the findings, data that is sent in a reduced Transport Block Size (TBS) units experiences less errors than data that is sent in a bigger size units. The outcomes also reveal that, in the propagation channel model, the ER rises as the Doppler frequency rises. The findings also demonstrate that as the subject of modulation and coding scheme (IMCS) increases, the ER rises;finally, the results indicate that an increasing number of antennas is nearly optimal for all parameters. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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An Automated Model for Fabric Weave Pattern Recognition and Classification Using Deep Learning Based Image Processing 7

An Automated Model for Fabric Weave Pattern Recognition and ...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Pasha, Quratulain Akram, Noreen Rafique, Ayesha Akber, Anam Abbasi, Madiha Sir Syed University of Engineering & Technology Telecommunication Engg. Department Karachi Pakistan Sir Syed University of Engineering & Technology Computer Science Department Karachi Pakistan

ISBN: (纸本)9798331534271

Fabric weave type identification has many uses that adds to importance of advance classification methods;e.g fabric weave should be known in order to identify the most suitable use. But the manual inspection of fabric causes human errors such as, stressed, less focused, eye focus and mishandling of the apparatus. There are lighting effects which also degrades the performance of manual inspection. In recent years, computer vision based approaches have achieved remarkable performance in the fields of object recognition and image processing, thus opening a new door to develop an automated system for the recognition of woven fabric patterns for production of fine quality fabric according to customer needs. In this paper, we have devised an automatic weave pattern recognition mechanism based on convolutional neural networks(CNN). Initially three basic weave patterns were chosen i.e Twill, Plain and Satin. The results were analysed for its possible adaptability in the task of fabric weave pattern recognition. The classification results give the average accuracy of 72% for all three samples whereas individually, Plain weave gives 100% accuracy, Twill gives 66% accuracy and satin weave gives 50% only, which indicates a room for improvement in future. © 2024 IEEE.

关键词： Convolutional neural networks

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Design of Medicine Recommendation System (MRS) for Biomedical Application Using Machine Learning Techniques

Design of Medicine Recommendation System (MRS) for Biomedica...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Sarode, Aditi Pojage, Sarang Dineshrao Jajulwar, Kapil Dongre, Snehlata Maidamwar, Priya Nagpur India G H Raisoni College of Engineering Department of Electronics & Telecommunication Engineering Nagpur India G H Raisoni College of Engineering Department of Computer Science & Engineering Nagpur India

ISBN: (纸本)9798350361537

Machine learning has been more prevalent recently in the medical industry. These systems can both assist individuals in locating the most appropriate drug for their conditions as well as assist physicians in making better treatment choices. To create pharmaceutical recommendation systems, a variety of different machine learning techniques may be applied. Collaborative filtering is a popular technique that suggest medicines to patients based on those that people with comparable diseases have used. A different strategy like Content-based filtering, Random forest approach, and Decision tree makes prescription recommendations by considering different healthcare parameters of patients like symptoms, duration of symptoms and temperature. Medical recommendations systems based on machine learning have the potential to raise the standard of patient care. These technologies can aid in ensuring that patients receive the care they require by making personalized suggestions to them. Additionally, these technologies can contribute to enhancing the security and effectiveness of drug therapy by assisting physicians in making more knowledgeable treatment choices. Basically, this technique analyses the symptoms, duration of symptom, Body temperature and predicts the best suitable medicine. It involve multiple comparisons and multiple decisions to predicted the medicine. The results are represented graphically that gives the relation between the features as symptoms, Duration, Temperature and Target as medicines. It gives the whole account of recommending the medicines based on all the above parameters. The research will aid in the healthcare sector's transition from the conventional scenario to a more personalize model in a tele-health environment. © 2024 IEEE.

关键词： Medicinal chemistry

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Deep-EERA:DRL-Based Energy-Efficient Resource Allocation in UAV-Empowered Beyond 5G Networks

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Tsinghua science and Technology 2025年第1期30卷 418-432页

作者： Shabeer Ahmad Jinling Zhang Ali Nauman Adil Khan Khizar Abbas Babar Hayat School of Electronic Engineering Beijing University of Posts and TelecommunicationBeijing 1000876China Department of Information and Communication Engineering Yeungnam UniversityGyeongsan 38541Republic of Korea School of Information Engineering Xi’an Eurasia UniversityXi’an 710065China Department of Computer Science Hanyang UniversitySeoul 04763Republic of Korea

The rise of innovative applications,like online gaming,smart healthcare,and Internet of Things(IoT)services,has increased demand for high data rates and seamless connectivity,posing challenges for Beyond 5G(B5G)*** is a need for cost-effective solutions to enhance spectral efficiency in densely populated areas,ensuring higher data rates and uninterrupted connectivity while minimizing *** Aerial Vehicles(UAVs)as Aerial Base Stations(ABSs)offer a promising and cost-effective solution to boost network capacity,especially during emergencies and high-data-rate ***,integrating UAVs into the B5G networks presents new challenges,including resource scarcity,energy efficiency,resource allocation,optimal power transmission control,and maximizing overall *** paper presents a UAV-assisted B5G communication system where UAVs act as ABSs,and introduces the Deep Reinforcement Learning(DRL)based Energy Efficient Resource Allocation(Deep-EERA)*** efficient DRL-based Deep Deterministic Policy Gradient(DDPG)mechanism is introduced for optimal resource allocation with the twin goals of energy efficiency and average throughput *** proposed Deep-EERA method learns optimal policies to conserve energy and enhance throughput within the dynamic and complex UAV-empowered B5G *** extensive simulations,we validate the performance of the proposed approach,demonstrating that it outperforms other baseline methods in energy efficiency and throughput maximization.

关键词： Deep Reinforcement Learning(DRL) Unmanned Aerial Vehicles(UAVs) resource allocation energy efficiency 5G and beyond network

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A Novel Hybrid Genetic and A-star Algorithm for UAV Path Optimization 1

A Novel Hybrid Genetic and A-star Algorithm for UAV Path Opt...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Mehmood, Danish Ali, Asharab Ali, Seerat Kulsoom, Farzana Chaudhry, Hassan Nazeer Haider, Ammar Zeb Ul University of Engg. & Technology Telecommunication Engineering Dpt Taxila Pakistan Barani Institute of Technology Computer Science Department Rawalpindi Pakistan

ISBN: (纸本)9798350373042

Unmanned aerial vehicles (UAVs) are essential in 5G/6G communication as they provide affordable and effective solutions. However, collecting data in an unfamiliar environment from various sensor nodes is challenging due to the limited energy resources of UAVs. Therefore, careful planning of the UAV's path is vital to conserve and effectively utilize available energy resources. Our paper presents a hybrid genetic and A-star algorithm that optimizes energy consumption, making it powerful and efficient. The genetic algorithm (GA) generates numerous potential paths for a UAV to take, while the A-star algorithm identifies the most optimal route from that pool. The results show that our algorithm outperforms the state-of-the-art system while increasing the number of data nodes. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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A Transformer-based approach for Fake News detection using Time Series Analysis 7

A Transformer-based approach for Fake News detection using T...

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7th International Multi-Topic ICT Conference: IMTIC 2023

作者： Ur Rahman, Aniq Chaudhry, Hassan Nazeer Asim, Malik Muhammad Kulsoom, Farzana University of Engineering and Technology Computer Department Taxila Pakistan Barani Institute of Technology Computer Science Department Rawalpindi Pakistan University of Engineering and Technology Telecommunication Engineering Department Taxila Pakistan

ISBN: (纸本)9798350338461

Fake news is a growing problem in the digital age, spreading misinformation and affecting public opinion. Existing fake news detection is based on style analysis or news generator's behavior analysis, the former fails if the content is generated using existing news corpus while the latter requires a very specific data set. In this research, we aim to address the issue of fake news detection by employing deep learning-based time series analysis (TSA). We propose a TSA method to gauge the authenticity of the news based on previously available news content of a similar genre. We employed pre-trained models for encoding including GloVe and BERT and transformer-based sequence-to-sequence (Seq2Seq) for TSA. The results demonstrate 98% accuracy of pre-trained models, such as GloVe and BERT, over traditional encoding approaches having accuracy between 77% and 93%. Our study also compares the effectiveness of various deep learning methods, including Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), LSTM with attention, GRU with attention, and Transformers with 8 and 16 multi-heads. The results show that Transformers with 8 and 16 multi-heads achieve 98% and 97% accuracy respectively as compared to LSTM (87%) and GRU (88%). Our work is useful for future research in TSA-based fake news detection and proposes to use GloVe and BERT-based encoding and multi-head transform architecture. © 2023 IEEE.

关键词： Harmonic analysis

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