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Integrated Multi-Sensory M2M System for Enhanced Military Situational Awareness and Response

Integrated Multi-Sensory M2M System for Enhanced Military Si...

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international conference on Electrical Information and Communication Technology (EICT)

作者： Fahmida Nusrat Md. Sajjad-Ul Islam Umma Honey Chowdhury Nazim Uddin Sakib Nudar Ibnat Md Musfiqur Rahman Mazumder Dept. of Electrical and Electronic Engineering International Islamic University Chittagong Chattogram Bangladesh Dept. of Electronic and Telecommunication Engineering International Islamic University Chittagong Chattogram Bangladesh

this paper presents a groundbreaking machine-to-machine (M2M) based system for military applications, focusing on object and sound detection, and command transmission. Leveraging modern sensors and M2M technology, the system enables real-time monitor and response, significantly enhanced decision-making in the high-risk scenarios. the main aim of this study is to create a resilient M2M system that provides timely data to military personnel, enabling prompt detection and reaction to potential threats. the object detection component uses ultrasonic waves that is capable of identifying intruders in real-time. the sound detecting module can distinguish gunshots, explosions, and speech using powerful signal processing. this facilitates the prompt evaluation of potential threats. the command interface of the system is designed to be user-friendly, enabling remote control capabilities that promote real-time situational awareness and prompt reactions. the primary outcomes are evident in a highly effective M2M system that provides real-time and precise recognition of objects and sounds, as well as the ability to remotely execute commands. this phenomenon enhances the overall effectiveness of military operations and ensures the safety of people. In summary, the proposed system exhibits diverse applications, from border protection to stealth detection, and offers substantial advantages over conventional methods. this research marks a crucial step towards modernizing military operations, with implications for both safety and operational efficiency.

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Breast Cancer Detection Using Optimal machine learning Techniques: Uncovering the Most Effective Approach 6th

Breast Cancer Detection Using Optimal Machine Learning Techn...

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6th international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2023

作者： Joshi, Tanmay Hegadi, Ravindra MGM’s Jawaharlal Nehru Engineering College Aurangabad India Central University of Karnataka Kalaburagi India

ISBN: (纸本)9783031530845

this research paper aimed to identify the most effective machine-learning approach for breast cancer detection. the study utilized the Breast Cancer Wisconsin (Diagnostic) Data Set and evaluated five different algorithms: Logistic Regression, Support Vector machine (SVM), Decision Tree, Random Forest, and K-Nearest Neighbours (KNN). After thorough analysis, all five algorithms demonstrated high accuracy rates, ranging from 93% to 97.37%. the SVM and Random Forest models achieved the highest accuracy, F1-score, recall, and precision values, making them the most effective approaches for breast cancer detection. these models successfully classified benign and malignant tumours, reducing unnecessary treatments and improving patient safety. Additionally, the research reviewed other studies in the field of breast cancer detection using machine learning. these studies highlighted the potential of uncertain expert systems, deep learning models, microarray analysis, and multi-classifier approaches to enhance breast cancer diagnosis and prognosis prediction. the findings of this research have significant implications for healthcare. SVM and Random Forest, as machine learning techniques, can be leveraged to develop accurate and efficient breast cancer detection systems. Implementing these models in clinical practice can improve early detection rates, minimize unnecessary surgeries, and enhance patient outcomes. this research contributes to the existing knowledge on breast cancer detection using machine learning techniques and offers valuable insights into the most effective approach for improving early diagnosis and treatment outcomes for breast cancer patients. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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Multi-modal signal Based Childhood Rolandic Epilepsy Detection 6th

Multi-modal Signal Based Childhood Rolandic Epilepsy Detecti...

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6th international conference on Cognitive Systems and signal processing, ICCSIP 2021

作者： Wu, Yixian Hu, Dinghan Jiang, Tiejia Gao, Feng Cao, Jiuwen Machine Learning and I-health International Cooperation Base of Zhejiang Province Hangzhou Dianzi University Hangzhou310018 China Artificial Intelligence Institute Hangzhou Dianzi University Zhejiang Hangzhou310018 China Department of Neurology The Children’s Hospital Zhejiang University School of Medicine National Clinical Research Center for Child Health Zhejiang Hangzhou China

ISBN: (纸本)9789811692468

Electroencephalogram (EEG) is the common signal used in epilepsy analysis but suffered from the inconvenient issue in data acquisition, especially when applied to children with Rolandic epilepsy. In this paper, we present a study on multi-modal signal based epileptic seizure detection for children suffered from Rolandic epilepsy, where EEG combined with the synchronized surveillance video is included for analysis. the Mel frequency cepstrum coefficient (MFCC) and linear prediction cepstrum coefficient (LPCC) features are taken to characterize EEG. the spatiotemporal interest points (STIP) are extracted from video sequences to construct a bag of words model, which are based on the descriptors of histograms of oriented gradient (HOG) and the histograms of oriented optical flow (HOF) in the neighborhood. the histograms of word frequency (HWF) features are obtained for video representation. Direct feature fusion on the EEG based MFCC+LPCC and video based HWF is applied for classifier training. Data of 13 children with Rolandic epilepsy recorded from the Children’s Hospital, Zhejiang University School of Medicine (CHZU) are applied in the experiment. the results show that the model trained on the multi-modal features of EEG and video can achieve the highest overall accuracy of 98.2%. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Electroencephalography

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Optimized Construction Project Management Using Ai And machine learning

Optimized Construction Project Management Using Ai And Machi...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Kamal Sharma Pravin P Patil Aparna Srivastava K Mayuri Jacob Michaelson Amit Srivastava Department of Mechanical Engineering Institute of Engineering and Technology GLA University Mathura Uttar Pradesh India Department of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Lloyd Institute of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Lloyd Law College Greater Noida Uttar Pradesh India

Starting during the 1950s, with the coming of Data Advances, artificial intelligence (AI) strategies have been created. New strong calculations and, specifically, the openness of the web as a monstrous store of unstructured information have rekindled the issue of AI's worth as processing power has expanded. this is empowering for the field of development the board specifically, as development projects are filling in size and intricacy as time and monetary constraints increment the quantity of stakeholders with contending needs. By the day's end, development the executives' utilized to get everything all together with the goal that precise expectations can be made about the end result. the subject of Artificial Intelligence (AI) incorporates AI as a subset and an assortment of related innovations. K-Nearest Neighbour's (KNN) is a famous managed learning strategy, making it one of the most broadly utilized AI calculations. In light of financial information in Vietnam, this article utilized the K-Nearest Neighbour's (KNN) calculation to appraise the development cost file. the structure cost record determining model utilized information from 2016–2019 and was predicated on seven financial factors. As per the discoveries, the K-Nearest Neighbour's (KNN) relapse technique utilized in the development cost list forecast model superior exactness over the customary strategy.

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A Hybrid IoT-ML Based Electromagnetic Compatibility Monitoring and Prediction System for Biomedical Devices

A Hybrid IoT-ML Based Electromagnetic Compatibility Monitori...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Jean Pierre Nyakuri Omar Gatera Chiedza Hwata Gerard Rushingabigwi Celestin Twizere Didacienne Mukanyiligira African Centre of Excellence in Internet of Things (ACEIoT) University of Rwanda Kigali Rwanda East Africa's Regional Centre of Excellence in Biomedical Engineering and E-Health (CEBE) University of Rwanda Kigali Rwanda

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

the increasing of electromagnetic radiation from wireless electronic devices has raised concerns regarding electromagnetic compatibility (EMC) and the potential for electromagnetic interference (EMI) in healthcare environments especially for critical medical equipment. To address this challenge, regular inspection is required. We propose an innovative solution integrating Internet of things (IoT) technology and machine learning (ML) algorithms for monitoring and predicting EMC levels for medical equipment. this research focuses on the development and implementation of a wireless sensor network (WSN) capable of capturing realtime data from medical equipment and surrounding environment such as electromagnetic radiation (EMR), temperature, operating voltage and aging of medical devices. this data is integrated into a ML model based on multi linear regression (MLR) to predict EMC and anticipate potential EMI events. the system utilizes a Raspberry Pi 4 for processing data to the edge of network and GSM/GPRS SIM7600 4G HAT for data transmission along with sensors for temperature, EMI, and operating voltage, sending data to cloud for further analysis and visualization. A dashboard is built for remote monitoring via cloud platform. the regression equations generated by ML algorithm, accurately forecast EMC levels based on environmental factors and device characteristics. Alerts are disseminated via email and smartphone notifications to facilitate timely intervention and ensure uninterrupted operation of medical equipment. Our findings warrantee the effectiveness of proposed system for proactively monitoring EMC hazards from biomedical devices and surrounding electronic equipment so as to improve hospital safety for both patients and staff.

关键词： Temperature sensors Wireless sensor networks Temperature distribution Electric potential Electromagnetic interference signal processing algorithms Voltage Electromagnetic radiation Electromagnetic compatibility Internet of things

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Energy Saving Temperature Control of UAV Nest Inverter Air Conditioning Based on Variable Air Volume and DE Algorithm

Energy Saving Temperature Control of UAV Nest Inverter Air C...

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), international conference on

作者： Weihao Zhang Wenbin Wu Zhuolei Chen Meiyan Yu Hui Dong State Grid Fujian Electric Power Research Institute Fuzhou China State Grid Electric Power Space Technology Co. Ltd. Beijing China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

the current temperature control model of UAV nest inverter air conditioning energy saving is mostly set to a single scheduling form, and the control efficiency is relatively low, resulting in a decline in the final energy consumption optimization ratio. therefore, the design and practical verification of the temperature control method of UAV nest inverter air conditioning energy saving based on variable air volume and DE algorithm are proposed. According to the current measurement, the dynamic balance adjustment of the air volume and frequency of variable frequency air conditioning is carried out first. Based on this, the change trend of the thermal load of the UAV nest is analyzed. On this basis, combined with variable air volume and DE algorithm, the efficiency of control is strengthened, and the energy-saving temperature control model of UAV nest inverter air conditioning with variable air volume and DE algorithm is established, and the multi-level real-time balance correction method is used to achieve temperature control processing. the test results show that compared with DFT central air conditioning control method and deep learning intelligent energy-saving air conditioning control method, the energy consumption optimization ratio finally obtained by the variable air volume and DE calculation UAV machine nest variable frequency air conditioning energy-saving temperature control method is relatively high, which indicates that this time, with the help and support of variable air volume and DE algorithm, the designed energy-saving temperature control method of UAV nest inverter air conditioning is more flexible and changeable, and its own temperature control effect has been further improved, and the results have been verified in practice.

关键词： Air conditioning Energy consumption Heuristic algorithms Energy conservation signal processing algorithms Autonomous aerial vehicles Inverters Temperature control Frequency measurement Optimization

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An Improved Positioning System For 6G Cellular Network

An Improved Positioning System For 6G Cellular Network

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international conference on Computer and Information Technology (ICCIT)

作者： Md. Zahidul Islam Sifat Muhammad Mostafa Amir Faisal Md. Mujahid Hossain Md. Aminul Islam Department of Electronic and Telecommunication Engineering International Islamic University Chittagong Chittagong Bangladesh

Information about the whereabouts of oneself or our cell phones has always been crucial to humans. Technological advancements have supported us across various domains, spanning from interior environments to outdoor global positioning systems. However, achieving real-time indoor positioning has remained a formidable task. the requirement for enhanced positioning has gained prominence in the context of the emergence of 6G cellular networks. the introduction of novel radio technologies, characterized by reduced end-to-end latency, specialized control protocols, and increased processing capacity at the network edge, has opened doors to harnessing the 6G cellular network's full potential for precise localization in indoor and outdoor settings. Within the 6G cellular network context, this study successfully implemented the classic signal fingerprinting approach using the Received signal Strength Indicator in combination with machine learning algorithms. Consequently, it proposed an improved positioning strategy designed for indoor localization scenarios in the next 6G cellular network environment. Using this method, with two datasets, the mean error distance of 0.08025523, meaning an accuracy of 99.92% and 0.10692412 was achieved, which is 99.89% accuracy.

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Research on Noise processing Methods of Speech Recognition in Noisy Environment 22

Research on Noise Processing Methods of Speech Recognition i...

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Proceedings of the 2022 6th international conference on Electronic Information Technology and Computer Engineering

作者： Zebin Zhao Nan Yang Shulin Yang School of Information Engineering Beijing Institute of Graphic Communication China Beijing International Studies University China

ISBN: (纸本)9781450397148

Speech noise reduction technology helps automatic speech recognition technology become an important tool to meet excellent human-computer interaction. According to the location of noise processing modules, it can be classified into two modes: signal-side noise reduction and model-side noise reduction. According to the methods used in speech noise reduction, it can be divided into digital signal noise reduction methods and model noise reduction methods. At present, speech noise reduction technology is in the detail development stage of striving for perfection. By introducing the current research status of speech noise reduction technology in academic circles and classifying signal noise reduction methods and model noise reduction methods, the improvement and innovation of these methods are summarized, and the advantages and disadvantages and applicable scenarios of the mainstream speech recognition noise reduction methods are objectively compared. the development and shortcomings of automatic speech recognition technology in the field of noise reduction are expounded, and the predicting the key direction of more optimized development of noise reduction methods can be used as reference for further research on automatic speech recognition noise reduction.

关键词： automatic speech recognition, noise reduction of speech, speech enhancement, signal processing deep learning

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Task-specific pre-training improves models for paraphrase generation 22

Task-specific pre-training improves models for paraphrase ge...

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Proceedings of the 2022 6th international conference on Natural Language processing and Information Retrieval

作者： Oleksandr Skurzhanskyi Oleksandr Marchenko Computer Science and Cybernetics / Applied Research Taras Shevchenko National University of Kyiv / Grammarly Ukraine Computer Science and Cybernetics Taras Shevchenko National University of Kyiv Ukraine

ISBN: (纸本)9781450397629

Paraphrase generation is a fundamental and longstanding problem in the Natural Language processing field. With the huge success of transfer learning, the pre-train → fine-tune approach has become a standard choice. At the same time, popular task-agnostic pre-trainings usually require gigabyte datasets and hundreds of GPUs, while available pre-trained models are limited by fixed architecture and size (i.e. base, large). We propose a simple and efficient pre-training approach specifically for paraphrase generation, which noticeably boosts model quality and matches the performance of general-purpose pre-trained models. We also investigate how this procedure influences the scores across different architectures and show that it works for all of them.

关键词： datasets fine-tuning machine learning neural networks paraphrase generation pre-training

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Anomaly modelling in machine learning based navigation system of autonomous vehicles 6

Anomaly modelling in machine learning based navigation syste...

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6th international conference on signal processing and Intelligent Systems (ICSPIS)

作者： Eshraghi, Hossein Majidi, Babak Movaghar, Ali Khatam Univ Dept Comp Engn Tehran Iran Sharif Univ Technol Fac Comp Engn Tehran Iran

ISBN: (纸本)9781728186290

In the past few years, autonomous navigation systems are gradually introduced to the consumer vehicles and the cars using these systems are gaining popularity. the autonomous navigation systems are using machine learning and deep learning models for visual processing of complex road scenes in various scenarios. these machine learning models are trained using numerous test rides and the process of training sometimes continues as the vehicle is navigating the streets. these machine learning models are vulnerable to anomalies which can lead to various issues for the autonomous driving systems. these issues will put the safety of the vehicle in danger. In this paper, the issue of the impact of the anomalies on the deep learning models of smart vehicles is investigated and a method for anomaly modelling in the machine learning based autonomous driving systems is presented. the presented model can increase the robustness of the autonomous vehicles.

关键词： Autonamous vehicles Deep learning Robustness machine learning

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