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Deployment of neural networks for WLAN thumbprint indoor positioning system

international Journal of information Technology (Singapore) 2025年 1-7页

作者： Kushwaha, Ajay Agarwal, Trapty Singh, Manpreet MP, Sunil Sharma, Amit Jain, Romil Bhuvaneswari, P. Computer Science and Engineering Rungta College of Engineering and Technology Chhatisgarh Bhilai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Centre of Research Impact and Outcome Chitkara University Punjab India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Jain (Deemed-to-be University) Karnataka Bangalore India School of Computer Applications Lovely Professional University Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Chandigarh India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Chennai India

With the application and development of related technologies based on user location information, location-based services have become essential for daily lives. Despite this fact, Wireless Local area networks (WLAN’s) often face time complexities in streamlining and collection of data. Thus, the given paper introduces a deep-learning model based on convolutional neural network (CNN) to predict user’s indoor location from the received wireless signal strength fingerprint maps. The aim is to enhance WLAN indoor localization by streamlining the data collection process. The proposed indoor positioning model leverages a region of interest (ROI) extraction technique to simulate the wireless received signal strength intensity in WLAN’s. By incorporating this advanced technique, the model aims to improve the accuracy and efficiency of WLAN localization. To optimize and evaluate the model’s performance in real-world scenarios, experiments are conducted using simulated datasets obtained from the publicly available database such as UCI repository. The results show that the proposed model attains the highest validation accuracy (97.11%) and training accuracy (97.34%) as compared to the existing deep learning algorithms used for indoor localization. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： CNN Deep learning Indoor localization Region of interest (ROI) Signal strength WLAN

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An advanced Cognitive Approach for Personalized Drug Formulation in Type 2 Diabetes Using Deep Neural Networks and Feature-based Fusion

An Advanced Cognitive Approach for Personalized Drug Formula...

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2025 international conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Singh, Brijendra Pratap Singh, Jagendra Suryavanshi, Madhuri Jailani, S. Amer, A. Beer A. Kumar, Rakesh School of Computer Science Engineering & Technology Bennett University Greater Noida India Pimpri Chinchwad College of Engineering Pune India Alpha Pharma Kaec R&d Formulation Dev. Rabigh Saudi Arabia Sadat academy for management and Sciences Canob street Alexandria Egypt Magadh University Department of Computer Science and I.T. Bodh Gaya India

ISBN: (纸本)9798331527495

Personalized medicine is an innovative approach toward achieving improved therapeutic outcomes, notably in chronic diseases like Type 2 Diabetes (T2D), where individualised drug formulations represent the crux of treatment. Conventional methods of drug prescription tend to focus on generic approaches without considering the specific genetic, physiological, and lifestyle characteristics of individuals. This research fills this gap by developing an advanced deep learning-based method for personalised drug formulation in T2D. It integrates diverse patient data, including demographics, medical history, clinical measurements, genomic information, pharmacokinetic information, real-time monitoring data, and responses to previous treatments. For optimal drug formulation, it makes use of a combination of deep learning models, such as Recurrent Neural Network (RNN) for time-series prediction, Convolutional Neural Network (CNN) for genomic analysis, GAN for producing synthetic data, and auto-encoder for feature extraction. The dataset consists of 2700 patient records collected over one year, wherein the pre-processing phase includes handling missing values, normalising continuous variables, and encoding categorical features. Trained models were assessed based on precision, recall, F1-score, and AUC ROC levels;the best performance came from the RNN model with a maximum accuracy of 97.35%. CNN, GAN, and Autoencoder also obtained similar competitive results. However, the RNN-based model received the highest score in recommending the appropriate drugs. The application of this research can contribute to the broader discipline of precision medicine. © 2025 IEEE.

关键词： Theranostics

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Design of a Micro Electromagnetic-Driven Electric Field Sensor 24

Design of a Micro Electromagnetic-Driven Electric Field Sens...

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8th international conference on Electronic information Technology and computer engineering, EITCE 2024

作者： Sun, Xiao Tang, Kai Lang, Xuqiang Ren, Zhenzhen Shi, Jianwei Wang, Kang He, Jing Scientific Research and Training Center China Astronaut Center Beijing China Beijing Information Science & Technology University Beijing China Sichuan Xichang China

ISBN: (纸本)9798400710094

The measurement of electric fields is widely used in various fields such as geophysical exploration, electrical engineering, meteorology, aerospace, and mechanical systems. Currently, mainstream electric field meters primarily use rotating shield electrodes as electric field detection probes. These probes detect charges in a spatial electric field through an induction plate. A rotating motor connects the grounded electrode on the induction plate, and the process of exposing, blocking, and shielding the induction electrode changes the induced charge, generating an induced current. However, such electric field meters are bulky and expensive. The aim of this study is to design a miniature membrane-type atmospheric electric field meter with electromagnetic drive using electrostatic induction from conductors. The device consists of a sensing module, a detection module, a wireless transmission module, and a protective casing. Compared to costly MEMS-based models, this technology offers lower costs, better environmental adaptability, and potential for widespread deployment. © 2024 Copyright held by the owner/author(s).

关键词： Electric shielding

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Design of a wearable wireless surface EMG acquisition device based on STM32F411 4

Design of a wearable wireless surface EMG acquisition device...

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4th international conference on advanced Manufacturing Technology and Electronic information, AMTEI 2024

作者： Jia, Danping Liu, Yuzhuo School of Information Science and Engineering Shenyang University of Technology Shenyang110870 China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

Human movement muscle contraction and diastole is controlled by the brain, the brain through the nerve control of the muscle will produce bioelectric signals, surface EMG signal is one of the many bioelectric signals, surface EMG signal in the field of medical rehabilitation has a significant role in this paper designed a surface EMG signal acquisition equipment, due to the surface of the EMG signal is extremely weak, a little bit of noise will produce a large interference, so this paper according to the characteristics of surface EMG signal designed amplification, filtering circuit, using the AD8221 instrumentation amplifier to collect the raw signal, combined with the amplification, filtering circuit, using the STM32 controller chip will be analog data A/D. Therefore, according to the characteristics of surface EMG signals, this paper designs an amplification and filtering circuit, using AD8221 instrumentation amplifier to collect the original signal, combined with the amplification and filtering circuit, using the STM32 master chip to convert the analog data to A/D and transfer it to the upper computer system, and then carry out the operation of real-time visualization of waveforms. Finally, through the experimental test this system can better amplify the original signal and have good anti-interference. © 2025 SPIE.

关键词： Human rehabilitation equipment

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Design and Implementation of Road Vehicle Counting Model Based on YOLOv8 24

Design and Implementation of Road Vehicle Counting Model Bas...

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8th international conference on Electronic information Technology and computer engineering, EITCE 2024

作者： Duan, Xu Xue, Sijuan Zhou, Zhihai Duan, Xuran College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

With the gradual increase in the number of urban vehicles, traffic management is faced with some research and challenges. Therefore, a road vehicle counting model based on YOLOv8 is proposed to realize efficient and accurate vehicle detection and counting. Through calculations, the system can determine whether the road is crowded and then send signals to the traffic system to control the timing of traffic lights. This will reduce road congestion and traffic accidents. Specifically, we collected data from different cities and trained YOLOv8 models to track target vehicles using Kalman filtering tracking algorithms. To avoid counting inaccuracies due to irrelevant regions, we add irrelevant region masking. The results of the experiment were very promising, with the YOLOv8 model averaging relatively stable scores in accuracy, precision, recall, and f1 scores. © 2024 Copyright held by the owner/author(s).

关键词： Traffic congestion

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Optimizing acoustic field rendering through heterogeneous computing 5

Optimizing acoustic field rendering through heterogeneous co...

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5th international conference on Signal Processing and computer science, SPCS 2024

作者： Sheng, Zhang Zhu, Huiling Hefei University of Technology Hefei China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

The objective of this research is to enhance sound field rendering efficiency using a heterogeneous computing framework. By integrating GPU and CPU resources, we address performance challenges in complex environments and large-scale ray tasks. Evaluations of the NVIDIA RTX A6000 GPU and AMD 9754 CPU led to an ray task allocation mechanism, optimizing resource use and maximizing computational efficiency. Experimental results show that this strategy significantly accelerates sound field rendering, achieving nearly 400 times faster performance than traditional single-core CPU computations. © 2025 SPIE.

关键词： Rendering (computer graphics)

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A review of ADDoS attack mechanisms, amplification vulnerability discovery, and mitigation 5

A review of ADDoS attack mechanisms, amplification vulnerabi...

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5th international conference on Signal Processing and computer science, SPCS 2024

作者： Jiang, Sikang Chen, Hongyu Jia, Zhipeng Wang, Zhichao Cai, Ruijie Information Engineering University Zhengzhou450001 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Amplification-based distributed denial of service attacks (ADDoS) are a common and severe threat to the Internet. Recent reports of ADDoS attacks demonstrate that such attacks not only generate massive traffic but also exploit a variety of protocol types. Traditionally, attackers needed to control a substantial amount of resources to launch an attack. With the use of reflectors and amplifiers in DDoS attacks, an increasing number of protocols are being utilized for ADDoS. In this paper, we explore and review the basic concepts and process of ADDoS attacks, provide a detailed analysis of previous ADDoS incidents and related CVE vulnerabilities, and classify amplification vulnerabilities from the perspective of their causes. Additionally, we summarize common reflection patterns and methods for discovering amplification vulnerabilities, and propose methods to mitigate ADDoS attacks. © 2025 SPIE.

关键词： Reflection Reflectors Internet Analytical research Tunable filters information security Network security Clocks Internet of things Network architectures

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Unmasking the Reality: A Comprehensive Study of Deepfake Identification Techniques and Their Expanding Terrain 2

Unmasking the Reality: A Comprehensive Study of Deepfake Ide...

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sharma, Shubh Pathak, Disha Mohini Jain, Dhyanendra Srivastava, Somya Vats, Prashant Upadhyay, Govind Murari Department of Computer Science ABES Engineering College U.P. Ghaziabad India Department of Computer Science Engineering-AIML ABES Engineering College U.P. Ghaziabad India Dept. of CSE School of CSE Manipal University Jaipur Rajasthan Jaipur India Dept. of Computer Application Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9798331530389

The swift progression of deepfake-generating technologies has elicited much apprehension about their potential misuse in digital media, necessitating the development of efficient detection methods. This research aims to improve the precision of deepfake detection by the application of spatial-temporal feature engineering methods. A comparative comparison of advanced preprocessing techniques is performed to enhance the extraction of spatial and temporal indicators essential for detecting deepfake abnormalities. The study investigates the amalgamation of sophisticated machine learning and deep learning models with designed characteristics to get enhanced detection efficacy. Experimental assessments are conducted on benchmark datasets to verify the effectiveness of the suggested methodologies. The findings highlight the importance of effective preprocessing strategies in enhancing model generalizability and dependability, hence supporting the overarching goal of preserving digital authenticity. © 2025 IEEE.

关键词： Contrastive Learning

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Fiber Optic Communication Systems for Next-Generation Smart Cities 3

Fiber Optic Communication Systems for Next-Generation Smart ...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Patil, Shrinivas Pandian, R. Naval, Preeti Jain, Romil Biswas, Sourav Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

The evolution of urbanization and the variety of information communication technologies have given birth to the idea of Smart Cities those territories that aspire to improve the lifestyle standards for their inhabitants through development technologies. Communication network infrastructures are the foundation of a smart city where information and services seamlessly flow between them. This era of communication faces challenges not only in reaching millions of users through relatively limited current process technologies but also in the increased demand for high throughput and low latency services, etc. Fiber optic communication systems are the most promising and effective solutions, with properties like high bandwidth, back-to-back transmission at low delay periods, and immunity to electromagnetic noise source devices among various existing commercial technologies. They utilize fiber optic cables to transmit data in the form of light pulses, which enables a faster and less susceptible means of communication. Fiber optic communications systems will grow as the need for smart city applications increases. Designs of next-generation fiber optic systems will meet smart city requirements, including high-speed data transmission, low power consumption and costeffectiveness. These configurations employ advanced technologies, including wavelength division multiplexing, coherent detection and modulation techniques for improved speed and distance capabilities. © 2025 IEEE.

关键词： Fiber to the x

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YOLOv5-Based Rice Disease and Pest Recognition Design 24

YOLOv5-Based Rice Disease and Pest Recognition Design

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8th international conference on Electronic information Technology and computer engineering, EITCE 2024

作者： Zhao, Xiaowei Zhou, Jiheng Zhong, Suixi Zheng, Zhiguo Zhang, Jianhua School of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China Department of Physics Hainan University Hainan Haikou China

ISBN: (纸本)9798400710094

This research aims to enhance the speed and accuracy of identifying rice pests and diseases utilizing YOLOv5 object detection algorithm to assisting agricultural producers in effective management. The study focuses on a publicly available data set of images to detect rice pests and diseases. Using YOLOv5 algorithm to train on this data set, developing an effective model that is integrated into a user-friendly interface, allowing producers to quickly identify types of rice pests and diseases by simply uploading images. After noising reduction and coloring transformations of the data set, the YOLOv5 model is trained with the processed images. The model achieved an accuracy and recall rate of 91.3% and 80.6%, respectively. It can accurately identify various types of rice pests and diseases, with all identification accuracies exceeding 86%. Ultimately, the experimental results demonstrate substantial practical value for agricultural producers in daily monitoring and managing rice pests and diseases. © 2024 Copyright held by the owner/author(s).

关键词： information management

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