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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Salahat, Mohammed Said, Raed A. Hamid, Khalid Haseeb, Usama Abdel Maguid Abdel Ghani, Elsaid Abualkishik, Abedallah Iqbal, Muhammad Waseem Inairat, Mohammad University of Science and Technology of Fujairah College of Engineering and Technology Fujairah United Arab Emirates Canadian University Dubai Faculty of Management Dubai United Arab Emirates Superior University Department of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Superior University Department of Software Engineering Lahore Pakistan Canadian University Dubai Dubai United Arab Emirates American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates VICE CHANCELLOR Skyline University College Dubai United Arab Emirates

ISBN: (纸本)9798350335644

Quality assurance ensures that the project is carried out following the agreed-upon supplies, principles, and then purposes. The goal of type and its calibration investigation remains to better comprehend the multifaceted course of software analysis. Current software rehearses have some errors such as analysis techniques, user arrogances, and logistic culture. Altogether of these subjects, including such rushed testing, way to quickly times, totally inadequate supporting documents, and so on others must be addressed together. Describe a solution to the significant trouble of product testing in quality managing. We will discuss about the solutions of testing problems in quality management. propose a solution to the glitches mentioned above. © 2023 IEEE.

关键词： Quality control

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Privacy-Preserving 3D Skeleton-Based Video Action Recognition via Graph Convolution Network

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IEEE Transactions on Consumer Electronics 2024年

作者： Gao, Xuesong Li, Keqiu Liu, Xiulong Nie, Jie Chen, Weiqiang Tian, Yonghong Tianjin University College of Intelligence and Computing China Ocean University of China Faculty of Information Science and Engineering China Hisense Co. Ltd State Key Laboratory of Digital Multimedia Technology China Peking University School of Computer Science China

In recent years, 3D skeleton data-based human action recognition has attracted an increasing number of researchers because of its high robustness under illumination change and scene variation. However, in 3D skeleton data-based human action recognition, there are still some challenges including multi-object recognition and private data leakage. To address this issue, in this paper, we propose a privacy-preserving end-to-end action recognition model based on a Graph Convolutional Network (GCN). Specifically, we innovatively propose a 3D pose estimation method to obtain 3D human skeleton data from color data streams and depth data streams, in which the mean filtering technique is applied to protect users' video data privacy while not affecting the extraction of the skeleton data. Then, we propose a feature representation method and represent the skeleton data to a GCN model, which convolves the centroid of the human skeleton, four limbs, and the trunk of a person. After that, we propose a method to fuse and extract multiple persons' movement features and propose a novel GCN-based interactive action recognition model to recognize multiple people's skeleton data. Moreover, to protect recognition model privacy, we also design a ciphertext-based secure action recognition method, which guarantees the confidentiality of model parameters during action recognition. Finally, we evaluate the performance of our model in real datasets, and the results demonstrate that our model can achieve high recognition accuracy than existing models. Meanwhile, experimental results demonstrate that the mean filtering technique can well protect the privacy of the appearance data and the information of skeleton data can be well preserved. © 1975-2011 IEEE.

关键词： Differential privacy

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IoT-Driven Image Recognition for Microplastic Analysis in Water Systems using Convolutional Neural Networks

IoT-Driven Image Recognition for Microplastic Analysis in Wa...

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computer, Communication and Control (IC4), International Conference on

作者： M D Ashfaqul Hasan K. Balasubadra G. Vadivel N. Arunfred M.V Ishwarya S. Murugan Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Information Technology R.M.D. Engineering College Kavaraipettai Chennai Tamil Nadu India Department of Information Technology University of Technology and Applied Science Salalah Dhofar Oman Faculty of Management Studies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Microplastic pollution in water systems is growing, requiring novel detection and analysis methods. This research presents an Internet of Things (IoT)-driven image identification system using Convolutional Neural Networks (CNNs) to detect and quantify microplastics in water samples. The suggested method is more scalable and responsive due to IoT real-time data capture and remote monitoring of water infrastructure. An innovative CNN architecture for image processing allows the system to accurately identify micro plastics. The CNN model is trained and validated using a large dataset of micro plastic-containing water samples. The trained model can recognize various sizes, shapes, and colors of micro plastics, making it responsive to different environmental situations. The IoT architecture also allows image recognition modules in dispersed sensor nodes to cover water systems. Extensive studies prove the system can analyze vast amounts of image data quickly and reliably. Edge computing also minimizes latency and improves micro plastic analysis system responsiveness. The suggested IoT-driven image recognition method for continuous micro plastic pollution monitoring and evaluation in water systems seems promising. Scalability, realtime capabilities, and accuracy make it useful for environmental monitoring agencies and academics trying to reduce microplastics’ influence on aquatic ecosystems. This system advances IoT applications in environmental and pollution management.

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Enforcing Social Distancing with YOLO Algorithm Utilizing Object-to-Object Distance

Enforcing Social Distancing with YOLO Algorithm Utilizing Ob...

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information technology (ACIT)

作者： Muhamad Irsan Rosilah Hassan Taj-Aldeen Naser Abdali Mohamad Khairi Ishak School of Computing Telkom University Bandung Indonesia Department of Information Technology College of Engineering and Information Technology Ajman University U.A.E Faculty of Basic Education University of Misan Misan Iraq Department of Electrical and Computer Engineering Ajman University Ajman United Arab Emirates

Instances of COVID-19 transmission occur daily due to individuals failing to maintain distance or engaging in physical contact with others who may be contaminated with the virus. To mitigate this issue, this study has developed a system to detect human subjects practicing social distancing. The system utilizes a Raspberry Pi 4 Model B 8GB device in combination with a Logitech HD Webcam C270 camera. To detect human subjects, the Convolutional Neural Network is employed, utilizing the You Only Look Once (YOLO) method. In the testing phase of the tool, the system successfully identifies human subjects and assesses their proximity to others. It also detects instances of social distancing violations. The system achieved an average mean Average Precision (mAP) of 0.9792, a Precision of 0.9482, a Recall of 0.9819, and an f1 score of 0.9648.

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Design of an Optimized Asymmetric Multilevel Inverter with Reduced Components Using Newton-Raphson Method and Particle Swarm Optimization

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Mathematical Problems in engineering 2023年第1期2023卷

作者： Gireesh Kumar, Devineni Venkata Sireesha, Nagineni Ganesh, Aman Kotb, Hossam Aboras, Kareem M. Zeinoddini-Meymand, Hamed Kamel, Salah Department of Electrical and Electronics Engineering B v Raju Institute of Technology Telangana Narsapur502313 India Department of Information Technology Institute of Aeronautical Engineering Hyderabad Telangana Dundigal500043 India Ambala Mullana133203 India Department of Electrical Power and Machines Faculty of Engineering Alexandria University Alexandria21544 Egypt Department of Electrical and Computer Engineering Graduate University of Advanced Technology Kerman Iran Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

Multilevel inverters have great scope in current developments of grid-connected solar PV systems. Two-level inverters are the simplest kind of multilevel inverter available (MLI). As the number of output levels is raised, the total harmonic distortion decreases. In classic MLI topologies, more electronic components are utilized to get higher-level outputs, which raise the cost, complexity, and volume of typical MLI installations. By reducing the design components, the cost of the system will be reduced. Furthermore, the two- and three-level inverters produce constant dv/dt output, which increases the stress on the power switches. This research proposes an asymmetric MLI topology that is suitable for PV applications and utilizes less number of DC sources and switches. The proposed inverter is controlled by selective harmonic elimination-based pulse width modulation (SHEPWM) to eliminate the lower-order dominant harmonics. The nonlinear equations produced by the SHEPWM are solved for the switching angles of the proposed inverter using the Newton-Raphson (NR) method and particle swarm optimization (PSO) method for various modulation indexes. The performance of the proposed inverter is analyzed based on the total harmonic distortion (THD) of the output for different operating levels of the inverter by comparing similar topologies in the literature. The THD obtained by the NR method is 7.3% and by using PSO is 4.23% at 0.9 modulation index. © 2023 Devineni Gireesh Kumar et al.

关键词： Newton-Raphson method

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A machine learning approach for text pattern diagnosis in mental health consultations

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Decision Analytics Journal 2025年 15卷

作者： Juanita, Safitri Daeli, Anisah Hasratniwati Syafrullah, Mohammad Anggraeni, Wiwik Purnomo, Mauridhi Hery Faculty of Information Technology Universitas Budi Luhur Jakarta 12260 Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia Department of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia

Online health consultation services are crucial for mental health support, particularly in densely populated areas. However, the heavy reliance on human expertise often leads to delays, necessitating more efficient and automated solutions. This study developed a machine learning framework to automate doctor response patterns for mental health questions — focusing on anxiety, depression, and stress — using clinically validated data from an Indonesian Online health consultation platform. We performed comprehensive text preprocessing, including duplicate removal, special character elimination, case folding, stopword removal, tokenization, lemmatization, and part-of-speech tagging, and evaluated four feature extraction methods: Word2Vec, Bag-of-Words, N-Gram, and Global Vectors for Word Representation. Five machine learning algorithms — Naïve Bayes, K-Nearest Neighbors, Random Forest, Neural Network, and Gradient Boosting — were tested, along with hybrid models combining Bagging Classifier or Genetic Algorithm. The results showed that Gradient Boosting achieved the highest accuracy (0.842) among standalone models, with high precision (0.858) and F1-score (0.864) for depression prediction, and recall (0.850) and F1-score (0.856) for stress prediction. The Gradient Boosting-Bagging Classifier hybrid matched this accuracy (0.842), while the Gradient Boosting-Genetic Algorithm hybrid showed superior performance for anxiety prediction (precision: 0.888, recall: 0.816). N-Gram and Bag-of-Words methods and the 90:10 and 70:30 train–test splits consistently produced optimal results. This work demonstrates that machine learning can automate mental health responses at scale, with Gradient Boosting balancing accuracy and efficiency. Future research will explore transformer-based models and multilingual validation to improve broader implementation. © 2025 The Authors

关键词： Health consultation Machine learning Online health consultation Pattern recognition Predictive modeling Text analytics

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Adaptive Temporal Frame Interpolation Algorithm for Frame Rate Up-Conversion

Adaptive Temporal Frame Interpolation Algorithm for Frame Ra...

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作者： Vranjes, Denis Rimac-Drlje, Snjezana Vranjes, Mario Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

This article presents a novel adaptive temporal frame interpolation (FI) algorithm for frame rate up-conversion with reduced complexity. According to the adaptability criteria in the proposed algorithm, frames with a low temporal activity are interpolated using frame averaging, which significantly decreases the algorithm complexity. In the case of a high temporal activity, motion compensated FI is used and the high quality of interpolated frames is achieved. Compared to well-known frame rate up-conversion algorithms, the proposed approach is of significantly lower computational complexity, while maintaining the high quality of the interpolated frames. © 2019 IEEE.

关键词： Interpolation

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Intrusion Detection in Networks using Honey Badger Algorithm with improved Adaptive Neuro-Fuzzy Inference System

Intrusion Detection in Networks using Honey Badger Algorithm...

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Communication and information technology (AICCIT), Al-Sadiq International Conference on

作者： Ibrahim Riyadh Alrikabi Murtadha A. Alazzawi Ahmed Al-Khaleefa Buraq T. Sh. Al-Mosawi Hassanain Raheem Kareem Ali Alsalamy Department of Computer Engineering Faculty of Engineering Shahid Chamran University of Ahvaz Ahvaz Iran Department of Computer Techniques Engineering Imam Al-Kadhum College (IKC) Baghdad Iraq Department of Physics Faculty of Education University of Misan Maysan Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna Iraq

An Intrusion detection system IDS is a key component of the security management infrastructure. The goal of IDS is to monitor the processes prevailing in a network and to analyze them for signs of any possible deviations. Because of the large volume of data, the network gets expansion with a false alarm rate of intrusion, and detection accuracy decreased. It is one of the main problems in unknown attacks on network experiences. The basic aim was to develop accuracy also reduce the false alarm rate (FAR). We will design an Adaptive Neuro-Fuzzy based system to identify the intrusion activities within a network effectively. The Honey Badger Algorithm with an improved Adaptive Neuro-Fuzzy Inference System (HBA-IANFIS) is used to address the problems presented. The IANFIS combines an artificial neural network and a fuzzy interference system. To enhance the performance of the IANFIS model, the Honey Badger Algorithm is used to optimize the IANFIS. The intrusion detection results based on the NSL-KDD dataset were better and more efficient than those models because the detection rate was 96.68%, and the FAR result was 0.438%.

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Classification of Retinal Fundus Images for Diabetic Retinopathy Using KNN 1st

Classification of Retinal Fundus Images for Diabetic Retinop...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Nayak, Manjushree Ghugar, Umashankar Dewangan, Bhupesh Kumar Choudhury, Tanupriya Goyal, S.B. Sar, Ayan Department of Computer Science and Engineering Nist University Odisha Berhampur761008 India Chandigarh University Mohali140413 India Department of Computer Science and Engineering OP Jindal University Raigarh496109 India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Faculty of Information Technology City University of Malaysia Selangor Malaysia

ISBN: (纸本)9783031860713

One of the melting public health issues, diabetes mellitus, is experienced by over 463 million persons across the globe as of 2021, and the number of people sustaining this condition is bound to escalate to over 700 million by 2045. Diabetes retinopathy (DR) is the most prevalent eye disease among people having diabetes. The disease affects at least one-third of those with the disease. The rampant diversion of diabetes ranks as the global leading cause of working-age adults’ blindness, with a Streptococcus flocculans number of identified patients with DR at 93 million. These figures are expected to be raised to a larger level in the rate of developing diabetes in Asia countries like China and India per capita. Fundus photography is the most popular way to assess the degree to which diabetic retinopathy has progressed, which is a phenomenon implicated in diabetes where the retina is damaged and causes blindness. CT scanning is a fast, well-tolerated, broadly accessible tool that is non-invasive. Patients having diabetic retinopathy can prove the fact that they are less likely to become blind, provided that they undergo early detection. Along the production of the seven early stages of diabetic retinopathy are almost asymptomatic, the neuronal, and retinal damage and below the detectable microvascular variations take place. Accordingly, people with diabetes must have eye examinations regularly, and immediate treatment and disease diagnosis are vital. Nevertheless, the manual telemammogram reports the effect of a highly skilled practitioner. This is a vital asset in dire situations of underserved people, e.g. in India and Africa, where the workforce is at overstretched limits. It applies Mobile Network V2 (MobileNetV2) and uses a KNN technique for the classification of the SMOTE oversampling integrated. This job has shown the potential of the fundus image date for DL-based cataract classification. © The Author(s), under exclusive license to Springer Nature Switzerland

关键词： Ophthalmology

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Exploring the Effectiveness of Deep Learning in Audio Compression and Restoration

Exploring the Effectiveness of Deep Learning in Audio Compre...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： S. Pushparani K. Sashi Rekha V. M. Sivagami R. Usharani M. Jothi Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Chennai Tamil Nadu Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case.

关键词： Deep learning Training Filtering Bit rate Noise computer architecture Feature extraction

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