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6th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2025

作者： Bhole, Chitra Nemade, Chaitanya Shetty, Chandrakant Patil, Atharv Yadav, Pranav Dept. Computer Engineering K.J. Somaiya Institute of Technology Sion Mumbai India Department K.J. Somaiya Medical College & Research Center Sion Mumbai India

ISBN: (纸本)9798331522667

Recent advances in using artificial intelligence (AI) in medical imaging toward the detection of mastoiditis have shown very promising applications. Therefore, the comprehensive review on the state-of-art approaches related to the application of CT and MRI imaging modalities for automated mastoiditis detection is provided. We review methodologies that go from conventional machine learning approaches such as SVM and Random Forests, to more novel and advanced deep learning architectures, such as U-Net, ResNet, or ViT, to image temporal bones. The review systematically assesses particular techniques: transfer learning using pre-trained models, attention mechanisms focusing feature extraction, and multi-modal fusion approaches combining the data found in CT and MRI. Window level adjustments, skull stripping, adaptive histogram equalization are subjected to critical review regarding the pre-processing methods. Its review ranges over performance metrics across the studies;the current systems achieve sensitivity rates of 85-95% and specificity of 88-97% within the controlled environment. We discuss some of the key challenges such as limited availability of dataset, variance in imaging protocols, and the need for robust clinical validation. Recent evidence from AI-driven systems is promising in the support of the process for clinical diagnosis, especially when using ensemble approaches. There could be several architectures that would work in combination to enhance the robustness and efficacy of clinical diagnosis. © 2025 IEEE.

关键词： computerized tomography

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Simulated deep CT characterization of liver metastases with high-resolution filtered back projection reconstruction

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 257-271页

作者： Christopher Wiedeman Peter Lorraine Ge Wang Richard Do Amber Simpson Jacob Peoples Bruno De Man Department of Electrical and Computer Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA GE Research-Healthcare NiskayunaNY 12309USA Department of Biomedical Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Radiology Memorial Sloan Kettering Cancer CenterNew YorkNY 10065USA Biomedical Computing and Informatics Queen’s UniversityKingstonON K7L 3N6Canada

Early diagnosis and accurate prognosis of colorectal cancer is critical for determining optimal treatment plans and maximizing patient outcomes,especially as the disease progresses into liver *** tomography(CT)is a frontline tool for this task;however,the preservation of predictive radiomic features is highly dependent on the scanning protocol and reconstruction *** hypothesized that image reconstruction with a highfrequency kernel could result in a better characterization of liver metastases features via deep neural *** kernel produces images that appear noisier but preserve more sinogram information.A simulation pipeline was developed to study the effects of imaging parameters on the ability to characterize the features of liver *** pipeline utilizes a fractal approach to generate a diverse population of shapes representing virtual metastases,and then it superimposes them on a realistic CT liver region to perform a virtual CT scan using *** of 10,000 liver metastases were generated,scanned,and reconstructed using either standard or high-frequency *** data were used to train and validate deep neural networks to recover crafted metastases characteristics,such as internal heterogeneity,edge sharpness,and edge fractal *** the absence of noise,models scored,on average,12.2%(α=0.012)and 7.5%(α=0.049)lower squared error for characterizing edge sharpness and fractal dimension,respectively,when using high-frequency reconstructions compared to ***,the differences in performance were statistically insignificant when a typical level of CT noise was simulated in the clinical *** results suggest that high-frequency reconstruction kernels can better preserve information for downstream artificial intelligence-based radiomic characterization,provided that noise is *** work should investigate the informationpreserving kernels in datasets with clinical labels.

关键词： Radiomics Deep learning Computed tomography Colorectal liver metastases Virtual clinical trials Image reconstruction

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Plant Monitoring System Cum Smart Irrigation using Bolt IOT 12

Plant Monitoring System Cum Smart Irrigation using Bolt IOT

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12th International Conference on computer Communication and informatics, ICCCI 2022

作者： Hanumann, Thiruwieddhi Swamy, Nune Veera Venkata Satya Narayana Gowtham, Patapanchula Sumathi, R. Chinnasamy, P. Kalaiarasi, A. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankovil TamilNadu India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Sri Shakthi Institute of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9781665480352

Plant monitoring system is one of the most important tasks in any farming or agriculture-based environment which helps the farmers for irrigation. An automated IOT based water system for monitoring and maintaining the farms in approximate moisture content in soil based on temperature, moisture, humidity and light intensity. They can record the temperature values daily. Farmers can know which farm will suit that particular land based on their sensor values analysis. The main goal of this automation is to aid the farmer yielding more crops, optimal use of water supply (some areas have drought lands, they can use this future very efficiently), decision of crop. Our automated IOT based water supply provides efficient resource utilization and yields high accuracy in temperature with existing Arduino UNO. Overall it reduces economic cost and human intervention. © 2022 IEEE.

关键词： Irrigation

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Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques

Sensitive Content Detection in Social Networks Using Deep Le...

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Big Data, Cloud Computing, Data Science & engineering (BCD)

作者： Isidoros Perikos Computer Engineering and Informatics Department University of Patras Patras Greece Electrical and Computer Engineering Dept. Computer Technology Institute and Press “Diophantus” University of Peloponnese Patras Greece

ISBN: (数字)9798350394191

ISBN: (纸本)9798350394207

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience.

关键词： Deep learning Privacy Sensitivity Accuracy Social networking (online) Decision making Predictive models Transformers User experience Cultural differences

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Psychological Stress Classification Using Extreme Gradient Boosting Algorithm 15

Psychological Stress Classification Using Extreme Gradient B...

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15th International Conference on Information and Communication technology Convergence, ICTC 2024

作者： Sakanti, Maria Mahardini Siniaev, Viacheslav Amaris, Aurelia Luo, Win-Jet Suhartono Kuncoro, C. Bambang Dwi Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung41170 Taiwan Institute of Applied Computer Science Itmo University St. Petersburg19710 Russia Universitas Brawijaya Faculty of Computer Science Department of Informatics Engineering Malang65145 Indonesia National Chin-Yi University of Technology Taichung41170 Taiwan National Chin-Yi University of Technology Department of Refrigeration Air Conditioning and Energy Engineering Taichung41170 Taiwan

ISBN: (纸本)9798350364637

Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which rely on subjective self-reporting and clinical assessments, often suffer from biases and inconsistencies. Artificial intelligence models have been explored to predict stress levels more accurately. This paper investigates the application of Extreme Gradient Boosting in classifying psychological stress using the WESAD dataset, which includes parameters such as acceleration, electrocardiogram, electromyography, electrodermal activity, temperature, and respiration. The dataset was balanced and sampled to create a manageable subset for experimental. Extreme Gradient Boosting was chosen for its efficiency and scalability in handling complex datasets. The model was trained and validated, achieving a 95% accuracy in predicting stress levels. This study highlights the potential of integrating Extreme Gradient Boosting models into wearable devices for real-time stress monitoring. Future work involves optimizing the model to utilize fewer sensors without decreasing accuracy, ensuring it can be integrated into portable/wearable systems using tiny microcontrollers. © 2024 IEEE.

关键词： Electromyography

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An Effective Method for Skin Cancer Detection using Convolutional Kernel Extreme Learning Machine 7

An Effective Method for Skin Cancer Detection using Convolut...

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7th International Conference on Trends in Electronics and informatics, ICOEI 2023

作者： Sarkar, Anuradha Badrinath, N. Kapse, Poonam Bhalerao, Diksha Madhav Singh, Kamlesh Sagar, K.V.Daya Neville Wadia Institute of Management Studies and Research Maharashtra Pune India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Kharghar Department of CSE Maharashtra Navi Mumbai India Department of Engineering and Technology Maharashtra Navi Mumbai India Graphie Era Hill University Dehradun Campus Department of Computer Science and Engineering Uttarakhand Dehradun India Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Electronics and Computer Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350397284

Skin cancer is the most common type of cancer. Every year, it has a significant effect on the lives of tens of millions of individuals. Approximately three million people are told they have it every year for the United States only. Survival rates are much lower at that point. In proportion to the severity of the condition. Yet, early skin cancer screening is a difficult and expensive process. The four main phases in skin cancer detection are preprocessing, segmentation, feature extraction, and classification. Because it improves the odds of successfully treating cancer, early detection is vital. In this study, Otsu's thresholding is utilized to distinguish the foreground from the background after the Gaussian filter has been applied during the pre-processing stage. The histogram equalization phase of the pre-processing stage uses the input from the median filter to remove noise. Compatibility of Grey-Level Signals, normalization for symmetry extraction of skin lesion characteristics are done by using GLCM matrix. Afterwards, the proposed model is trained via CKELM. When compared to other state-of-the-art models like CNN and KELM, the proposed model achieves a higher accuracy of around 98%. © 2023 IEEE.

关键词： Median filters

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A robust subband adaptive filter algorithm for sparse and block-sparse systems identification

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Journal of Systems engineering and Electronics 2021年第2期32卷 487-497页

作者： ZAHRA Habibi HADI Zayyani MOHAMMAD Shams Esfand Abadi Research Institute for Information and Communications Technologies Academic Center for EducationCulture and ResearchTehran ***Iran Department of Electrical and Computer Engineering Qom University of TechnologyQom 371951519Iran Faculty of Electrical Engineering Shahid Rajaee Teacher Training UniversityTehran ***Iran

This paper presents a new subband adaptive filter(SAF)algorithm for system identification scenario under impulsive interference,named generalized continuous mixed p-norm SAF(GCMPN-SAF)*** proposed algorithm uses a GCMPN cost function to combat the impul-sive *** further accelerate the convergence rate in the sparse and the block-sparse system identification processes,the proportionate versions of the proposed algorithm,the L0-norm GCMPN-SAF(L0-GCMPN-SAF)and the block-sparse GCMPN-SAF(BSGCMPN-SAF)algorithms are also ***,the convergence analysis of the proposed algorithm is *** results show that the proposed algorithms have a better performance than some other state-of-the-art algorithms in the literature with respect to the convergence rate and the tracking capability.

关键词： subband adaptive filter(SAF) generalized continuous mixed p-norm(GCMPN) sparse system block-sparse system impulsive interference

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The Novel DeepBoost Multi Output Regressor for Household Finance Intelligence 5

The Novel DeepBoost Multi Output Regressor for Household Fin...

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5th International Conference on Advanced research in Computing, ICARC 2025

作者： Suwandaratna, S.H.S.V. Priyankara, G.O. Institute of Technology Department of Computer Science and Engineering Informatics No 10 Trelawney PI Colombo00400 Sri Lanka

ISBN: (纸本)9798331530983

This paper forecasts the microeconomic level household expenditures using a novel hybrid deep learning approach. In terms of research significance, household finance control has a major influence on the finance system within the economy. Accurate forecasting of household finances assists in maintaining positive financial behavior among individuals and the economy. The DeepBoost multi-output regressor proposed in this paper is based on the 1D CNN-ANN and the XGBoost. The proposed model in this paper is compared with the R2, MSE, and MAE since it's a regression problem. The experimental results reveal that the proposed DeepBoost multi-output regressor has the best application in forecasting the multiple expenditures of households by outperforming the ANN, 1D CNN-ANN, and Random Forest Regressor models. The proposed DeepBoost multi-output regressor evaluated the housing, food, transportation, healthcare, other necessities, childcare, and tax expenditures that had 0.94, 0.98, 0.83, 0.94, 0.97, 0.97, and 0.99 values for the R2, 9037.71, 2692.12, 9788, 15077.33, 1373.93, 13629.36, and 1904.52 values for the MSE, and 66.07, 34.05, 73.17, 87.05, 26.25, 78.74, and 29.47 MAE values than the ANN, RFR, and 1D CNN-ANN models. © 2025 IEEE.

关键词： Taxation

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Performance Creativity Enhancement Method Based on Emotional Analysis and Concentration

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第3期8卷 1019-1048页

作者： Liu, Chang Zhang, Longfei Huang, Tianyu Wu, Yufeng Zhang, Fuquan Lin, Qi Department of Computer Science and Technology Beijing Institute of Technology 100081 China Beijing Key Laboratory of Digital Performance and Simulation Technology 100081 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Informatics King’s College London LondonWC2R 2LS United Kingdom

Intelligent performance creativity is a new research direction of the intersection of technology and art. At present, the cutting-edge technologies such as computer simulation, emotional computing and machine learning have been applied to the evaluation and enhancement of intelligent performance creativity. The application of these technologies has greatly increased the diversity of performance forms and the complexity of performance content. For the traditional stage performance, due to the lack of timely feedback from the audience, it is difficult for the director to enhance the creativity effec-tively. In order to solve this problem, this paper proposes concentration level enhancement method (CLEM). The CLEM extracts and analyzes the physiological and emotional characteristics of the audience by collecting the multimodal physiological signal data of the audience. Through the analysis of the features, the concentration information of the audience is obtained and the concentration level (CL) is defined. Based on the physiological and emotional induction mechanism, a performance creativity enhancement strategy is proposed, which enhances the performance creativity in two aspects: " Visual-based" and " Content-based". Through the concentration level enhancement method, compare the audience’s performance attention changes before and after the performance creativity enhancement, select the effective strategies to enhance the performance creativity, and provide the director with quantifiable performance creativity enhancement feedback results. The experimental results show that the CLEM proposed in this paper can effectively induce significant changes in the audience’s emotion, and the selected creative enhancement strategy can significantly improve the audience’s concentration and achieve the purpose of enhancing the creativity of the performance. © 2023, Journal of Network Intelligence.

关键词： Physiology

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Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

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computers, Materials & Continua 2022年第2期70卷 2761-2775页

作者： Abdul Hanan Ashraf Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood Muhammad Attique Mohamed Habib Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyIslamabad44000Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Department of Computer Science HITEC UniversityTaxila47080Pakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Faculty of Engineering Port Said UniversityPort Fuad CityEgypt

In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting *** is why an automated weapon detection system is *** automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good ***,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection *** models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance *** research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key *** proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur *** proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 *** results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are *** reached 0.010 s per frame compared to the 0.17 s of the Faster *** is promising to be used in the field of security and weapon detection.

关键词： Video surveillance weapon detection you only look once convolutional neural networks

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