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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Pareddy, Sudha V. Virupakshappa PDA College of Engineering Department of Computer Science & Engineering Kalaburagi India Sharnbasva University Department of Computer Science & Engineering Kalaburagi India

ISBN: (纸本)9798350371802

The early and accurate diagnosis of melanoma, a potentially harmful skin cancer, is essential in enhancing the survival of patients. This paper describes a new method for detecting melanoma called Panoptic Region Slicing Segmentation (PRS2), which employs an AlexNet-based optimized convolution neural network (PRS2-OCNN). The proposed system integrates several advanced techniques to improve the efficiency and accuracy of melanoma detection. The first step is to preprocess the dermoscopic image with a 2D Fusion Filter to improve its quality and prepare it for further analysis. The Panoptic Region Slicing Segmentation (PRS2) method is then used to emphasize the boundary regions, allowing for more precise localization of melanoma-affected areas. We use Threshold Histogram Evaluation (THE) to assess color variations within segmented regions, which effectively characterizes melanoma-specific color patterns. The features were extracted using Spread Spectral Menzies' Feature Selection (SSMFS), which reduced dimensionality and increased the efficiency of the subsequent analysis. The optimized convolution neural network, derived from the influential AlexNet architecture, serves as the foundation of our proposed approach. By fine-tuning the AlexNet-OCNN on the reduced feature set, we can improve its ability to accurately classify melanoma lesions based on their risk level. The PRS2-OCNN categorizes melanoma classes based on their risk severity, allowing dermatologists to make informed decisions about timely and appropriate treatment. A broad and diversified dataset of dermoscopic images was used in the experimental evaluations. By thoroughly analyzing the melanoma-affected regions, the proposed system performed better than existing methods in terms of detection accuracy. © 2024 IEEE.

关键词： Diagnosis

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High-rate metal-free MXene microsupercapacitors on paper substrates

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Carbon Energy 2024年第5期6卷 94-104页

作者： Han Xue Po‐Han Huang Lee‐Lun Lai Yingchun Su Axel Strömberg Gaolong Cao Yuzhu Fan Sergiy Khartsev Mats Göthelid Yan‐Ting Sun Jonas Weissenrieder Kristinn BGylfason Frank Niklaus Jiantong Li School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSweden School of Engineering Sciences KTH Royal Institute of TechnologyStockholmSweden

MXene is a promising energy storage material for miniaturized microbatteries and microsupercapacitors(MSCs).Despite its superior electrochemical performance,only a few studies have reported MXene-based ultrahigh-rate(>1000 mV s^(−1))on-paper MSCs,mainly due to the reduced electrical conductance of MXene films deposited on ***,ultrahigh-rate metal-free on-paper MSCs based on heterogeneous MXene/poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)(PEDOT:PSS)-stack electrodes are fabricated through the combination of direct ink writing and femtosecond laser *** a footprint area of only 20 mm^(2),the on-paper MSCs exhibit excellent high-rate capacitive behavior with an areal capacitance of 5.7 mF cm^(−2)and long cycle life(>95%capacitance retention after 10,000 cycles)at a high scan rate of 1000 mV s^(−1),outperforming most of the present on-paper ***,the heterogeneous MXene/PEDOT:PSS electrodes can interconnect individual MSCs into metal-free on-paper MSC arrays,which can also be simultaneously charged/discharged at 1000 mV s^(−1),showing scalable capacitive *** heterogeneous MXene/PEDOT:PSS stacks are a promising electrode structure for on-paper MSCs to serve as ultrafast miniaturized energy storage components for emerging paper electronics.

关键词： direct ink writing femtosecond laser scribing MXene on-paper microsupercapacitors PEDOT:PSS ultrahigh rate capability

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A Base Station Deployment Algorithm for Wireless Positioning Considering Dynamic Obstacles

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computers, Materials & Continua 2025年第3期82卷 4573-4591页

作者： Aiguo Li Yunfei Jia Department of Network Engineering Xi’an University of Science and TechnologyXi’an710600China College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710600China

In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of *** studies focus on optimizing base station deployment under the assumption of static obstacles,aiming to maximize the perception coverage of wireless RF(Radio Frequency)signals and reduce positioning blind ***,in practical security systems,obstacles are subject to change,necessitating the consideration of base station deployment in dynamic ***,research in this area still needs to be *** paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm(DIE-BDA)to address this *** algorithm considers the dynamic alterations in obstacle locations within the designated *** determines the requisite number of base stations,the requisite time,and the area’s practical and overall signal coverage *** experimental results demonstrate that the algorithm can calculate the deployment strategy in 0.12 s following a change in obstacle *** results show that the algorithm in this paper requires 0.12 s to compute the deployment strategy after the positions of obstacles *** 13 base stations,it achieves an effective coverage rate of 93.5%and an overall coverage rate of 97.75%.The algorithm can rapidly compute a revised deployment strategy in response to changes in obstacle positions within security systems,thereby ensuring the efficacy of signal coverage.

关键词： Wireless positioning base station deployment dynamic obstacles dynamic obstacle wireless positioning

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Investigating mobility patterns through crowd-sourced activity data

Advances in Transportation Studies

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Advances in Transportation Studies 2025年 65卷 253-270页

作者： Castiglione, M. Cipriani, E. Gemma, A. Nigro, M. Department of Civil Computer Science Aeronautical Technologies Engineering Roma Tre University Rome00146 Italy

Investigating trip purposes represents an important phase of travel demand modeling which allows to correctly infer mobility patterns and to better understand travel behavior. Until now, researchers collected information on the motivation for carrying out a trip mainly through travel surveys. However, traditional methods of acquiring this type of information are challenging and expensive to implement;therefore, they are typically performed infrequently and with low sampling rates. These two occurrences do not always allow for adequate representation of the heterogeneity of trip purposes. This paper aims to investigate trip purposes through the joint processing of GPS-based data, such as Floating Car Data (FCD), and aggregated activity data available through open-source platforms, such as Google Popular Times (GPT), which provide information on the daily distribution of visits in a certain activity venue as well as the average visit duration based on aggregated data obtained from users who share their mobile phones geotraces. Through the application of clustering techniques on a FCD dataset containing trips carried out between September and November 2020 in the EUR district of Rome, Italy, we classify the trips as Home-Work trips and Not Home-Work trips, obtaining a total of 96 Origin-Destination matrices (one for every 15 minute time interval). Not Home-Work trips are further examined, exploiting 6 patterns obtained through the clustering of GPT activity data, and classified according to the arrival time at destination and the duration of their stopover. The obtained Origin-Destination matrices have been compared to values found in literature in terms of spatial and temporal flexibility to validate the results. © 2025, Aracne Editrice. All rights reserved.

关键词： Spatio-temporal data

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Supervised single-channel dual domains speech enhancement technique using bidirectional long short-term memory

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Multimedia Tools and Applications 2025年第5期84卷 2779-2803页

作者： Hosen, Md. Shakhawat Basir, Samiul Khan, Md. Farukuzzaman Asaduzzaman, A.O.M. Islam, Md. Mojahidul Islam, Md Shohidul Department of Computer Science and Engineering Islamic University Kushtia7003 Bangladesh Dhaka Bangladesh Hong Kong

This article introduces a brand new supervised method to make speech sound better and clearer by getting rid of background noise and mess-ups. It brings together two kinds of signal processes: the dual-tree complex wavelet transform (DTCWT) along with the short-time Fourier transform (STFT), in addition to a bidirectional long short-term memory (BLSTM) network. By taking the features from both time-frequency and wavelet domains, this way deals with issues of the discrete wavelet transform (DWT), like dealing with big changes and getting the direction right. The DTCWT makes subband signals that then change into a complex spectrogram using STFT. The size of this spectrogram is put into a bunch of layers in the BLSTM network, which finds speech bits well by knowing what came before and what’s coming next in data sequences. Soft mask parts are used to tweak this procedure. Matrix multiplication is implemented between the noisy complex spectrogram and the predicted mask of the model to produce the enhanced matrix. The final speech parts are made using inverse STFT and inverse DTCWT with the original parameters. How well this way works is tested against different kinds of noise, like stationary, quasi-stationary, and non-stationary, using both the IEEE Corpus and Aurora-2 databases. Results prove it gets a PESQ score of 3.34 and an STOI score of 0.918 at 0 signal-to-noise ratio (SNR) and also performs better for others. Other ways to gauge how good it is, like HASQI and HASPI, also do better than other methods, showing off better voice quality and understandability in different noisy spots. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

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Automated breast nuclei feature extraction for segmentation in histopathology images using Deep-CNN-based gaussian mixture model and color optimization technique

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Multimedia Tools and Applications 2025年 1-27页

作者： Murmu, Anita Kumar, Piyush Computer Science and Engineering National Institute of Technology Patna Ashok Rajpath Bihar Patna800005 India

Hematoxylin and eosin (H&E) staining are the key sources for identifying breast cancer patterns with different colors and shapes of nuclei cells for segmenting histopathology nucleus images. In nucleus cells, the color categorization of hematoxylin is stained blue, whereas cytoplasmic cells of eosin are stained pink. This color dissimilarity in the nucleus cells indicates different structures of the cellular and cancer-specific cells, which need to detect on time is a challenge for accurate diagnosis. To overcome this issue, a Deep Convolution Neural Network (Deep-CNN) based Unet framework with Transfer Learning (TL) is proposed for segmenting histopathology breast cancer images. Specifically, the Deep-CNN framework has been used to extract features automatically from input images, and select the features by using the TL technique for minimizing the target tasks to train it faster. Furthermore, a deep convolutional-based Gaussian Mixture Model (GMM) is used to normalize the shapes and merge the nucleus cell pixels for color optimization. The proposed method has been tested on Kaggle’s 2018 Data science Blow histopathology breast image dataset. The experimental results of the proposed scheme have been evaluated by performance evaluation matrices, namely accuracy, Jaccard coefficient, Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and F1-score. The proposed approach outperforms compared with different state-of-the-art models on the same dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolution

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Global-local term fusion for optimised community Q&A topic modelling

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International Journal of Innovative Computing and Applications 2024年第1期15卷 50-69页

作者： Prabha, Sneh Sardana, Neetu Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Community question and answer (Q&A) websites have become invaluable information and knowledge-sharing sources. Effective topic modelling on these platforms is crucial for organising and navigating the vast amount of user-generated content. To address these challenges, we propose a novel global-local term fusion with optimised community (GLOCOM) Q&A topic modelling approach that leverages both local and global term importance to enhance topic modelling on community Q&A websites. GLOCOM combines term frequency-inverse document frequency for local importance and entropy for global importance. Further, we employ fuzzy clustering to enhance the representation of multifaceted topics. Furthermore, clustering results are optimised using a genetic algorithm (GA) to refine cluster assignments and centroids. We compared the proposed model with baseline models LDA and FLSA. GLOCOM has performed consistently well for all topic numbers. It has shown an improvement of 8.86% in silhouette score as compared to LDA and excelled for datasets with size > 3 MB. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Inverse problems

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Predicting Depressive Behavior with Monitoring Activity Data Using Machine Learning and Feature Selection Approaches 6

Predicting Depressive Behavior with Monitoring Activity Data...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Sarder, Md. Hosain Raihan, M. Debnath, Anjan Islam, Md. Shymon Samrat, Nadim Efty, Riyana Islam Hasan, Md. Mahedi Fahimuzzaman, Md. North Western University Computer Science and Engineering Khulna9100 Bangladesh Khulna University of Engineering & Technology Computer Science and Engineering Khulna9203 Bangladesh Khulna University Computer Science and Engineering Khulna9208 Bangladesh

ISBN: (纸本)9798350385779

Depression is a common mental health disorder. It significantly affects millions of people's daily lives all around the world. Despite the high incidence of depression and limited treatment options, many people are hesitant to seek professional help. Researchers are now exploring the use of wearable technology to collect personalized physiological data, which has recently been applied to provide healthcare services. Depression can be recognized with the help of machine learning using motor activity data collected by the ActiGraph wristband. Large data samples and higher effectiveness are necessary for developing a reliable and robust machine-learning model. However such quantitative and relevant clinical data are not openly available. This study used the depresjon (depression in Norwegian) dataset consisting of time-series data on the activity of only 23 depressed and 32 healthy individuals. However, a data augmentation technique was used that increased the number of samples from 693 to 5500. Among these algorithms, XGBoost obtained an impressive accuracy of 98.8% in 10-fold cross-validation, which met the requirements to accurately detect depression. In addition, this investigation highlighted different hidden patterns included in the time series of the two classes. Furthermore, this study utilized Explainable AI approaches, such as SHAP. These approaches further improve model transparency and interpretability. The explanation highlighted the significance of the statistical feature Median activity among features like Mean, Zero crossing rate, Standard deviation, etc. that significantly influence the model's decision-making process. © 2024 IEEE.

关键词： Wearable technology

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Analysis of facial emotion expression in eating occasions using deep learning

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Multimedia Tools and Applications 2023年第20期82卷 31659-31671页

作者： Yildirim, Elif Akbulut, Fatma Patlar Catal, Cagatay Department of Computer Engineering Istanbul Kültür University Istanbul Turkey Department of Computer Science and Engineering Qatar University Doha Qatar

Eating is experienced as an emotional social activity in any culture. There are factors that influence the emotions felt during food consumption. The emotion felt while eating has a significant impact on our lives and affects different health conditions such as obesity. In addition, investigating the emotion during food consumption is considered a multidisciplinary problem ranging from neuroscience to anatomy. In this study, we focus on evaluating the emotional experience of different participants during eating activities and aim to analyze them automatically using deep learning models. We propose a facial expression-based prediction model to eliminate user bias in questionnaire-based assessment systems and to minimize false entries to the system. We measured the neural, behavioral, and physical manifestations of emotions with a mobile app and recognize emotional experiences from facial expressions. In this research, we used three different situations to test whether there could be any factor other than the food that could affect a person’s mood. We asked users to watch videos, listen to music or do nothing while eating. This way we found out that not only food but also external factors play a role in emotional change. We employed three Convolutional Neural Network (CNN) architectures, fine-tuned VGG16, and Deepface to recognize emotional responses during eating. The experimental results demonstrated that the fine-tuned VGG16 provides remarkable results with an overall accuracy of 77.68% for recognizing the four emotions. This system is an alternative to today’s survey-based restaurant and food evaluation systems. © 2023, The Author(s).

关键词： Emotion Recognition

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AI-Enhanced Precision Crop Rotation Management for Sustainable Agriculture

AI-Enhanced Precision Crop Rotation Management for Sustainab...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Hafiyya, R.M. Jemsheer Ahmed, P. Nabhan, P.A. Melethil, Safvan Mohammed, Nadhi Muhammed Murshid, A.P. Mea Engineering College Computer Science & Engineering India

ISBN: (纸本)9798350394399

This paper presents a novel approach to crop rotation management that integrates cutting-edge artificial intelligence (AI) techniques to improve and modernise agricultural processes. The study seeks to revolutionise traditional crop rotation tactics by utilising state-of-the-art machine learning techniques, namely supervised learning approaches. Through the use of real-time weather forecasts and historical crop performance data, AI-powered technology provides farmers with tailored advice for maximising crop rotations, ultimately leading to better agricultural results. The methodical methodology includes planning the project, gathering data, creating AI models, and deploying the system. Establishing precise project objectives, collecting and analysing soil data with care, creating AI-driven recommendations, and making sure that monitoring and improvement are ongoing are all crucial tasks. At every level, the system uses a range of instruments and software to guarantee a methodical, data-driven approach that produces improved crop rotation techniques that are advantageous to the environment and the agricultural industry. This study adds to the rapidly developing field of artificial intelligence in agriculture by offering a workable and creative answer to the problems farmers face when managing crop rotation. The results underline how AI may be used to improve farming techniques, increase crop yields, and promote sustainable farming, which makes it an important contribution to the field of precision agriculture research. © 2024 IEEE.

关键词： Crop rotation

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