检索结果-内蒙古大学图书馆

Multi-feature Fusion Deep Network for Skin Disease Diagnosis

Multimedia Tools and Applications 2025年第1期84卷 419-444页

作者： Gairola, Ajay Krishan Kumar, Vidit Sahoo, Ashok Kumar Diwakar, Manoj Singh, Prabhishek Garg, Deepak Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era deemed to be university Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science and Artificial Intelligence SR University Warangal Telangana Warangal506371 India

The skin acts as an important barrier between the body and the external environment, playing a vital role as an organ. The application of deep learning in the medical field to solve various health problems has generated increasing interest. The objective of this research is to create a new model using a deep network with multi-feature fusion that enables accurate identification of various skin diseases. This paper proposes a fully fused network (FFN), which includes an improved single block (ISB) and an improved fusion block (IFB) to achieve optimal performance. To implement this, a convolutional neural network-based model for multi-class recognition of skin images has been developed, in which ISB is used to segment diseases in skin images. The IFB module is designed to enhance the effectiveness of the fused network. The model is built and evaluated using our proprietary dataset (Skin_disease_v1) along with publicly available datasets, namely ISIC2016, ISIC2017, and HAM10000. The highest accuracy achieved in experiments was 86% for ISB, 90% for IFB, and 92% for FFN using HAM10000 (for ResNet101V2), HAM10000 (for Resnet50 + ResNet101V2), and HAM10000 (for Resnet50 + ResNet101V2). This shows an accuracy improvement of 9.2%, 13.2%, and 15.2% compared to the state-of-the-art ISB, IFB, and FFN approaches, respectively. Our network outperforms existing networks in terms of performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

SMILES2DTA: a CNN-based approach for identifying drug candidates and predicting drug-target binding affinity

引用

Neural Computing and Applications 2024年第4期37卷 2891-2910页

作者： Mukit, Hasanul Hossain, Sayeed Farabi, Mirza Milan Chowdhury, Mehrab Zaman Pritom, Ahmed Iqbal Rana, Humayan Kabir Department of Computer Science and Engineering Green University of Bangladesh Narayanganj-1461 Dhaka Bangladesh Department of Computer Science The University of Alabama at Birmingham BirminghamAL United States Department of Computer Science and Engineering Islamic University of Technology Gazipur Bangladesh

Computational approaches can speed up the drug discovery process by predicting drug-target affinity, otherwise it is time-consuming. In this study, we developed a convolutional neural network (CNN)-based model named SMILES2DTA (Simplified Molecular Input Line Entry System to Drug-Target Affinity) for predicting the binding affinity between a drug and a target protein. The model utilizes CNNs to process sequences of both drug SMILES and target proteins. SMILES2DTA generates multiple sequences from a single drug SMILES sequence, validates them based on Lipinski’s rule of five, and assesses their binding affinity against a target protein sequence. We evaluated our model using publicly available datasets and compared its performance to state-of-the-art methods. The results showed that SMILES2DTA outperformed the existing methods and demonstrated improved accuracy, mean squared error, and area under the precision-recall curve. SMILES2DTA has the potential to speed up the drug discovery process by reducing the time and cost complexity of identifying effective drugs. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Drug discovery

来源：评论

学校读者我要写书评

暂无评论

Drawing a parallel between the trend of confirmed COVID-19 deaths in the winters of 2022/2023 and 2023/2024 in Italy, with a prediction

引用

Mathematical Biosciences and engineering 2024年第3期21卷 3742-3754页

作者： Roccetti, Marco Department of Computer Science and Engineering University of Bologna Bologna Italy

We studied the weekly number and the growth/decline rates of COVID-19 deaths of the period from October 31, 2022, to February 9, 2023, in Italy. We found that the COVID-19 winter wave reached its peak during the three holiday weeks from December 16, 2022, to January 5, 2023, and it was definitely trending downward, returning to the same number of deaths as the end of October 2022, in the first week February 2023. During this period of 15 weeks, that wave caused a number of deaths as large as 8,526. Its average growth rate was +7.89% deaths per week (10 weeks), while the average weekly decline rate was -15.85% (5 weeks). At the time of writing of this paper, Italy has been experiencing a new COVID-19 wave, with the latest 7 weekly bulletins (October 26, 2023 – December 13, 2023) showing that deaths have climbed from 148 to 322. The weekly growth rate had risen by +14.08% deaths, on average. Hypothesizing that this 2023/2024 wave will have a total duration similar to that of 2022/2023, with comparable extensions of both the growth period and the decline period and similar growth/decline rates, we predict that the number of COVID-19 deaths of the period from the end of October 2023 to the beginning of February 2024 should be less than 4100. A preliminary assessment of this forecast, based on 11 of the 15 weeks of the period, has already confirmed the accuracy of this approach. ©2024 the Author(s), licensee AIMS Press.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

3D brain image based tumor classification using ensemble of reinforcement transfer-based belief neural networks

引用

Multimedia Tools and Applications 2025年第14期84卷 14001-14028页

作者： Arora, Shraddha Lamba, Monika Department of Computer Science and Engineering The NorthCap University Haryana Gurugram India

The procedure of segmenting a brain tumour is an essential step in the field of medical image processing. In order to improve the efficacy of disease treatment choices and the likelihood of patient survival, the timely detection of a brain tumour is not only highly essential but a vital factor. For the purpose of cancer diagnosis, manually segmenting brain tumours from the large number of MRI images that are generated in clinical practice is a hard and time-consuming process. Automatic brain tumour image segmentation is a technique that absolutely has to exist. Using deep learning and machine learning architectures for segmentation and feature extraction, this research proposes a novel approach to the detection of brain tumours. In this case, the input consists of a collection of 3-D brain slice pictures that have been smoothed and with noise eliminated. After that, the picture that has been processed is segmented by using a convolutional radial function architecture that centres on contours. The segmented image features retrieval has been accomplished by the use of an ensemble of reinforcement transfer-based belief neural networks. The results of the experimental study have been completed with regard to precision, recall, accuracy, F-1 score, area under the curve, dice coefficient, and root mean square error. The strategy that was recommended was successful in achieving 97.57% accuracy, 97.15% precision, 89.37% recall, 83.56% F-1 score, 69.59% area under the curve, 63.83% dice coefficient, and 55.56% root mean square error. The sagittal, coronal, and axial planes of the pictures were shown in order to acquire the specific position of the tumour as well as its characteristics, which included its height, breadth, and depth. When it comes to the prediction of tumours, the findings of the assessment of semantic segmentation based on deep learning networks are highly positive. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Spri

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Enhanced prediction of agricultural CO2 emission using ensemble machine learning-based imputation approach

引用

Neural Computing and Applications 2024年第36期36卷 22867-22887页

作者： Thendral, M. Sathya Abinaya, S. Devi, M. K. Kavitha Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

The agricultural sector contributes significantly to greenhouse gas emissions, which cause global warming and climate change. Numerous mathematical models have been developed to predict the greenhouse gas emissions from agriculture. However, the database utilized for prediction has thousands of missing values and imbalanced data records due to various factors, such as environmental disasters, sensor failure, maintenance issues, and budgetary constraints. Many researchers have either completely discarded records with missing values or have only partially addressed the issue, leading to less precise predictions. This study proposes a machine learning-based ensemble approach that uses multiple imputation by chained equations (MICE), K-nearest neighbors (KNN), and MissForest techniques to impute the missing values by reducing uncertainties and improves robustness in predictions. Furthermore, Synthetic Minority Oversampling Technique (SMOTE R) addresses data imbalance in CO2 emissions prediction by generating synthetic data to balance the target variable's distribution in the regression problem. Thereby the proposed SMOTE R and ensemble imputation approach aims to improve prediction accuracy and reliability by tackling data completeness and distribution issues simultaneously. The experiments are conducted on the GRACEnet database shows that the proposed approach outperforms the existing work in measure of R2, MAE, and RMSE metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Greenhouse gas emissions

来源：评论

学校读者我要写书评

暂无评论

Privacy-preserving intrusion detection with federated learning: enhancing privacy and efficiency in IoT networks

引用

International Journal of Machine Learning and Cybernetics 2025年 1-19页

作者： Sharma, Hemant Yadav, Gyan Singh Department of Computer Science and Engineering IIIT Kota Rajasthan Kota India

Intrusion detection systems (IDS) are crucial in the identification of unauthorized activities on a digital network, enabling cybersecurity measures to initiate prevention protocols to protect the security of their networks and data. With Internet of Things (IoT) as a ubiquitous phenomenon, IDS has become increasingly important owing to the massive growth of connected devices. Machine learning algorithms can detect anomalies in large datasets, but centralized methods pose security risks by gathering all data in one place. Federated learning (FL) offers a safer alternative, allowing multiple clients to collaborate on a model without sharing data, reducing the risk of data leaks and enhancing privacy. In this paper, we propose a FL-based IDS. Herein, we have applied the FL approach to a number of clients that are grouped in sets where the number of clients in a set varies from 2 to 10. After that, we applied feature ranking via a random forest approach, wherein the features are ranked based on their importance to find the optimal and reduced dataset for reducing the inference time. Further, three models for each of the clients were trained using the refined dataset obtained. Next, the central server aggregates the data from the clients using three methods. We have updated the dataset with two types of scenarios, one with two classes (Benign and Attacks) and the second with a multi-class (Benign, Dodag, Flooding, Rank, Blackhole) for identifying the attack. The experimental results establish that the proposed methodology achieves superior performance as compared to similar existing methodologies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

引用

CAAI Transactions on Intelligence Technology 2024年第6期9卷 1412-1434页

作者： Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingJiangsuChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Software Northwestern Polytechnical UniversityXianShaanxiChina Department of Computer Science School of PhysicsEngineering&Computer ScienceUniversity of HertfordshireHatfieldUK Department of AI and Software Gachon University Seongnam-siSeongnamSouth Korea

Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement engineering(RE)activities to solve the problems that occur in RE *** authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–*** authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this ***-five research studies were selected based on our exclusion and inclusion *** results show that the scientific community used 57 *** those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random *** results show that researchers used these algorithms in eight major RE *** activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural *** selected research studies used 32 private and 41 public data *** most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in Software engineering,and iTrust Electronic Health Care System.

关键词： data sources machine learning requirement engineering supervised learning algorithms

来源：评论

学校读者我要写书评

暂无评论

A TSN-Like Slot-Based Scheduler for Improved Wireless Quality and Platoon Formation in Smart Factories

IEEE Open Journal of the Communications Society

引用

IEEE Open Journal of the Communications Society 2025年 6卷 3003-3013页

作者： Feraudo, Angelo Garbugli, Andrea Bellavista, Paolo University of Bologna Department of Computer Science and Engineering Bologna40126 Italy

The platooning of mobile robots, facilitated by Device-to-Device (D2D) communications, has become central in Industry 4.0, enhancing material transport, reducing energy consumption, and improving safety in smart factories. However, as the number of participating robots increases, maintaining effective communication and coordination becomes increasingly challenging. Shared spectrum usage in D2D networks can lead to communication collisions, particularly in highly dynamic scenarios, posing significant challenges in industrial environments. This paper introduces a novel slot-based solution inspired by Time Sensitive Networking (TSN) to address these challenges, ensuring reliable, low-latency communication while optimizing resource allocation and communication efficiency in platooning systems. To achieve this, we propose a controller named TSNCtl, operating at the application layer of the network stack. TSNCtl leverages a finite state machine (FSM) to manage platoon formation and employs slot-based scheduling for efficient message dissemination. Using the OMNeT++ simulation framework and INET library, we demonstrate the effectiveness of TSNCtl in reducing packet collisions across a variety of scenarios. Our experimental results highlight a significant improvement over the traditional CSMA/CA baseline employed in Wi-Fi-based protocols such as IEEE 802.11p. For instance, TSNCtl achieves near-zero packet collisions with appropriate configurations, even in highly dynamic environments. © 2020 IEEE.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

Emotion mining for early suicidal threat detection on both social media and suicide notes using context dynamic masking-based transformer with deep learning

引用

Multimedia Tools and Applications 2025年第13期84卷 11729-11752页

作者： Kodati, Dheeraj Tene, Ramakrishnudu Department of Computer Science and Engineering National Institute of Technology Warangal India

Suicide represents a poignant societal issue deeply entwined with mental well-being. While existing research primarily focuses on identifying suicide-related texts, there is a gap in the advanced detection of mental health states leading to suicidal tendencies. The purpose is to recognize the emotions influencing individuals’ mental states, potentially leading to suicidal behavior. This study seeks to identify specific emotions like anger, depression, anxiety, guilt, fear, stress, and sadness within suicide-related texts sourced from both social media and suicide notes. We introduce a novel suicide severity assessment method emphasizing emotion detection across categories like suicidal ideation, suicide planning, attempted, committed, suicide notes, describing, and cannot dare. By identifying high-intensity negative emotions, our approach enables early detection of potential suicide threats. Existing studies often struggle to capture nuances and semantic relationships, particularly in suicide-related contexts. To address this, we propose a context dynamic masking based on bidirectional long short-term memory along with a multi-head self-attention and convolutional neural network (CoDyn-BMHSA-CNN) model, capturing bidirectional contexts and maintaining variable-length sequences. Our model achieves accuracies of 98.2% and 97.4%, with F1 scores of 95.5% and 93.8% for social media and suicide note datasets, respectively. We perform ablation tests to pinpoint the key components of our proposed models. Furthermore, a thorough comparative analysis evaluates the model’s performance across various contexts and platforms. Experimental results illustrate the superior effectiveness of our model in identifying emotions from text sequences compared to state-of-the-art approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Mental Health Sentiment Analysis With BERT-Fuse: A Hybrid Deep Learning Model

引用

IEEE Access 2025年 13卷 85428-85446页

作者： Hossain, Md. Mithun Sanjara Hossain, Md. Shakil Chaki, Sudipto Rahman, Md. Saifur Ali, A. B. M. Shawkat Department of Computer Science and Engineering Dhaka1216 Bangladesh

Mental health disorders profoundly affect intellectual ability, emotional resilience, and social stability, leading to lasting effects on both individuals and society. Increasing global concern over mental health calls for innovative solutions, with sentiment analysis emerging as a promising approach for early diagnosis and intervention. Traditional methods are limited by the complexity of mental health-related texts, which contain specialized terminology and domain-specific nuances. To address these challenges, we propose BERT-Fuse, a hybrid deep learning model combining traditional feature extraction techniques (TF-IDF and Bag-of-Words) with advanced neural architectures, including Bidirectional Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Transformer blocks, and Convolutional Neural Networks (CNN). We evaluated three word embedding techniques-BERT, GloVe, and FastText within BERT-Fuse and found that BERT embeddings achieve the highest performance in terms of accuracy, precision, recall, and F1-score. The model was trained on a balanced dataset of text samples representing seven mental health conditions. An ablation study highlights the contributions of key model components to its performance. BERT-Fuse shows promise as a scalable, high-precision system for mental health detection with an impressive average test accuracy of 97.05%. Our model bridges the gap between deep learning and psychological text analysis, paving the way for future mental health assessment tools that are more intelligent and easily accessible. © 2013 IEEE.

关键词： mHealth

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：