Network-Computation in Neural Systems最新文献_第3页

Statement of Retraction. 撤回。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2024-07-31 DOI: 10.1080/0954898X.2024.2385532

引用次数: 0

Hybrid deep learning approach for sentiment analysis using text and emojis. 使用文本和表情符号进行情感分析的混合深度学习方法。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2024-05-29 DOI: 10.1080/0954898X.2024.2349275

Arjun Kuruva, C Nagaraju Chiluka

{"title":"Hybrid deep learning approach for sentiment analysis using text and emojis.","authors":"Arjun Kuruva, C Nagaraju Chiluka","doi":"10.1080/0954898X.2024.2349275","DOIUrl":"10.1080/0954898X.2024.2349275","url":null,"abstract":"Sentiment Analysis (SA) is a technique for categorizing texts based on the sentimental polarity of people's opinions. This paper introduces a sentiment analysis (SA) model with text and emojis. The two preprocessed data's are data with text and emojis and text without emojis. Feature extraction consists text features and text with emojis features. The text features are features like N-grams, modified Term Frequency-Inverse Document Frequency (TF-IDF), and Bag-of-Words (BoW) features extracted from the text. In classification, CNN (Conventional Neural Network) and MLP (Multi-Layer Perception) use emojis and text-based SA. The CNN weight is optimized by a new Electric fish Customized Shark Smell Optimization (ECSSO) Algorithm. Similarly, the text-based SA is carried out by hybrid Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) classifiers. The bagged data are given as input to the classification process via RNN and LSTM. Here, the weight of LSTM is optimized by the suggested ECSSO algorithm. Then, the mean of LSTM and RNN determines the final output. The specificity of the developed scheme is 29.01%, 42.75%, 23.88%,22.07%, 25.31%, 18.42%, 5.68%, 10.34%, 6.20%, 6.64%, and 6.84% better for 70% than other models. The efficiency of the proposed scheme is computed and evaluated.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"923-952"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141162790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An optimized deep strategy for recognition and alleviation of DDoS attack in SD-IoT. 用于识别和缓解 SD-IoT 中 DDoS 攻击的优化深度策略。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2024-06-17 DOI: 10.1080/0954898X.2024.2356852

Kalpana Kumbhar, Prachi Mukherji

{"title":"An optimized deep strategy for recognition and alleviation of DDoS attack in SD-IoT.","authors":"Kalpana Kumbhar, Prachi Mukherji","doi":"10.1080/0954898X.2024.2356852","DOIUrl":"10.1080/0954898X.2024.2356852","url":null,"abstract":"The attacks like distributed denial-of-service (DDoS) are termed as severe defence issues in data centres, and are considered real network threat. These types of attacks can produce huge disturbances in information technologies. In addition, it is a complex task to determine and fully alleviate DDoS attacks. The new strategy is developed to identify and alleviate DDoS attacks in the Software-Defined Internet of Things (SD-IoT) model. SD-IoT simulation is executed to gather data. The data collected through nodes of SD-IoT are fed to the selection of feature phases. Here, the hybrid process is considered to select features, wherein features, like wrapper-based technique, cosine similarity-based technique, and entropy-based technique are utilized to choose the significant features. Thereafter, the attack discovery process is done with Elephant Water Cycle (EWC)-assisted deep neuro-fuzzy network (DNFN). The EWC is adapted to train DNFN, and here EWC is obtained by grouping Elephant Herd Optimization (EHO) and water cycle algorithm (WCA). Finally, attack mitigation is carried out to secure the SD-IoT. The EWC-assisted DNFN revealed the highest accuracy of 96.9%, TNR of 98%, TPR of 90%, precision of 93%, and F1-score of 91%, when compared with other related techniques.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1047-1078"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm for preventing MANET Cyber security attacks. 采用熊嗅觉搜索算法优化的深度克罗内克神经网络的自适应激活函数，用于防范城域网网络安全攻击。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2024-03-14 DOI: 10.1080/0954898X.2024.2321391

E V R M Kalaimani Shanmugham, Saravanan Dhatchnamurthy, Prabbu Sankar Pakkiri, Neha Garg

{"title":"Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm for preventing MANET Cyber security attacks.","authors":"E V R M Kalaimani Shanmugham, Saravanan Dhatchnamurthy, Prabbu Sankar Pakkiri, Neha Garg","doi":"10.1080/0954898X.2024.2321391","DOIUrl":"10.1080/0954898X.2024.2321391","url":null,"abstract":"An Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm (BSSA) (ADKNN-BSSA-CSMANET) is proposed for preventing MANET Cyber security attacks. The mobile users are enrolled with Trusted Authority Using a Crypto Hash Signature (SHA-256). Every mobile user uploads their finger vein biometric, user ID, latitude and longitude for confirmation. The packet analyser checks if any attack patterns are identified. It is implemented using adaptive density-based spatial clustering (ADSC) that deems information from packet header. Geodesic filtering (GF) is used as a pre-processing method for eradicating the unsolicited content and filtering pertinent data. Group Teaching Algorithm (GTA)-based feature selection is utilized for ideal collection of features and Adaptive Activation Functions along Deep Kronecker Neural Network (ADKNN) is used to categorizing normal and attack packets (DoS, Probe, U2R, and R2L). Then BSSA is utilized for optimizing the weight parameters of ADKNN classifier for optimal classification. The proposed technique is executed in python and its efficiency is evaluated by several performances metrics, such as Accuracy, Attack Detection Rate, Detection Delay, Packet Delivery Ratio, Throughput, and Energy Consumption. The proposed technique provides 36.64%, 33.06%, and 33.98% lower Detection Delay on NSL-KDD dataset compared with the existing methods.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"426-450"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140121421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective prediction of human skin cancer using stacking based ensemble deep learning algorithm. 使用基于堆叠的集合深度学习算法有效预测人类皮肤癌。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2024-05-28 DOI: 10.1080/0954898X.2024.2346608

David Neels Ponkumar Devadhas, Hephzi Punithavathi Isaac Sugirtharaj, Mary Harin Fernandez, Duraipandy Periyasamy

{"title":"Effective prediction of human skin cancer using stacking based ensemble deep learning algorithm.","authors":"David Neels Ponkumar Devadhas, Hephzi Punithavathi Isaac Sugirtharaj, Mary Harin Fernandez, Duraipandy Periyasamy","doi":"10.1080/0954898X.2024.2346608","DOIUrl":"10.1080/0954898X.2024.2346608","url":null,"abstract":"Automated diagnosis of cancer from skin lesion data has been the focus of numerous research. Despite that it can be challenging to interpret these images because of features like colour illumination changes, variation in the sizes and forms of the lesions. To tackle these problems, the proposed model develops an ensemble of deep learning techniques for skin cancer diagnosis. Initially, skin imaging data are collected and preprocessed using resizing and anisotropic diffusion to enhance the quality of the image. Preprocessed images are fed into the Fuzzy-C-Means clustering technique to segment the region of diseases. Stacking-based ensemble deep learning approach is used for classification and the LSTM acts as a meta-classifier. Deep Neural Network (DNN) and Convolutional Neural Network (CNN) are used as input for LSTM. This segmented images are utilized to be input into the CNN, and the local binary pattern (LBP) technique is employed to extract DNN features from the segments of the image. The output from these two classifiers will be fed into the LSTM Meta classifier. This LSTM classifies the input data and predicts the skin cancer disease. The proposed approach had a greater accuracy of 97%. Hence, the developed model accurately predicts skin cancer disease.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"855-891"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141159240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RETRACTED ARTICLE: Stable route selection for adaptive packet transmission in 5G-based mobile communications. 基于 5G 的移动通信中自适应数据包传输的稳定路由选择。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-08-01 Epub Date: 2024-03-03 DOI: 10.1080/0954898X.2024.2318344

Muthulakshmi Karuppiyan, Hariharan Subramani, Shanthy Kandasamy Raju, Manimekalai Maradi Anthonymuthu Prakasam

引用次数: 0

Optimized Huffman encoding based medical image compression with Improved HDBSCAN. 基于改进HDBSCAN的优化Huffman编码医学图像压缩。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-06-19 DOI: 10.1080/0954898X.2025.2513691

Rajasekhar Butta, Mastan Sharif Shaik

引用次数: 0

BUBMO-based Bi-GRU-CNN model for crop classification with improved feature set: A bigdata perspective. 基于bubmo改进特征集的Bi-GRU-CNN作物分类模型：大数据视角。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-06-02 DOI: 10.1080/0954898X.2025.2503791

Shivi Sharma, D D Sharma, Ashish Sharma, Munish Manas

{"title":"BUBMO-based Bi-GRU-CNN model for crop classification with improved feature set: A bigdata perspective.","authors":"Shivi Sharma, D D Sharma, Ashish Sharma, Munish Manas","doi":"10.1080/0954898X.2025.2503791","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2503791","url":null,"abstract":"Purpose: Big Data's extensive capabilities can aid in addressing the unpredictability of food supply caused by a variety of issues including soil degradation, climate change, water pollution, socio-cultural expansion, governmental laws, and market volatility. However, crop monitoring and classification are critical components of agricultural precision farming. This paper intends to propose a crop classification via a hybrid classification model.Design: First, the input image dataset is subjected to the preprocessing stage to enhance the image dataset by removing noise and blurring the edges with the aid of Gaussian filtering. Second, the improved spider local image feature, median binary pattern and haralick texture features are extracted from the preprocessed image dataset by utilizing the map-reduce framework, to handle big data. Third, the hybrid classification model is proposed that involves two classifiers such as Bi-GRU and CNN.Findings: The weights of both classifier Bi-GRU and CNN were tuned optimally by the proposed hybrid optimization BUBMO that combined both BMO and BWO. The greatest MCC obtained by the propose is 91.47%, whilst the traditional model scored the lowest MCC.Originality: The accuracy and improved efficacy of the crop categorization are achieved by employing the suggested classification method.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-36"},"PeriodicalIF":1.1,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An optimized deep neural network with explainable artificial intelligence framework for brain tumour classification. 一个优化的深度神经网络，具有可解释的人工智能框架，用于脑肿瘤分类。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-05-04 DOI: 10.1080/0954898X.2025.2500046

Roohum Jegan, Bhakti Kaushal, Gajanan K Birajdar, Mukesh D Patil

{"title":"An optimized deep neural network with explainable artificial intelligence framework for brain tumour classification.","authors":"Roohum Jegan, Bhakti Kaushal, Gajanan K Birajdar, Mukesh D Patil","doi":"10.1080/0954898X.2025.2500046","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2500046","url":null,"abstract":"Brain tumour classification plays a significant role in improving patient care, treatment planning, and enhancing the overall healthcare system's effectiveness. This article presents a ResNet framework optimized using Henry gas solubility optimization (HGSO) for the classification of brain tumours, resulting in improved classification performance in magnetic resonance images (MRI). Two variants of the deep residual neural network, namely ResNet-18 and ResNet-50, are trained on the MRI training dataset. The four critical hyperparameters of the ResNet model: momentum, initial learning rate, maximum epochs, and validation frequency are tuned to obtain optimal values using HGSO algorithm. Subsequently, the optimized ResNet model is evaluated using two separate databases: Database1, comprising four tumour classes, and Database2, with three tumour classes. The performance is assessed using accuracy, sensitivity, specificity, precision, and F-score. The highest classification accuracy of 0.9825 is attained using the proposed optimized ResNet-50 framework on Database1. Moreover, the Gradient-weighted Class Activation Mapping (GRAD-CAM) algorithm is utilized to enhance the understanding of deep neural networks by highlighting the regions that are influential in making a particular classification decision. Grad-CAM heatmaps confirm the model focuses on relevant tumour features, not image artefacts. This research enhances MRI brain tumour classification via deep learning optimization strategies.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-35"},"PeriodicalIF":1.1,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction. 修正。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-05-02 DOI: 10.1080/0954898X.2025.2501418

引用次数: 0