Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (15)
  • Open Access


    Time and Space Efficient Multi-Model Convolution Vision Transformer for Tomato Disease Detection from Leaf Images with Varied Backgrounds

    Ankita Gangwar1, Vijaypal Singh Dhaka1, Geeta Rani2,*, Shrey Khandelwal1, Ester Zumpano3,4, Eugenio Vocaturo3,4

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 117-142, 2024, DOI:10.32604/cmc.2024.048119

    Abstract A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20% of the total consumption. An increase of 3.3% in consumption is predicted from 2024 to 2032. Tomatoes are also rich in iron, potassium, antioxidant lycopene, vitamins A, C and K which are important for preventing cancer, and maintaining blood pressure and glucose levels. Thus, tomatoes are globally important due to their widespread usage and nutritional value. To face the high demand for tomatoes, it is mandatory to investigate the causes of crop loss and minimize them. Diseases are… More >

  • Open Access


    Fake News Detection Based on Text-Modal Dominance and Fusing Multiple Multi-Model Clues

    Lifang Fu1, Huanxin Peng2,*, Changjin Ma2, Yuhan Liu2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4399-4416, 2024, DOI:10.32604/cmc.2024.047053

    Abstract In recent years, how to efficiently and accurately identify multi-model fake news has become more challenging. First, multi-model data provides more evidence but not all are equally important. Secondly, social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical. Unfortunately, existing approaches fail to handle these problems. This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues (TD-MMC), which utilizes three valuable multi-model clues: text-model importance, text-image complementary, and text-image inconsistency. TD-MMC is… More >

  • Open Access


    Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

    Nazik Alturki1, Raed Alharthi2, Muhammad Umer3,*, Oumaima Saidani1, Amal Alshardan1, Reemah M. Alhebshi4, Shtwai Alsubai5, Ali Kashif Bashir6,7,8,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3387-3415, 2024, DOI:10.32604/cmes.2023.044700

    Abstract The concept of smart houses has grown in prominence in recent years. Major challenges linked to smart homes are identification theft, data safety, automated decision-making for IoT-based devices, and the security of the device itself. Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features. This paper proposes a smart home system based on ensemble learning of random forest (RF) and convolutional neural networks (CNN) for programmed decision-making tasks, such as categorizing gadgets… More >

  • Open Access


    An Adaptive DDoS Detection and Classification Method in Blockchain Using an Integrated Multi-Models

    Xiulai Li1,2,3,4, Jieren Cheng1,3,*, Chengchun Ruan1,3, Bin Zhang1,3, Xiangyan Tang1,3, Mengzhe Sun5

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3265-3288, 2023, DOI:10.32604/cmc.2023.045588

    Abstract With the rising adoption of blockchain technology due to its decentralized, secure, and transparent features, ensuring its resilience against network threats, especially Distributed Denial of Service (DDoS) attacks, is crucial. This research addresses the vulnerability of blockchain systems to DDoS assaults, which undermine their core decentralized characteristics, posing threats to their security and reliability. We have devised a novel adaptive integration technique for the detection and identification of varied DDoS attacks. To ensure the robustness and validity of our approach, a dataset amalgamating multiple DDoS attacks was derived from the CIC-DDoS2019 dataset. Using this, our… More >

  • Open Access


    Multi-Model Fusion Framework Using Deep Learning for Visual-Textual Sentiment Classification

    Israa K. Salman Al-Tameemi1,3, Mohammad-Reza Feizi-Derakhshi1,*, Saeed Pashazadeh2, Mohammad Asadpour2

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2145-2177, 2023, DOI:10.32604/cmc.2023.040997

    Abstract Multimodal Sentiment Analysis (SA) is gaining popularity due to its broad application potential. The existing studies have focused on the SA of single modalities, such as texts or photos, posing challenges in effectively handling social media data with multiple modalities. Moreover, most multimodal research has concentrated on merely combining the two modalities rather than exploring their complex correlations, leading to unsatisfactory sentiment classification results. Motivated by this, we propose a new visual-textual sentiment classification model named Multi-Model Fusion (MMF), which uses a mixed fusion framework for SA to effectively capture the essential information and the… More >

  • Open Access


    A Multi-Modal Deep Learning Approach for Emotion Recognition

    H. M. Shahzad1,3, Sohail Masood Bhatti1,3,*, Arfan Jaffar1,3, Muhammad Rashid2

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1561-1570, 2023, DOI:10.32604/iasc.2023.032525

    Abstract In recent years, research on facial expression recognition (FER) under mask is trending. Wearing a mask for protection from Covid 19 has become a compulsion and it hides the facial expressions that is why FER under the mask is a difficult task. The prevailing unimodal techniques for facial recognition are not up to the mark in terms of good results for the masked face, however, a multimodal technique can be employed to generate better results. We proposed a multimodal methodology based on deep learning for facial recognition under a masked face using facial and vocal… More >

  • Open Access


    Data Augmentation and Random Multi-Model Deep Learning for Data Classification

    Fatma Harby1, Adel Thaljaoui1, Durre Nayab2, Suliman Aladhadh3,*, Salim EL Khediri3,4, Rehan Ullah Khan3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5191-5207, 2023, DOI:10.32604/cmc.2022.029420

    Abstract In the machine learning (ML) paradigm, data augmentation serves as a regularization approach for creating ML models. The increase in the diversification of training samples increases the generalization capabilities, which enhances the prediction performance of classifiers when tested on unseen examples. Deep learning (DL) models have a lot of parameters, and they frequently overfit. Effectively, to avoid overfitting, data plays a major role to augment the latest improvements in DL. Nevertheless, reliable data collection is a major limiting factor. Frequently, this problem is undertaken by combining augmentation of data, transfer learning, dropout, and methods of More >

  • Open Access


    Combined Linear Multi-Model for Reliable Route Recommender in Next Generation Network

    S. Kalavathi1,*, R. Nedunchelian2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 39-56, 2023, DOI:10.32604/iasc.2023.031522

    Abstract Network analysis is a promising field in the area of network applications as different types of traffic grow enormously and exponentially. Reliable route prediction is a challenging task in the Large Scale Networks (LSN). Various non-self-learning and self-learning approaches have been adopted to predict reliable routing. Routing protocols decide how to send all the packets from source to the destination addresses across the network through their IP. In the current era, dynamic protocols are preferred as they network self-learning internally using an algorithm and may not entail being updated physically more than the static protocols.… More >

  • Open Access


    Differential Evolution Algorithm with Hierarchical Fair Competition Model

    Amit Ramesh Khaparde1,*, Fawaz Alassery2, Arvind Kumar3, Youseef Alotaibi4, Osamah Ibrahim Khalaf5, Sofia Pillai6, Saleh Alghamdi7

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1045-1062, 2022, DOI:10.32604/iasc.2022.023270

    Abstract This paper presents the study of differential evolution algorithm with hierarchical fair competition model (HFC-DE). HFC model is based on the fair competition of societal system found in natural world. In this model, the population is split into hierarchy and the competition is allowed between the hierarchical members. During evolution, the population members are allowed to move within the hierarchy levels. The standard differential evolution algorithm is used for population evolution. Experimentation has carried out to define the parameter for proposed model on test suit having unimodal problems and multi-model problems. After analyzing the results, More >

  • Open Access


    Solving the Feature Diversity Problem Based on Multi-Model Scheme

    Guanghao Jin1, Na Zhao1, Chunmei Pei1, Hengguang Li2, Qingzeng Song3, Jing Yu1,*

    Journal on Artificial Intelligence, Vol.3, No.4, pp. 135-143, 2021, DOI:10.32604/jai.2021.027154

    Abstract Generally, the performance of deep learning models is related to the captured features of training samples. When the training samples belong to different domains, the diverse features may increase the difficulty of training high performance models. In this paper, we built a new framework that generates multiple models on the organized samples to increase the accuracy of classification. Firstly, our framework selects some existing models and trains each of them on organized training sets to get multiple trained models. Secondly, we select some of them based on a validation set. Finally, we use some fusion More >

Displaying 1-10 on page 1 of 15. Per Page