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  • 1
    Online Resource
    Online Resource
    White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images
    MDPI AG ; 2022
    In:  Healthcare Vol. 10, No. 11 ( 2022-11-08), p. 2230-
    Details
    In: Healthcare, MDPI AG, Vol. 10, No. 11 ( 2022-11-08), p. 2230-
    Abstract: White blood cell (WBC) type classification is a task of significant importance for diagnosis using microscopic images of WBC, which develop immunity to fight against infections and foreign substances. WBCs consist of different types, and abnormalities in a type of WBC may potentially represent a disease such as leukemia. Existing studies are limited by low accuracy and overrated performance, often caused by model overfit due to an imbalanced dataset. Additionally, many studies consider a lower number of WBC types, and the accuracy is exaggerated. This study presents a hybrid feature set of selective features and synthetic minority oversampling technique-based resampling to mitigate the influence of the above-mentioned problems. Furthermore, machine learning models are adopted for being less computationally complex, requiring less data for training, and providing robust results. Experiments are performed using both machine- and deep learning models for performance comparison using the original dataset, augmented dataset, and oversampled dataset to analyze the performances of the models. The results suggest that a hybrid feature set of both texture and RGB features from microscopic images, selected using Chi2, produces a high accuracy of 0.97 with random forest. Performance appraisal using k-fold cross-validation and comparison with existing state-of-the-art studies shows that the proposed approach outperforms existing studies regarding the obtained accuracy and computational complexity.
    Type of Medium: Online Resource
    ISSN: 2227-9032
    URL: Article
    DOI: 10.3390/healthcare10112230
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2721009-1
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  • 2
    Online Resource
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    An enhanced opportunistic rank-based parent node selection for sustainable & smart IoT networks
    Elsevier BV ; 2023
    In:  Sustainable Energy Technologies and Assessments Vol. 56 ( 2023-03), p. 103079-
    Details
    In: Sustainable Energy Technologies and Assessments, Elsevier BV, Vol. 56 ( 2023-03), p. 103079-
    Type of Medium: Online Resource
    ISSN: 2213-1388
    URL: Article
    DOI: 10.1016/j.seta.2023.103079
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2701012-0
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  • 3
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    Towards Security Mechanism in D2D Wireless Communication: A 5G Network Approach
    Hindawi Limited ; 2022
    In:  Wireless Communications and Mobile Computing Vol. 2022 ( 2022-7-22), p. 1-9
    Details
    In: Wireless Communications and Mobile Computing, Hindawi Limited, Vol. 2022 ( 2022-7-22), p. 1-9
    Abstract: Device-to-device (D2D) communication has attracted many researchers, cellular operators, and equipment makers as mobile traffic and bandwidth demands have increased. It supports direct communication within devices with no need for any intermediate node and, therefore, offers advantage in 5G network while providing wide cell coverage range and frequency reuse. However, establishing acceptable and secure mechanism for D2D communication which ensures confidentiality, integrity, and availability is an issue encountered in this situation. Furthermore, in a resource-constrained IoT environment, these security challenges are more critical and difficult to mitigate, especially during emergence of IoT with 5G network application scenarios. To address these issues, this paper proposed a security mechanism in 5G network for D2D wireless communication dependent on lightweight modified elliptic curve cryptography (LMECC). The proposed scheme follows a proactive routing protocol to discover services, managing link setup, and for data transfer with the aim to reduce communication overhead during user authentication. The proposed approach has been compared against Diffie–Hellman (DH) and ElGamal (ELG) schemes to evaluate the protocol overhead and security enhancement at network edge. Results proved the outstanding performance of the proposed LMECC for strengthening data secrecy with approximate 13% and 22.5% lower overhead than DH and ELG schemes.
    Type of Medium: Online Resource
    ISSN: 1530-8677 , 1530-8669
    URL: Article
    DOI: 10.1155/2022/6983655
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2045240-8
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  • 4
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    Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification
    MDPI AG ; 2022
    In:  Sustainability Vol. 14, No. 18 ( 2022-09-13), p. 11484-
    Details
    In: Sustainability, MDPI AG, Vol. 14, No. 18 ( 2022-09-13), p. 11484-
    Abstract: One of the toughest biometrics and document forensics problems is confirming a signature’s authenticity and legal identity. A forgery may vary from a genuine signature by specific distortions. Therefore, it is necessary to continuously monitor crucial distinctions between real and forged signatures for secure work and economic growth, but this is particularly difficult in writer-independent tasks. We thus propose an innovative and sustainable writer-independent approach based on a Siamese neural network for offline signature verification. The Siamese network is a twin-like structure with shared weights and parameters. Similar and dissimilar images are exposed to this network, and the Euclidean distances between them are calculated. The distance is reduced for identical signatures, and the distance is increased for different signatures. Three datasets, namely GPDS, BHsig260 Hindi, and BHsig260 Bengali datasets, were tested in this work. The proposed model was analyzed by comparing the results of different parameters such as optimizers, batch size, and the number of epochs on all three datasets. The proposed Siamese neural network outperforms the GPDS synthetic dataset in the English language, with an accuracy of 92%. It also performs well for the Hindi and Bengali datasets while considering skilled forgeries.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su141811484
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2518383-7
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  • 5
    Online Resource
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    Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques
    MDPI AG ; 2022
    In:  Cancers Vol. 14, No. 16 ( 2022-08-13), p. 3914-
    Details
    In: Cancers, MDPI AG, Vol. 14, No. 16 ( 2022-08-13), p. 3914-
    Abstract: Thyroid disease prediction has emerged as an important task recently. Despite existing approaches for its diagnosis, often the target is binary classification, the used datasets are small-sized and results are not validated either. Predominantly, existing approaches focus on model optimization and the feature engineering part is less investigated. To overcome these limitations, this study presents an approach that investigates feature engineering for machine learning and deep learning models. Forward feature selection, backward feature elimination, bidirectional feature elimination, and machine learning-based feature selection using extra tree classifiers are adopted. The proposed approach can predict Hashimoto’s thyroiditis (primary hypothyroid), binding protein (increased binding protein), autoimmune thyroiditis (compensated hypothyroid), and non-thyroidal syndrome (NTIS) (concurrent non-thyroidal illness). Extensive experiments show that the extra tree classifier-based selected feature yields the best results with 0.99 accuracy and an F1 score when used with the random forest classifier. Results suggest that the machine learning models are a better choice for thyroid disease detection regarding the provided accuracy and the computational complexity. K-fold cross-validation and performance comparison with existing studies corroborate the superior performance of the proposed approach.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers14163914
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2527080-1
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  • 6
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    Online Resource
    An Artificial Neural Network Model for Water Quality and Water Consumption Prediction
    MDPI AG ; 2022
    In:  Water Vol. 14, No. 21 ( 2022-10-23), p. 3359-
    Details
    In: Water, MDPI AG, Vol. 14, No. 21 ( 2022-10-23), p. 3359-
    Abstract: With rapid urbanization, high rates of industrialization, and inappropriate waste disposal, water quality has been substantially degraded during the past decade. So, water quality prediction, an essential element for a healthy society, has become a task of great significance to protecting the water environment. Existing approaches focus predominantly on either water quality or water consumption prediction, utilizing complex algorithms that reduce the accuracy of imbalanced datasets and increase computational complexity. This study proposes a simple architecture of neural networks which is more efficient and accurate and can work for predicting both water quality and water consumption. An artificial neural network (ANN) consisting of one hidden layer and a couple of dropout and activation layers is utilized in this regard. The approach is tested using two datasets for predicting water quality and water consumption. Results show a 0.96 accuracy for water quality prediction which is better than existing studies. A 0.99 R2 score is obtained for water consumption prediction which is superior to existing state-of-the-art approaches.
    Type of Medium: Online Resource
    ISSN: 2073-4441
    URL: Article
    DOI: 10.3390/w14213359
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2521238-2
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  • 7
    Online Resource
    Online Resource
    Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model
    MDPI AG ; 2022
    In:  Sensors Vol. 22, No. 19 ( 2022-10-10), p. 7692-
    Details
    In: Sensors, MDPI AG, Vol. 22, No. 19 ( 2022-10-10), p. 7692-
    Abstract: Building energy consumption prediction has become an important research problem within the context of sustainable homes and smart cities. Data-driven approaches have been regarded as the most suitable for integration into smart houses. With the wide deployment of IoT sensors, the data generated from these sensors can be used for modeling and forecasting energy consumption patterns. Existing studies lag in prediction accuracy and various attributes of buildings are not very well studied. This study follows a data-driven approach in this regard. The novelty of the paper lies in the fact that an ensemble model is proposed, which provides higher performance regarding cooling and heating load prediction. Moreover, the influence of different features on heating and cooling load is investigated. Experiments are performed by considering different features such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. Results indicate that relative compactness, surface area, and wall area play a significant role in selecting the appropriate cooling and heating load for a building. The proposed model achieves 0.999 R2 for heating load prediction and 0.997 R2 for cooling load prediction, which is superior to existing state-of-the-art models. The precise prediction of heating and cooling load, can help engineers design energy-efficient buildings, especially in the context of future smart homes.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s22197692
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2052857-7
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  • 8
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    Optimal Sizing and Deployment of Renewable Energy Generators in Practical Transmission Network Using Grid-Oriented Multiobjective Harmony Search Algorithm for Loss Reduction and Voltage Profile Improvements
    Hindawi Limited ; 2023
    In:  International Transactions on Electrical Energy Systems Vol. 2023 ( 2023-3-22), p. 1-19
    Details
    In: International Transactions on Electrical Energy Systems, Hindawi Limited, Vol. 2023 ( 2023-3-22), p. 1-19
    Abstract: This paper presents grid-oriented multiobjective harmony search algorithm (GOMOHSA) to incorporate the multiple grid parameters for minimization of the active power loss, reactive power loss, and total voltage deviations (TVD) in a part of practical transmission network of Rajasthan Rajya Vidyut Prasaran Nigam Limited (RVPN) in southern parts of Rajasthan state of India. This is achieved by optimal deployment of optimally sized renewable energy (RE) generators using GOMOHSA. Performance indexes such as active power loss minimization index (APMLI), the reactive power loss minimization index (RPMLI), and the total voltage deviation improvement index (TVDII) are introduced to evaluate the health of the test network with different load scenarios. Performance of proposed GOMOHSA has been tested for five different operating scenarios of loads and RE generation. It is established that the proposed GOMOHSA finds the optimal deployment of optimally sized RE generators, and the investment cost of deployment of these RE generators can be recovered within a time period that is less than 5 years. Performance of GOMOHSA is superior compared to a conventional genetic algorithm (GA) in terms of performance indexes, RE generator capacity, payback period, and parameter sensitivity. Study is performed using MATLAB software for loading scenario of base year 2021 and projected year 2031.
    Type of Medium: Online Resource
    ISSN: 2050-7038
    URL: Article
    DOI: 10.1155/2023/6315918
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2023
    detail.hit.zdb_id: 2702272-9
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  • 9
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    Online Resource
    A CAD System for Alzheimer’s Disease Classification Using Neuroimaging MRI 2D Slices
    Hindawi Limited ; 2022
    In:  Computational and Mathematical Methods in Medicine Vol. 2022 ( 2022-8-9), p. 1-11
    Details
    In: Computational and Mathematical Methods in Medicine, Hindawi Limited, Vol. 2022 ( 2022-8-9), p. 1-11
    Abstract: Developments in medical care have inspired wide interest in the current decade, especially to their services to individuals living prolonged and healthier lives. Alzheimer’s disease (AD) is the most chronic neurodegeneration and dementia-causing disorder. Economic expense of treating AD patients is expected to grow. The requirement of developing a computer-aided technique for early AD categorization becomes even more essential. Deep learning (DL) models offer numerous benefits against machine learning tools. Several latest experiments that exploited brain magnetic resonance imaging (MRI) scans and convolutional neural networks (CNN) for AD classification showed promising conclusions. CNN’s receptive field aids in the extraction of main recognizable features from these MRI scans. In order to increase classification accuracy, a new adaptive model based on CNN and support vector machines (SVM) is presented in the research, combining both the CNN’s capabilities in feature extraction and SVM in classification. The objective of this research is to build a hybrid CNN-SVM model for classifying AD using the MRI ADNI dataset. Experimental results reveal that the hybrid CNN-SVM model outperforms the CNN model alone, with relative improvements of 3.4%, 1.09%, 0.85%, and 2.82% on the testing dataset for AD vs. cognitive normal (CN), CN vs. mild cognitive impairment (MCI), AD vs. MCI, and CN vs. MCI vs. AD, respectively. Finally, the proposed approach has been further experimented on OASIS dataset leading to accuracy of 86.2%.
    Type of Medium: Online Resource
    ISSN: 1748-6718 , 1748-670X
    URL: Article
    DOI: 10.1155/2022/8680737
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2256917-0
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  • 10
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    Consortium Framework Using Blockchain for Asthma Healthcare in Pandemics
    MDPI AG ; 2022
    In:  Sensors Vol. 22, No. 21 ( 2022-11-07), p. 8582-
    Details
    In: Sensors, MDPI AG, Vol. 22, No. 21 ( 2022-11-07), p. 8582-
    Abstract: Asthma is a deadly disease that affects the lungs and air supply of the human body. Coronavirus and its variants also affect the airways of the lungs. Asthma patients approach hospitals mostly in a critical condition and require emergency treatment, which creates a burden on health institutions during pandemics. The similar symptoms of asthma and coronavirus create confusion for health workers during patient handling and treatment of disease. The unavailability of patient history to physicians causes complications in proper diagnostics and treatments. Many asthma patient deaths have been reported especially during pandemics, which necessitates an efficient framework for asthma patients. In this article, we have proposed a blockchain consortium healthcare framework for asthma patients. The proposed framework helps in managing asthma healthcare units, coronavirus patient records and vaccination centers, insurance companies, and government agencies, which are connected through the secure blockchain network. The proposed framework increases data security and scalability as it stores encrypted patient data on the Interplanetary File System (IPFS) and keeps data hash values on the blockchain. The patient data are traceable and accessible to physicians and stakeholders, which helps in accurate diagnostics, timely treatment, and the management of patients. The smart contract ensures the execution of all business rules. The patient profile generation mechanism is also discussed. The experiment results revealed that the proposed framework has better transaction throughput, query delay, and security than existing solutions.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s22218582
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2052857-7
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