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  1. Article ; Online: Cancer Detection and Prediction Using Genetic Algorithms.

    Bhandari, Aradhita / Tripathy, B K / Jawad, Khurram / Bhatia, Surbhi / Rahmani, Mohammad Khalid Imam / Mashat, Arwa

    publication RETRACTED

    Computational intelligence and neuroscience

    2022  Volume 2022, Page(s) 1871841

    Abstract: Cancer is a wide category of diseases that is caused by the abnormal, uncontrollable growth of cells, and it is the second leading cause of death globally. Screening, early diagnosis, and prediction of recurrence give patients the best possible chance ... ...

    Abstract Cancer is a wide category of diseases that is caused by the abnormal, uncontrollable growth of cells, and it is the second leading cause of death globally. Screening, early diagnosis, and prediction of recurrence give patients the best possible chance for successful treatment. However, these tests can be expensive and invasive and the results have to be interpreted by experts. Genetic algorithms (GAs) are metaheuristics that belong to the class of evolutionary algorithms. GAs can find the optimal or near-optimal solutions in huge, difficult search spaces and are widely used for search and optimization. This makes them ideal for detecting cancer by creating models to interpret the results of tests, especially noninvasive. In this article, we have comprehensively reviewed the existing literature, analyzed them critically, provided a comparative analysis of the state-of-the-art techniques, and identified the future challenges in the development of such techniques by medical professionals.
    MeSH term(s) Algorithms ; Biological Evolution ; Humans ; Neoplasms/diagnosis ; Neoplasms/genetics
    Language English
    Publishing date 2022-05-16
    Publishing country United States
    Document type Journal Article ; Retracted Publication
    ZDB-ID 2388208-6
    ISSN 1687-5273 ; 1687-5273
    ISSN (online) 1687-5273
    ISSN 1687-5273
    DOI 10.1155/2022/1871841
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Blockchain-Based Trust Management Framework for Cloud Computing-Based Internet of Medical Things (IoMT): A Systematic Review.

    Rahmani, Mohammad Khalid Imam / Shuaib, Mohammed / Alam, Shadab / Siddiqui, Shams Tabrez / Ahmad, Sadaf / Bhatia, Surbhi / Mashat, Arwa

    publication RETRACTED

    Computational intelligence and neuroscience

    2022  Volume 2022, Page(s) 9766844

    Abstract: The internet of medical things (IoMT) is a smart medical device structure that includes apps, health services, and systems. These medical equipment and applications are linked to healthcare systems via the internet. Because IoT devices lack computational ...

    Abstract The internet of medical things (IoMT) is a smart medical device structure that includes apps, health services, and systems. These medical equipment and applications are linked to healthcare systems via the internet. Because IoT devices lack computational power, the collected data can be processed and analyzed in the cloud by more computationally intensive tools. Cloud computing in IoMT is also used to store IoT data as part of a collaborative effort. Cloud computing has provided new avenues for providing services to users with better user experience, scalability, and proper resource utilization compared to traditional platforms. However, these cloud platforms are susceptible to several security breaches evident from recent and past incidents. Trust management is a crucial feature required for providing secure and reliable service to users. The traditional trust management protocols in the cloud computing situation are centralized and result in single-point failure. Blockchain has emerged as the possible use case for the domain that requires trust and reliability in several aspects. Different researchers have presented various blockchain-based trust management approaches. This study reviews the trust challenges in cloud computing and analyzes how blockchain technology addresses these challenges using blockchain-based trust management frameworks. There are ten (10) solutions under two broad categories of decentralization and security. These challenges are centralization, huge overhead, trust evidence, less adaptive, and inaccuracy. This systematic review has been performed in six stages: identifying the research question, research methods, screening the related articles, abstract and keyword examination, data retrieval, and mapping processing. Atlas.ti software is used to analyze the relevant articles based on keywords. A total of 70 codes and 262 quotations are compiled, and furthermore, these quotations are categorized using manual coding. Finally, 20 solutions under two main categories of decentralization and security were retrieved. Out of these ten (10) solutions, three (03) fell in the security category, and the rest seven (07) came under the decentralization category.
    MeSH term(s) Blockchain ; Cloud Computing ; Internet ; Reproducibility of Results ; Trust
    Language English
    Publishing date 2022-05-19
    Publishing country United States
    Document type Journal Article ; Review ; Systematic Review ; Retracted Publication
    ZDB-ID 2388208-6
    ISSN 1687-5273 ; 1687-5273
    ISSN (online) 1687-5273
    ISSN 1687-5273
    DOI 10.1155/2022/9766844
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

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  3. Article: Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection.

    Safdar, Sadia / Rizwan, Muhammad / Gadekallu, Thippa Reddy / Javed, Abdul Rehman / Rahmani, Mohammad Khalid Imam / Jawad, Khurram / Bhatia, Surbhi

    Diagnostics (Basel, Switzerland)

    2022  Volume 12, Issue 5

    Abstract: Breast cancer is one of the most widespread diseases in women worldwide. It leads to the second-largest mortality rate in women, especially in European countries. It occurs when malignant lumps that are cancerous start to grow in the breast cells. ... ...

    Abstract Breast cancer is one of the most widespread diseases in women worldwide. It leads to the second-largest mortality rate in women, especially in European countries. It occurs when malignant lumps that are cancerous start to grow in the breast cells. Accurate and early diagnosis can help in increasing survival rates against this disease. A computer-aided detection (CAD) system is necessary for radiologists to differentiate between normal and abnormal cell growth. This research consists of two parts; the first part involves a brief overview of the different image modalities, using a wide range of research databases to source information such as ultrasound, histography, and mammography to access various publications. The second part evaluates different machine learning techniques used to estimate breast cancer recurrence rates. The first step is to perform preprocessing, including eliminating missing values, data noise, and transformation. The dataset is divided as follows: 60% of the dataset is used for training, and the rest, 40%, is used for testing. We focus on minimizing type one false-positive rate (FPR) and type two false-negative rate (FNR) errors to improve accuracy and sensitivity. Our proposed model uses machine learning techniques such as support vector machine (SVM), logistic regression (LR), and K-nearest neighbor (KNN) to achieve better accuracy in breast cancer classification. Furthermore, we attain the highest accuracy of 97.7% with 0.01 FPR, 0.03 FNR, and an area under the ROC curve (AUC) score of 0.99. The results show that our proposed model successfully classifies breast tumors while overcoming previous research limitations. Finally, we summarize the paper with the future trends and challenges of the classification and segmentation in breast cancer detection.
    Language English
    Publishing date 2022-05-03
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662336-5
    ISSN 2075-4418
    ISSN 2075-4418
    DOI 10.3390/diagnostics12051134
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: A Deterministic Model for Determining Degree of Friendship Based on Mutual Likings and Recommendations on OTT Platforms.

    Khalique, Aqeel / Rahmani, Mohammad Khalid Imam / Saquib, Mohd / Hussain, Imran / Muzaffar, Abdul Wahab / Ahad, Mohd Abdul / Nafis, Md Tabrez / Ahmad, Mohd Wazih

    Computational intelligence and neuroscience

    2022  Volume 2022, Page(s) 9576468

    Abstract: In recent years, the application of various recommendation algorithms on over-the-top (OTT) platforms such as Amazon Prime and Netflix has been explored, but the existing recommendation systems are less effective because either they fail to take an ... ...

    Abstract In recent years, the application of various recommendation algorithms on over-the-top (OTT) platforms such as Amazon Prime and Netflix has been explored, but the existing recommendation systems are less effective because either they fail to take an advantage of exploiting the inherent user relationship or they are not capable of precisely defining the user relationship. On such platforms, users generally express their preferences for movies and TV shows and also give ratings to them. For a recommendation system to be effective, it is important to establish an accurate and precise relationship between the users. Hence, there is a scope of research for effective recommendation systems that can define a relationship between users and then use the relationship to enhance the user experiences. In this research article, we have presented a hybrid recommendation system that determines the degree of friendship among the viewers based on mutual liking and recommendations on OTT platforms. The proposed enhanced model is an effective recommendation model for determining the degree of friendship among viewers with improved user experience.
    MeSH term(s) Algorithms ; Friends ; Humans ; Motion Pictures
    Language English
    Publishing date 2022-06-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2388208-6
    ISSN 1687-5273 ; 1687-5273
    ISSN (online) 1687-5273
    ISSN 1687-5273
    DOI 10.1155/2022/9576468
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

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    Kategorien

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    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

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