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  • Open Access

    ARTICLE

    Accurate Machine Learning Predictions of Sci-Fi Film Performance

    Amjed Al Fahoum1,*, Tahani A. Ghobon2

    Journal of New Media, Vol.5, No.1, pp. 1-22, 2023, DOI:10.32604/jnm.2023.037583

    Abstract A groundbreaking method is introduced to leverage machine learning algorithms to revolutionize the prediction of success rates for science fiction films. In the captivating world of the film industry, extensive research and accurate forecasting are vital to anticipating a movie’s triumph prior to its debut. Our study aims to harness the power of available data to estimate a film’s early success rate. With the vast resources offered by the internet, we can access a plethora of movie-related information, including actors, directors, critic reviews, user reviews, ratings, writers, budgets, genres, Facebook likes, YouTube views for movie trailers, and Twitter followers. The… More >

  • Open Access

    ARTICLE

    Short Term Traffic Flow Prediction Using Hybrid Deep Learning

    Mohandu Anjaneyulu, Mohan Kubendiran*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1641-1656, 2023, DOI:10.32604/cmc.2023.035056

    Abstract Traffic flow prediction in urban areas is essential in the Intelligent Transportation System (ITS). Short Term Traffic Flow (STTF) prediction impacts traffic flow series, where an estimation of the number of vehicles will appear during the next instance of time per hour. Precise STTF is critical in Intelligent Transportation System. Various extinct systems aim for short-term traffic forecasts, ensuring a good precision outcome which was a significant task over the past few years. The main objective of this paper is to propose a new model to predict STTF for every hour of a day. In this paper, we have proposed… More >

  • Open Access

    ARTICLE

    Large Scale Fish Images Classification and Localization using Transfer Learning and Localization Aware CNN Architecture

    Usman Ahmad1, Muhammad Junaid Ali2, Faizan Ahmed Khan3, Arfat Ahmad Khan4, Arif Ur Rehman1, Malik Muhammad Ali Shahid5, Mohd Anul Haq6,*, Ilyas Khan7, Zamil S. Alzamil6, Ahmed Alhussen8

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2125-2140, 2023, DOI:10.32604/csse.2023.031008

    Abstract Building an automatic fish recognition and detection system for large-scale fish classes is helpful for marine researchers and marine scientists because there are large numbers of fish species. However, it is quite difficult to build such systems owing to the lack of data imbalance problems and large number of classes. To solve these issues, we propose a transfer learning-based technique in which we use Efficient-Net, which is pre-trained on ImageNet dataset and fine-tuned on QuT Fish Database, which is a large scale dataset. Furthermore, prior to the activation layer, we use Global Average Pooling (GAP) instead of dense layer with… More >

  • Open Access

    ARTICLE

    A Pattern Classification Model for Vowel Data Using Fuzzy Nearest Neighbor

    Monika Khandelwal1, Ranjeet Kumar Rout1, Saiyed Umer2, Kshira Sagar Sahoo3, NZ Jhanjhi4,*, Mohammad Shorfuzzaman5, Mehedi Masud5

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3587-3598, 2023, DOI:10.32604/iasc.2023.029785

    Abstract Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problems observed in the fuzzification of an unknown pattern is that importance is given only to the known patterns but not to their features. In contrast, features of the patterns play an essential role when their respective patterns overlap. In this paper, an optimal fuzzy nearest neighbor model has been introduced in which a fuzzification process has been carried out for the unknown pattern using… More >

  • Open Access

    ARTICLE

    Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

    Shyla1,*, Vishal Bhatnagar2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2431-2447, 2023, DOI:10.32604/iasc.2023.029126

    Abstract The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of information flowing over the network. The data will always remain under the threat of technological suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights. In this paper, the authors proposed the HDTbNB (Hybrid Decision Tree-based Naïve Bayes) algorithm to find the essential features without data scaling to maximize the model’s performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of… More >

  • Open Access

    ARTICLE

    Predict the Chances of Heart Abnormality in Diabetic Patients Through Machine Learning

    Monika Saraswat*, A. K. Wadhwani, Sulochana Wadhwani

    Journal on Artificial Intelligence, Vol.4, No.2, pp. 61-76, 2022, DOI:10.32604/jai.2022.028140

    Abstract Today, more families are affected by Diabetes Mellitus (DM) disease on account of its continually increasing occurrence. Most patients remain unknown about their health quality or the DM’s risk factors prior to diagnosis. The medical world has witnessed that individuals are affected by two different diabetes namely a) Type-1 diabetes (T1D), as well as b) Type-2 diabetes (T2D). As Type 2 Diabetes affects the other organs of the body, the proposed system concentrates specifically on Type 2 Diabetes. This work aims to ascertain the cardiac disorder in T2D patients. As of the ECG dataset, the requisite data is gathered it… More >

  • Open Access

    ARTICLE

    Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk

    Polin Rahman1, Ahmed Rifat1, MD. IftehadAmjad Chy1, Mohammad Monirujjaman Khan1,*, Mehedi Masud2, Sultan Aljahdali2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 757-775, 2023, DOI:10.32604/csse.2023.021469

    Abstract Heart failure is now widely spread throughout the world. Heart disease affects approximately 48% of the population. It is too expensive and also difficult to cure the disease. This research paper represents machine learning models to predict heart failure. The fundamental concept is to compare the correctness of various Machine Learning (ML) algorithms and boost algorithms to improve models’ accuracy for prediction. Some supervised algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), Logistic Regression (LR) are considered to achieve the best results. Some boosting algorithms like Extreme Gradient Boosting (XGBoost) and CatBoost are… More >

  • Open Access

    ARTICLE

    Handling High Dimensionality in Ensemble Learning for Arrhythmia Prediction

    Fuad Ali Mohammed Al-Yarimi*

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1729-1742, 2022, DOI:10.32604/iasc.2022.022418

    Abstract Computer-aided arrhythmia prediction from ECG (electrocardiograms) is essential in clinical practices, which promises to reduce the mortality caused by inexperienced clinical practitioners. Moreover, computer-aided methods often succeed in the early detection of arrhythmia scope from electrocardiogram reports. Machine learning is the buzz of computer-aided clinical practices. Particularly, computer-aided arrhythmia prediction methods highly adopted machine learning methods. However, the high dimensionality in feature values considered for the machine learning models’ training phase often causes false alarming. This manuscript addressed the high dimensionality in the learning phase and proposed an (Ensemble Learning method for Arrhythmia Prediction) ELAP (ensemble learning-based arrhythmia prediction). The… More >

  • Open Access

    ARTICLE

    Position Vectors Based Efficient Indoor Positioning System

    Ayesha Javed1, Mir Yasir Umair1,*, Alina Mirza1, Abdul Wakeel1, Fazli Subhan2, Wazir Zada Khan3

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1781-1799, 2021, DOI:10.32604/cmc.2021.015229

    Abstract With the advent and advancements in the wireless technologies, Wi-Fi fingerprinting-based Indoor Positioning System (IPS) has become one of the most promising solutions for localization in indoor environments. Unlike the outdoor environment, the lack of line-of-sight propagation in an indoor environment keeps the interest of the researchers to develop efficient and precise positioning systems that can later be incorporated in numerous applications involving Internet of Things (IoTs) and green computing. In this paper, we have proposed a technique that combines the capabilities of multiple algorithms to overcome the complexities experienced indoors. Initially, in the database development phase, Motley Kennan propagation… More >

  • Open Access

    ARTICLE

    Machine Learning Empowered Security Management and Quality of Service Provision in SDN-NFV Environment

    Shumaila Shahzadi1, Fahad Ahmad1,*, Asma Basharat1, Madallah Alruwaili2, Saad Alanazi2, Mamoona Humayun2, Muhammad Rizwan1, Shahid Naseem3

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2723-2749, 2021, DOI:10.32604/cmc.2021.014594

    Abstract With the rising demand for data access, network service providers face the challenge of growing their capital and operating costs while at the same time enhancing network capacity and meeting the increased demand for access. To increase efficacy of Software Defined Network (SDN) and Network Function Virtualization (NFV) framework, we need to eradicate network security configuration errors that may create vulnerabilities to affect overall efficiency, reduce network performance, and increase maintenance cost. The existing frameworks lack in security, and computer systems face few abnormalities, which prompts the need for different recognition and mitigation methods to keep the system in the… More >

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