Browsing by Author "Musa, Jamilu Maaruf"

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    Analysis And Classification Of Motor Imagery Using Deep Neural Network
    (institution of Applied Materials and Technology Society with the cooperation of Faculty of Engineering, Universitas Riau, Pekanbaru, Indonesia, 2021-05-25) Ahmad, Isah Salim; Zhang, Shuai; Saminu, Sani; Isselmou; Musa, Jamilu Maaruf; Javaid, Imran; KAMHI, SOUHA; KULSUM, UMMAY
    Motor imagery based on brain-computer interface (BCI) has aĨracted important research aĨention despite its difficulty. It plays a vital role in human cognition and helps in making the decision. Many researchers use electroencephalogram (EEG) signals to study brain activity with leě and right-hand movement. Deep learning (DL) has been employed for motor imagery (MI). In this article, a deep neural network (DNN) is proposed for classiėcation of leě and right movement of EEG signal using Common Spatial PaĨern (CSP) as feature extraction with standard gradient descent (GD) with momentum and adaptive learning rate LR. (GDMLR), the performance is compared using a confusion matrix, the average classiėcation accuracy is 87%, which is improved as compared with state-of-the-art methods that used different datasets.
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    Updates on Movie Recommendation System
    (Faculty of Technology Education, Abubakar Tafawa Balewa University Bauchi, 2021-02) Musa, Jamilu Maaruf; Zhihong, Xu; Saminu, Sani; Muswelu, Cecillia; Karaye, Ibrahim Abdullahi; Ahmad, Isah Salim
    In recent years, there is a huge number of movies on the internet. Users have different desires for a movie to watch as there are different cultures, languages, and genres to choose from in a movie domain. As a result, a recommendation system approach is used to suggest the best movies to users according to their preferences. Several different algorithms and strategies have been proposed to effectively capture users’ interest and provide an accurate recommendation of movies. Memory-Based Collaborative Filtering Recommender Systems existed for the best part of the last two decades. It is an advanced technology, implemented in various commercial applications which because of its effectiveness has been the predominantly used technique to date in recommendation system. Memory-based collaborative filtering approach is popularly and extensively used in practice but yet faces some key challenges in providing high-quality recommendations due to the daily increase of items and visitors of different websites. This paper presents a review of different techniques and similarity measures used in the movie recommendation system and also proposed a model that can be used to build robust, accurate and scalable movie recommendation to users.