Movie Popularity Classification based on Inherent Movie Attributes using C4.5,PART and Correlation Coefficient

Abundance of movie data across the internet makes it an obvious candidate for machine learning and knowledge discovery. But most researches are directed towards bi-polar classification of movie or generation of a movie recommendation system based on reviews given by viewers on various internet sites. Classification of movie popularity based solely on attributes of a movie i.e. actor, actress, director rating, language, country and budget etc. has been less highlighted due to large number of attributes that are associated with each movie and their differences in dimensions. In this paper, we propose classification scheme of pre-release movie popularity based on inherent attributes using C4.5 and PART classifier algorithm and define the relation between attributes of post release movies using correlation coefficient.Comment: 6 page

An Agent-based Grouping Strategy for Federated Grid Computing

Characterizing users based on their requirements and forming groups among providers accordingly to deliver them the stronger quality of service is a challenge for federated grid community Federated grid computing allows providers to behave cooperatively to ensure required utility by users Grouping grid providers under such an environment thus enhance the possibility of more jobs executed whereas a single provider or organization might not be able to do the same In this paper we propose an agent-based iterative Contract Net Protocol which supports in building federated grid via negotiating distributed providers The main focus of this paper is to minimize the number of iterations using a grouping mechanism Minimizing the number of iterations would produce less communication overhead which results in the minimum queue waiting time for users to publish their jobs Simulation results further ensure the feasibility of our approach in terms of profit and resource utilization compared to that of the traditional non-grouped marke

ResEMGNet: A Lightweight Residual Deep Learning Architecture for Neuromuscular Disorder Detection from Raw EMG Signals

Amyotrophic Lateral Sclerosis (ALS) and Myopathy are debilitating neuromuscular disorders that demand accurate and efficient diagnostic approaches. In this study, we harness the power of deep learning techniques to detect ALS and Myopathy. Convolutional Neural Networks (CNNs) have emerged as powerful tools in this context. We present ResEMGNet, designed to identify ALS and Myopathy directly from raw electromyography (EMG) signals. Unlike traditional methods that require intricate handcrafted feature extraction, ResEMGNet takes raw EMG data as input, reducing computational complexity and enhancing practicality. Our approach was rigorously evaluated using various metrics in comparison to existing methods. ResEMGNet exhibited exceptional subject-independent performance, achieving an impressive overall three-class accuracy of 94.43\%

Uncharted Universe of Educational Technology: Potential Awaits

Educational technological tools are now an integral part of the education industry. Various platforms used for educational purposes were analyzed to find the perception of the learner; however, the major analyzing trends revolve around Zoom, Google meet, Google Classroom, and Institutional LMS, overlooking the evaluation of the perception of Teachly: an Ed-tech application developed by Harvard Kennedy School. The objective of this study is to determine the perception of students at Stamford University (n = 36) who enrolled and completed a semester at Teachly using descriptive statistics. For precision, a slider scale was used to collect data using the Google form in a semi-structured questionnaire. The data were then analyzed using the mean and standard deviation to find the central tendency and the measure of variability. The analysis confirms that the student has a positive perception towards using Teachly covering Walgito’s three components of perception, and it also points out some limitations identified by the student which hampers its future implementation

An Interpretable Systematic Review of Machine Learning Models for Predictive Maintenance of Aircraft Engine

This paper presents an interpretable review of various machine learning and deep learning models to predict the maintenance of aircraft engine to avoid any kind of disaster. One of the advantages of the strategy is that it can work with modest datasets. In this study, sensor data is utilized to predict aircraft engine failure within a predetermined number of cycles using LSTM, Bi-LSTM, RNN, Bi-RNN GRU, Random Forest, KNN, Naive Bayes, and Gradient Boosting. We explain how deep learning and machine learning can be used to generate predictions in predictive maintenance using a straightforward scenario with just one data source. We applied lime to the models to help us understand why machine learning models did not perform well than deep learning models. An extensive analysis of the model's behavior is presented for several test data to understand the black box scenario of the models. A lucrative accuracy of 97.8%, 97.14%, and 96.42% are achieved by GRU, Bi-LSTM, and LSTM respectively which denotes the capability of the models to predict maintenance at an early stage

Building Pattern Technique of an Indigenous Community – Does Its Appearances a Distinctive Representation?

Bangladesh is enriched with beautiful traditional indigenous cultures. Different indigenous peoples with their distinctive existences also considerably create an enhance values and lifestyles to the socio-cultural sectors of Bangladesh [1]. Habitually, these indigenous communities have been comparable to live a large combined family to shear their lifestyles [2]. Presently the country has 45 indigenous communities who are living in different locations. All indigenous people within this country have their own style to build their settlements with special techniques to keep them safe and sound from all types of natural and environmental vulnerabilities and also enhance their knowledge of construction techniques and lifestyle. Rakhain is one of them with very small number of people are still living in different regions within the country which have their own system of building techniques. Study found that for several hundreds of years Rakhains are strictly following their indigenous prescription of house and settlement pattern. Although like other indigenous people of this country, they have mountains of problems, such as forced land occupation, lack of security and minority characteristics. Above all, forced political separation has gradually drowned them in the abysmal pit of marginal destiny. This has turned them into exiles in their own land. As a result, many of them are being forced to leave the country and as a result they misplaced their native knowledge and technique to construct. Thus, this study will initially focus on to search for the distinctiveness of their settlement pattern and building construction techniques and lifestyle. Again, in view of their problems, knowledge and experiences concerning archetype, built and house pattern, this study will finally explain how Rakhains accumulate their every distinctiveness from history and for present and future invention