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International Journal of Information Engineering and Electronic Business @ijieeb
Статьи журнала - International Journal of Information Engineering and Electronic Business
Все статьи: 626
Estimation of Possible Profit/ Loss of a New Movie Using “Natural Grouping” of Movie Genres
Статья научная
Film industry is the most important component of entertainment industry. A large amount of money is invested in this high risk industry. Both profit and loss are very high for this business. Thus if the production houses have an option to know the probable profit/loss of a completed movie to be released then it will be very helpful for them to reduce the said risk. We know that artificial neural networks have been successfully used to solve various problems in numerous fields of application. For instance backpropagation neural networks have successfully been applied for Stock Market Prediction, Weather Prediction etc. In this work we have used a backpropagation network that is being trained using a subset of data points. These subsets are nothing but the “natural grouping” of data points, being extracted by an MST based clustering methods. The proposed method presented in this paper is experimentally found to produce good result for the real life data sets considered for experimentation.
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Статья научная
Evaluation of water reuse options is also one of the applications of multi-criteria decision-making (MCDM) problems. In this paper, we refer to a new method for selecting the best water reuse option in the available options by using picture fuzzy MCDM.
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Evaluation of Load Balancing Performance of Parallel Processing Linear Time-Delay Systems
Статья научная
Time delays in system states or control may result into unacceptable system operation or uncertainty in specialised technical systems like aircraft control, plant control, robotics etc. The issue of robustness, controllability, traceability, flexible management, reliability, and safety of such systems with time-delays, has been one of the primary research focuses of the last few decades. In parallel computing, different computing subunits share their tasks to balance loads to increase performance and throughput. In order to do so, subsystems have to communicate among themselves, adding further delay on top of existing system delay. It is possible to maintain performance and stability of the whole system, by designing observer for every subsystem in the system, overseeing the system state and compensating for existing time-delay. This paper reviews present literature to identify a linear time-delay system for load balancing and evaluates the stability and load balancing performance of the system with and without an observer. Stability is analysed in terms of oscillation in the system responses and performance is evaluated as the speed of load-balancing operation.
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Evaluation of different machine learning methods for caesarean data classification
Статья научная
Recently, a new dataset has been introduced about the caesarean data. In this paper, the caesarean data was classified with five different algorithms; Support Vector Machine, K Nearest Neighbours, Naïve Bayes, Decision Tree Classifier, and Random Forest Classifier. The dataset is retrieved from California University website. The main objective of this study is to compare selected algorithms’ performances. This study has shown that the best accuracy that was for Naïve Bayes while the highest sensitivity which was for Support Vector Machine.
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Evolution of NFT Marketplace for Influencing Buying Behaviour in Sportswear Industry
Статья научная
The advent of metaverses, NFTs, blockchain, and similar growth topics influence the future of businesses, and the industry is looking at a transformation. SAP has been a pioneer in innovation and enables some of the best businesses around the World. With its own Cloud infrastructure in place and expertise in areas of Blockchain etc., it’s time to take a leap and give the existing and new customers an edge over the others. The solution proposed here is to address how the future could look for retail, for example, in sportswear. It is possible to provide an NFT Marketplace for sportswear manufacturers like Nike, where the subscribers can sell their NFTs (digital shoes, wearables, etc.) or buy the ones available in the marketplace with the help of Crypto Wallets/Payments, etc. The idea proposed in this paper helps sellers’ who design products to have a review process, where the company can evaluate if they want to introduce those products in their physical form. Or, if a digital product sells well in the NFT marketplace, the company can decide to make a physical launch of the product as well, sharing some royalty.
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Статья научная
73rd Panchayati Raj Act came into existence in 1993 and it paved the way for a strong and effective decentralized administrative system in India. The Indian Constitution added 11th schedule to it detailing 29 subjects which are devolved to the local self-government institutions [LSGIs]. It is nearly two decades passed and still LSGIs are hesitant to adapt with their roles to develop them as self-sufficient administrating regions. The devolved function is not fully exercised by the LSGIs. The transferred the departments connected with the 29 subjects are still working in a bureaucratic manner. This study reveals the real reasons behind the poor performance of LSGIs and coming up with a technological solution to overcome the problem through an interactive E-Governance system. Even though 29 subjects are given to the LSGIs, 4 departments are considered in this research due to time constrains.
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Experimental Analysis of Xie and Kuek's Traffic Model with Handoff Scheme in Wireless Networks
Статья научная
Mobility becomes a distinct feature for a wireless mobile cellular system. For the traffic which is non stationary and is away from the base station, the chances of a call to be handed off are increased. In urban mobile cellular systems, especially when the cell size becomes relatively small, the procedure has a significant impact on system performance. Blocking probability of originating calls and the forced termination probability of ongoing calls are the primary criteria for indicating this performance. In this paper, we report our recent work on closed form solutions to the blocking probability followed by dropping probability in wireless cellular networks with . First, we develop a performance model of a cell in a wireless network where the effect of arrivals and the use of guard channels are included. Then we simulate the Traffic Model with exploiting our model.
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Exploration on Quick Response (QR) Code Behaviour in Commerce based Platforms Using Machine Learning
Статья научная
The "rapid response" code, or QR code, is made to quickly decode vast amounts of data. Any managed device, such as a smartphone, is able to capture it, and it is simple to access simply scanning the 2D matrix code. The dataset is analyzed utilizing machine learning techniques, such as the confusion matrix score utilized for the multinomial naive Bayes algorithm's performance analysis. The QR code generation is limited to single product and is extended now to include all products. Due to its ability to provide clients with benefits including speedy, error-free access and the ability to store a lot of data. Generally, many people are using the online payment for any transaction for flexibility and one can do at any place at any time. For bulk or huge payment, cash is not a good option. Hence many retailers join in the e-wallet companies and make their payment so flexible and faster transaction. Because of these benefits, QR code has becoming widespread.
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Exploring Semantic Relatedness in Arabic Corpora using Paradigmatic and Syntagmatic Models
Статья научная
In this paper we explore two paradigms: firstly, paradigmatic representation via the native HAL model including a model enriched by adding word order information using the permutation technique of Sahlgren and al [21], and secondly the syntagmatic representation via a words-by-documents model constructed using the Random Indexing method. We demonstrate that these kinds of word space models which were initially dedicated to extract similarity can also been efficient for extracting relatedness from Arabic corpora. For a given word the proposed models search the related words to it. A result is qualified as a failure when the number of related words given by a model is less than or equal to 4, otherwise it is considered as a success. To decide if a word is related to other one, we get help from an expert of the economic domain and use a glossary1 of the domain. First we begin by a comparison between a native HAL model and term- document model. The simple HAL model records a better result with a success rate of 72.92%. In a second stage, we want to boost the HAL model results by adding word order information via the permutation technique of sahlgren and al [21]. The success rate of the enriched HAL model attempt 79.2 %.
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Статья научная
Businesses nowadays may save a significant amount of money by using technological solutions. It is impossible to deny this when considering the expenses of acquiring and training new personnel. When faced with such difficulties, technology is virtually always able to assist. Business Intelligence/Machine Learning (BI/ML) is an essential tool in today's decision-making process because of the many issues it has created for contemporary business decision-making. A comparative study of regression models, including linear regression, random forests, and gradient boosting, could unravel their effectiveness in predictive analytics within BI. Machine learning contribution in businesses is vital as it has a strong link with business intelligence, and it helps business decision-making in businesses. Without machine learning, business intelligence is not practical while making decisions, as business owners can't make decisions effectively. This paper will comprehensively review the noteworthy contributions of Machine Learning and its Impact on Business Intelligence. Further, it will discuss the challenges and opportunities of machine learning in business intelligence. Finally, the paper will discuss future correspondence about machine learning in businesses.
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Статья научная
This paper tackles a critical issue emerging when planning the deployment of a wireless network in rural regions: the cost estimation. Wireless Networks have usually been presented as a cost-effective solution to bridge the digital divide between rural and urban regions. But this assertion is too general and does not give an insight about the real estimation of the deployment cost of such an infrastructure. Providing such a cost estimation framework may help to avoid underestimation or overestimation of required resources since the budget is almost always limited in rural regions. This work extends the Probabilistic Cost Model (PCM) that has been proposed. This model does not take into account the difference in the costs of unexpected events. To extend the PCMfirst, a list of unexpected events that can occur when deploying Wireless Networks has been established. This list is based on data from past projects and a set of unexpected events that can occur. Afterwards, the standard deviation and the average have been computed for each unexpected event. The Poisson process has been therefore used to predict the number of unexpected events that may occur during the network deployment. This approach led to the proposal of a model that gives an estimation of the total cost of contingencies, which takes into account the probability that the total cost of unexpected events does not exceed a given contingency. The evaluation of the proposed model on a given dataset provided a good accuracy in the prediction of the cost induced by unexpected events.
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Extended Proxy Mobile IPv6 Scheme Using Global Local Mobility Anchor
Статья научная
The internet was basically designed for the static nodes, but with the development of mobile nodes such as smart phones, that have wireless capabilities, the first design was insufficient. MNs change their point of attachment while they are roaming (traveling) in the internet, to maintain the survival of ongoing sessions for these mobile nodes, the internet requires techniques for managing mobility.. Currently, there are two types of mobility management protocols, host-based protocols and network-based protocols, the involvement of MN in the mobility process is must in the first type, while is unnecessary in the second type. The IETF standardized the Proxy Mobile IPv6 (PMIPv6) protocol in 2004, to overcome the limitations experienced by the host-based protocol, Mobile IPv6 (MIPv6) such as sub-optimal routing, handover latency, packet loss and single point of failure, however, the biggest drawback of PMIPv6 is the lack of inter-domain handover. This paper provides an efficient scheme based on standard PMIPv6 called (E-PMIPv6) to support inter-domain handover by introducing a new entity called (GLMA) which enables MN to traverse different domains while keeping the ongoing sessions, additionally; we use buffering techniques to pre-emptively lighten packet losses. The ultimate goal for the suggested scheme is to solve the scalability problem for the PMIPv6 and it is extensions to encourage network operators to deploy E-PMIPv6 for large networks. Results of preliminary analysis of handover latencies and related packet losses favored (E-PMIPv6) over two of the leading contenders.
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Extracting Feature Curves on Point Sets
Статья научная
We present an effective algorithm for detecting feature curves on point sets. Based on the local surface fitting method, our algorithm first compute the curvatures and principal directions of each point of point sets. The algorithm then extracts potential feature points according to the biggist principal curvature of the point, and evaluates the principal directions of the detected points. By projecting the points onto the principal axes of their neighborhoods, the potential feature points are smoothed. Using the principal directions with each optimized point, feature curves are generated by polyline growing along the principal directions of feature points. The results indicate that our algorithm is sensitive to both sharp and smooth feature curves of point set, and it supports multi-resolution extraction of features.
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Статья научная
Although remarkable success has been achieved by genome-wide association (GWA) studies over the past few years, genetic variants discovered in GWA studies can typically account for only a small fraction of heritability of most common diseases. As such, the identification of multiple rare variants that are associated with complex diseases has been receiving more and more attentions. However, most of the recently developed statistical approaches for detecting association of rare variants with diseases require the selection of functional variants before the successive analysis, making an effective bioinformatics method for filtering out non-relevant rare variants indispensible. In this paper, we focus on a specific type of genetic variants called single amino acid polymorphisms (SAAPs). We propose to prioritize candidate SAAPs for a specific disease according to their association scores that are calculated using a guilt-by-association model with a set of features derived from protein sequences. We validate the proposed approach in a systematic way and demonstrate that the proposed model is powerful in distinguishing disease-associated SAAPs for the specific disease of interest.
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Extractive based text summarization using k-means and TF-IDF
Статья научная
The quantity of information on the internet is massively increasing and gigantic volume of data with numerous compositions accessible openly online become more widespread. It is challenging nowadays for a user to extract the information efficiently and smoothly. As one of the methods to tackle this challenge, text summarization process diminishes the redundant information and retrieves the useful and relevant information from a text document to form a compressed and shorter version which is easy to understand and time-saving while reflecting the main idea of the discussed topic within the document. The approaches of automatic text summarization earn a keen interest within the Text Mining and NLP (Natural Language Processing) communities because it is a laborious job to manually summarize a text document. Mainly there are two types of text summarization, namely extractive based and abstractive based. This paper focuses on the extractive based summarization using K-Means Clustering with TF-IDF (Term Frequency-Inverse Document Frequency) for summarization. The paper also reflects the idea of true K and using that value of K divides the sentences of the input document to present the final summary. Furth more, we have combined the K-means, TF-IDF with the issue of K value and predict the resulting system summary which shows comparatively best results.
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Статья научная
The implementation of face recognition with TensorFlow deep learning uses the webcam as a surveillance camera on the Raspberry Pi, aiming to provide a sense of security to the requiring party. A frequent surveillance camera problem is that crimes are performed at certain hours, the absence of early warning features, and there is no application of facial recognition on surveillance cameras. The function of this system is to perform facial recognition on every face captured by the webcam. Use the Histogram of the Oriented Gradient (HOG) method for the extraction process of deep learning. The image that is input from the camera will undergo a gray scaling process, then it will be taken the extraction value and classified by deep learning framework with TensorFlow. The system will send notifications when faces are not recognized. Based on the analysis of the data is done, the conclusion that the implementation of face recognition is built on the Raspberry Pi using a Python programming language with the help of TensorFlow so that the training process of the sample is much faster and more accurate. It uses a Graphical User Interface (GUI) as the main display and is built using Python designer, using email as an initial warning delivery medium to the user as well as using the webcam as the main camera to capture image.
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Failures in Cloud Computing Data Centers in 3-tier Cloud Architecture
Статья научная
This paper presents an methodology for providing high availability to the demands of cloud's clients. To succeed this objective, failover approaches for cloud computing using combined checkpointing procedures with load balancing algorithms are purposed in this paper. Purposed methodology assimilate checkpointing feature with load balancing algorithms and also make multilevel barrier to diminution checkpointing overheads. For execution of purposed failover approaches, a cloud simulation environment is established, which the ability to provide high availability to clients in case of disaster/recovery of service nodes. Also in this paper comparison of developed simulator is made with existing approaches. The purposed failover strategy will work on application layer and provide highly availability for Platform as a Service (PaaS) feature of cloud computing.
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Статья научная
The key of fast identification algorithm of time-varying modal parameter based on subspace tracking is to find efficient and fast subspace-tracking algorithm. This paper presents a modified version of NIC(Novel Information Criterion) adopted in two-layer linear neural network learning for subspace tracking, which is applied in time-varying modal parameter identification algorithm based on subspace tracking and get a new time-varying modal parameter identification algorithm. Comparing with the original subspace-tracking algorithm, there is no need to set a key control parameter in advance. Simulation experiments show that new time-varying modal parameter identification algorithm has a faster convergence in the initial period and a real experiment under laboratory conditions confirms further its validity of the time-varying modal identification algorithm presented in this paper.
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Feature Engineering based Approach for Prediction of Movie Ratings
Статья научная
The buying behavior of the consumer is grown nowadays through recommender systems. Though it recommends, still there are limitations to give a recommendation to the users. In order to address data sparsity and scalability, a hybrid approach is developed for the effective recommendation in this paper. It combines the feature engineering attributes and collaborative filtering for prediction. The proposed system implemented using supervised learning algorithms. The results empirically proved that the mean absolute error of prediction was reduced. This approach shows very promising results.
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Статья научная
Dimensionality reduction is generally performed when high dimensional data like text are classified. This can be done either by using feature extraction techniques or by using feature selection techniques. This paper analyses which dimension reduction technique is better for classifying text data like emails. Email classification is difficult due to its high dimensional sparse features that affect the generalization performance of classifiers. In phishing email detection, dimensionality reduction techniques are used to keep the most instructive and discriminative features from a collection of emails, consists of both phishing and legitimate, for better detection. Two feature selection techniques - Chi-Square and Information Gain Ratio and two feature extraction techniques – Principal Component Analysis and Latent Semantic Analysis are used for the analysis. It is found that feature extraction techniques offer better performance for the classification, give stable classification results with the different number of features chosen, and robustly keep the performance over time.
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