International Journal of Information Technology and Computer Science @ijitcs
Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1243
Статья научная
The computational intelligence such as artificial neural network (ANN) and fuzzy inference system (FIS) is a strong tool for prediction and simulation in engineering applications. In this paper, radial basis function (RBF) network and adaptive neuro-fuzzy inference system (ANFIS) are used for prediction of IC50 (the 50% inhibitory concentration) values evaluated by the MTT assay in human cancer cell lines. For developing of the proposed models, the input parameters are the concentration of the drug and the types of cell lines and the output is IC50 values in the A549, H157, H460 and H1975 cell lines. The predicted IC50 values using the proposed RBF and ANFIS models are compared with the experimental data. The obtained results show that both RBF and ANFIS models have achieved good agreement with the experimental data. Therefore, the proposed RBF and ANFIS models are useful, reliable, fast and cheap tools to predict the IC50 values determined by the MTT assay in human cancer cell lines.
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The Use of PLC Technology in Broadband Services Offered to Households
Статья научная
The volume and quality of broadband services in any country is being understood not only as a major parameter related to the economic growth of that country, but also as a major parameter how much that country is ready for economic growth in the near future. With this understanding the main reasons why expansion of broadband services in Slovakia is an urgent issue are briefly outlined in this paper.
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The Web Navigability Structure of E- Banking in India
Статья научная
The recent exponential growth of Internet has made the online banking very popular .It has become integral part of life for many people. But still the majority of people have probably not even tried it yet possibly because the websites of the banks are too complicated to understand and navigate. It has therefore become important to evaluate the quality of the banking websites. Most of the studies in the literature on banking websites have focused on evaluating the quality of services of these websites. In this paper we have investigated the structural properties of the websites with emphasis on navigability study of these business sites. Also evaluated the correlation between the navigability, popularity and importance of the Web sites.
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The effects of mobile phone use on human behaviors: a study of developing country like Bangladesh
Статья научная
The mobile phone is an essential part for human life all over the world. From developed to developing, developing to under developed countries are affected by the mobile phone usages, each and every corner of this universe. In every single minute we are using mobile phone for our various purposes. Even when there is no purpose we are also just using mobile phone. This scenario is almost same all over the world. The mobile phone has been affected the human behavior and changed the nature of behavior in developing countries. This paper has divided into three parts first part gathered information; second part analyses collected information and third part draw conclusions. In this paper, the result of the mobile phone used and effect on human behaviors in developing country like Bangladesh has been presented. This paper will also analyze this issue by exploring the exiting literature related to the mobile phone usages on human behavior and effect of the mobile phone on society in developing countries. Finally, this paper was presented some efficient solutions to minimize the problems in developing countries.
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The impact of feature selection techniques on the performance of predicting Parkinson’s disease
Статья научная
Parkinson’s Disease (PD) is one of the leading causes of death around the world. However, there is no cure for this disease yet; only treatments after early diagnosis may help to relieve the symptoms. This study aims to analyze the impact of feature selection techniques on the performance of diagnosing PD by incorporating different data mining techniques. To accomplish this task, identifying the best feature selection approach was the primary focus. In this paper, the authors had applied five feature selection techniques namely: Gain Ratio, Kruskal-Wallis Test, Random Forest Variable Importance, RELIEF and Symmetrical Uncertainty along with four classification algorithms (K-Nearest Neighbor, Logistic Regression, Random forest, and Support Vector machine) on the PD dataset collected from the UCI Machine Learning repository. The result of this study was obtained by taking the four different subsets (Top 5, 10, 15, and 20 features) from each feature selection approach and applying the classifiers. The obtained result showed that in terms of accuracy, Random Forest Variable Importance, Gain Ratio, and Kruskal-Wallis Test techniques generated the highest 89% score. On the other hand, in terms of sensitivity, Gain Ratio and Kruskal-Walis Test approaches produced the highest 97% score. The findings of this research clearly indicated the impact of feature selection techniques on predicting PD and our applied methods outperformed the state-of-the-art performance.
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The impact of web contents color contrast on human psychology in the lens of HCI
Статья научная
Web contents include text, image, and any visual element that represents in web applications. Users conduct web applications throughout visual contents; therefore, the contents should visible clearly and follow a strict contrast ratio to differentiate from the other contents of the application. The color contrast assists to visualize contents combining the contrast ratio between background and foreground. Whether the web contents not visible clearly or overpass to split its color contrast from the background shall be worthless, and in addition, the human brain and psychology have an impact of colors which lead physiologically effects such as feelings and senses. Numerous web applications existing on the web and some applications failed to follow the design principles of Human-Computer Interaction (HCI). In HCI, visualization is the most widespread research area and, in the context of visual interaction, the HCI facilitates and guides application design that to be user-centric. This research reveals the HCI for color effects on the human eye, brain, phycology, and contrast ratio. Also extended the existing standard minimum contrast ratio for the design of web contents in light and dark background and foreground following HCI principles. The extended ratio experimented on a web application contents to differentiate the accuracy between the existing and the extended ratio.
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Time and Accuracy Analysis of Skew Detection Methods for Document Images
Статья научная
Detecting skew angle in a document image has been an area of research interest for a long time. This paper presents an experimental analysis of various existing skew detection techniques involving methods such as Radon transform, Hough transform, Principal Component Analysis (PCA), PCA with Wavelet transform and Moments with Wavelet transform. Detailed analysis of existing skew detection method against the parameters time complexity, space complexity, robustness, accuracy, flexibility, etc. has been carried out for seven different categories of digital documents. The categories of these documents spans from those containing handwritten text in different languages, to the ones with both text and pictures. Radon transform is observed to be the fastest method when the image size is small and works with virtually all types of documents. It is an accurate method as well as works faster, even with the document containing pictures. PCA method is also faster than Hough transform for machine printed documents but used less for real time skew distortion due to its limitations. If the document image size is large, then Moments with Wavelet transform has better time complexity than other methods, but do not work well with documents containing images. Hough transform is the most accurate method, though it is computationally expensive.
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Time effective workflow scheduling using genetic algorithm in cloud computing
Статья научная
Cloud computing is service based technology on internet which facilitates users to access plenty of resources on demand from anywhere and anytime in a metered manner i.e. pay per usage without paying much heed to the maintenance and implementation details of application. As cloud technology is evolving day by day it is being confronted by numerous challenges, such as time and cost under deadline constraints. Research work done so far mainly focused on reducing cost as well as execution time. In order to minimize cost and execution time previously existing workflow scheduling model known as predict earliest finish time is used. In this research work we have proposed a new PEFT genetic algorithm approach to further reduce the execution time on this model. A strategy is developed to let GA focus on to optimize chromosomes objective to get best suitable mutated children. After obtaining a feasible solution, the genetic algorithm focuses on optimizing the execution time. Experimental results show that our algorithm can find better solution within lesser time.
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Time-Delay Neural Network for Smart MIMO Channel Estimation in Downlink 4G-LTE-Advance System
Статья научная
Long-Term Evolution (LTE) is the next generation of current mobile telecommunication networks. LTE has a new flat radio-network architecture and significant increase in spectrum efficiency. In this paper, main focus on throughput performance analysis of robust MIMO channel estimators for Downlink Long Term Evolution-Advance (DL LTE-A)-4G system using three Artificial Neural Networks: Feed-forward neural network (FFNN), Cascade-forward neural network (CFNN) and Time-Delay neural network (TDNN) are adopted to train the constructed neural networks’ models separately using Back-Propagation Algorithm. The methods use the information received by the received reference symbols to estimate the total frequency response of the channel in two important phases. In the first phase, the proposed ANN based method learns to adapt to the channel variations, and in the second phase, it estimates the MIMO channel matrix and try to improve throughput of LTE. The performance of the estimation methods is evaluated by simulations in Vienna LTE-A DL Link Level Simulator. Performance of the proposed channel estimator, Time-Delay neural network (TDNN) is compared with traditional Least Square (LS) algorithm and ANN based other estimators for Closed Loop Spatial Multiplexing (CLSM) - Single User Multi-input Multi-output (MIMO-2×2 and 4×4) in terms of throughput. Simulation result shows TDNN gives better performance than other ANN based estimations methods and LS.
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Toward Grasping the Dynamic Concept of Big Data
Статья научная
The idea of Big Data represents a growing challenge for companies such as Google, Yahoo, Bing, Amazon, eBay, YouTube, LinkedIn, Facebook, Instagram, and Twitter. However, the challenge goes beyond private companies, government agencies, and many other organizations. It is actually an alarm clock that is ringing everywhere: newspapers, magazines, books, research papers, online, offline, it is all over the world and people are worried about it. Its economic impact and consequences are of unproportioned dimensions. This research outlines the fundamental literature required to understand the concept of Big Data. Additionally, the present work provides a conclusion and recommendations for further research on Big Data. This study is part of an ongoing research that addresses the link between Economic Growth and Big Data.
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Towards Automated Web Accessibility Evaluation: A Comparative Study
Статья научная
With each passing day, the Web is becoming increasingly important in our lives. Hence, the need of making it more accessible to everyone, especially for the disabled and elderly spurred a great interest in automated tools, the total registered number of which has been continuously increasing and reached from forty-five software bids in 2014 to ninety-three in 2017. The purpose of this empirical research is to assess and compare eight popular and free online automated Web accessibility evaluation tools (AWAETs) such as AChecker, Cynthia Says, EIII Checker, MAUVE, SortSite, TAW, Tenon and WAVE with regard to the WCAG 2.0 conformance. As a result, significant differences were observed in terms of tool’s coverage (a maximum of 32.4%), completeness (ranges between 10% and 59%), correctness (an average of 70.7%), specificity (reaches 32%), inter-reliability (lies between 1.56% and 18.32%) and intra-reliability (the acceptable score), validity, efficiency and capacity. These eight criteria can help to determine a new role played by modern AWAETs as dependent methods in Web accessibility evaluation. Moreover, consequences of relying on AWAETs alone are quantified and concluded that applying such approaches is a great mistake since subjective and less frequent objective success criteria (SC) failed to be automated. However, using a good combination of AWAETs is highly recommended as overall results in all the mentioned quality criteria are maximized and tools could definitely validate and complete each other. Ultimately, integrating automated methods with the others is ideal and preferably at an early stage of the website development life cycle. The study also provides potential accessibility barriers that make websites inaccessible, challenges AWAETs are currently facing, nineteen pros and fourteen cons and fifteen improvement recommendations for the existing and next generation of AWAETs. Fundamentally, achieving the objectives of this study was possible due to the elaboration and implementation of a new five-phased methodology named as “5PhM-for-AWAEMs” for successful selection, evaluation and/or comparison of AWAEMs. In addition to providing detailed descriptions of the estimation process, this methodology represents eleven key criteria for effective selection of suitable AWAEMs and necessary numbers of web pages and expert evaluators for acceptable, normal or ideal assessment.
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Статья научная
Solid Waste Management is an especially important task related to human health and the environment. Due to ineffective scheduled date & time, poor communication between waste collecting institutions and local house owners, people are compelled to throw waste on streets which is not good. Even if there is a routine, people tend to miss the schedule. Our aim is to develop an application for mobile phones, which consists of two parties- the user and waste management officials, where the second one acts as reminders. Officials will send a notification to the user, signaling that they are at a certain checkpoint near the user and the user can now throw waste properly and not on the streets. An incremental model was used throughout our project; basic requirements are fulfilled first and then iterated to create the final product. The proposed application includes two portals for whether you are user or waste management personnel. This application helps to improve the coordination between clients and collectors and determines whether the waste in an area has been collected or not. The survey conducted in this study involved consulting the Environment and Agricultural Department of Kathmandu Metropolitan City, which highlighted the significance of a notifying application. This application addresses the issue of uncoordinated waste disposal by providing users with information about collection schedules, leading to better waste management practices and reduced unsystematic garbage disposal.
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Towards Improving the Quality of Mobile App Reviews
Статья научная
Mobile app reviews are gaining importance as a crowd source to improve the quality of mobile apps. Mobile app review systems are providing a platform for users to share their experiences and to support in decision making for a certain app. Developers, on the other side, are utilizing the review system to get real-life user experience as a source of improving their apps. This paper has analyzed existing review system and proposed few recommendations for the current review system to improve the quality of app reviews. The proposed review system can help for collection and analysis of user reviews to make it more meaningful with less intensive data mining techniques. The proposed system can help the end users to get an overview of mobile apps. The recommendations in this paper are derived from the existing literature related to app reviews and will help to improve the current review systems for better app reviews from users as well as developers perspective.
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Статья научная
The Agent Petri Nets (APN) formalism provides a set of adapted and specific tools, relations and functions for modeling multi-agent systems (MAS). However, there is a lack of tools for verifying the APN models. In order to fill some of these gaps, we propose in this paper, a meta-modeling approach based on the Model Driven Architecture (MDA). The Eclipse Modeling Framework (EMF) permits to define a generic APN Meta-model in Ecore informal format. Its abstraction level is very high, it offers as a basis for developing system models dedicated to various specific domains. In addition, the Object Constraint Language (OCL) aims to increase the structural verification level of the model and the Graphical Modeling Framework (GMF), for its part, is concerned with generating a graphical editor associated with the APN meta-model. Thus, we combine the rigor of APN formalism with the power of the MDA-based meta-modeling tools for verifying APN models.
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Towards data resilience: the analytical case of crypto ransomware data recovery techniques
Статья научная
Crypto ransomware has earned an infamous reputation in the malware landscape and its sound sends a lot of shivers to many despite being a new entrant. The media has not helped matters even as the myths and inaccuracies surrounding crypto ransomware continue to deepen. It’s been purported that once crypto ransomware attacks, the victim is left with no option but to pay in order to retrieve the encrypted data, and that without a guarantee, or risk losing the data forever. Security researchers are inadvertently thrown into a cat-and-mouse chase to catch up with the latest vices of the aforesaid in order to provide data resilience. In this paper, we debunk the myths surrounding loss of data via a crypto ransomware attack. Using a variety of crypto ransomware samples, we employ reverse engineering and dynamic analysis to evaluate the underlying attack structures and data deletion techniques employed by the ransomware. Further, we expose the data deletion techniques used by ransomware to prevent data recovery and suggest how such could be countered. From the results, we further present observed sandbox evasion techniques employed by ransomware against both static and dynamic analysis in an effort to obfuscate its operations and subsequently prevent data recovery. Our analyses have led us to the conclusion that no matter how devastating a crypto ransomware attack might appear, the key to data recovery options lies in the underlying attack structure and the implemented data deletion methodology.
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Статья научная
This survey study evaluates the peoples’ acceptance and comfortability on accessing the HIV/AIDS healthcare information through visiting HIV/AIDS Care and Treatment Centers (CTCs). Furthermore, the survey examines whether a mobile application platform can be the way forward towards enhancing HIV/AIDS healthcare information delivery in Tanzania. This study was conducted in Dar es Salaam city in Tanzania. The structured questionnaire-based survey was carried-out involving a total of 208 respondents, among them, 45 were the HIV/AIDS healthcare practitioners. The collected data were analyzed by using WEKA and Python computer programming software. The study findings indicated that: 24.5% of the respondents claimed that they were not comfortable going to HIV/AIDS CTCs indicating that they were afraid of being exposed and stigmatized; almost one-third (31.3%) of respondents prefer to seek HIV/AIDS related information from online sources; 78.5% of respondents preferred to have an official mobile application for access the HIV/AIDS healthcare information; 64.4% of HIV/AIDS practitioners indicated the need of having a mobile application platform for HIV/AIDS healthcare information delivery; and more than two-third of HIV/AIDS practitioners claimed to be able to serve people with HIV/AIDS healthcare information online. It is concluded that there is a need for the HIV/AIDS healthcare providers to have a mobile application platform for HIV/AIDS healthcare information delivery. The mobile application platform will consequently help people to confidentially access the HIV/AIDS healthcare information in their mobile electronic gadgets frequently without fear of being exposed as if they would frequently visit CTCs.
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Tracking Area Boundary-aware Protocol for Pseudo Stochastic Mobility Prediction in LTE Networks
Статья научная
Accurate mobility prediction enables efficient and faster paging services in these networks. This in turn facilitates the attainment of higher bandwidths and execution of activities such as handovers at low latencies. The conventional mobility prediction models operate on unrealistic assumptions that make them unsuitable for cellular network mobile station tracking. For instance, the Feynman-Verlet, first order kinetic model and Random Waypoint assume that mobile phones move with constant velocity while Manhattan, Freeway, city area, street unit, obstacle mobility, and pathway mobility postulate that mobile station movement is restricted along certain paths. In addition, obstacle mobility model speculate that the mobile station signal is completely absorbed by an obstacle while random walk, random waypoint, Markovian random walk, random direction, shortest path model, normal walk, and smooth random assume that a mobile station can move in any direction. Moreover, the greatest challenge of the random direction model is the requirement that a border behavior model be specified for the reaction of mobile stations reaching the simulation area boundary. In this paper, a protocol that addresses the border behavior problem is developed. This protocol is shown to detect when the subscriber has moved out of the current tracking area, which is crucial during handovers.
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Traffic Accident Analysis Using Decision Trees and Neural Networks
Статья научная
This work employed Artificial Neural Networks and Decision Trees data analysis techniques to discover new knowledge from historical data about accidents in one of Nigeria’s busiest roads in order to reduce carnage on our highways. Data of accidents records on the first 40 kilometres from Ibadan to Lagos were collected from Nigeria Road Safety Corps. The data were organized into continuous and categorical data. The continuous data were analysed using Artificial Neural Networks technique and the categorical data were also analysed using Decision Trees technique .Sensitivity analysis was performed and irrelevant inputs were eliminated. The performance measures used to determine the performance of the techniques include Mean Absolute Error (MAE), Confusion Matrix, Accuracy Rate, True Positive, False Positive and Percentage correctly classified instances. Experimental results reveal that, between the machines learning paradigms considered, Decision Tree approach outperformed the Artificial Neural Network with a lower error rate and higher accuracy rate. Our research analysis also shows that, the three most important causes of accident are Tyre burst, loss of control and over speeding.
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Статья научная
Traffic signs are symbols erected on the sides of roads that convey the road instructions to its users. These signs are essential in conveying the instructions related to the movement of traffic in the streets. Automation of driving is essential for efficient navigation free of human errors, which could otherwise lead to accidents and disorganized movement of vehicles in the streets. Traffic sign detection systems provide an important contribution to automation of driving, by helping in efficient navigation through relaying traffic sign instructions to the system users. However, most of the existing techniques have proposed approaches that are mostly capable of detection through static images only. Moreover, to the best of the author’s knowledge, there exists no approach that uses video frames. Therefore, this article proposes a unique automated approach for detection and recognition of Bangladeshi traffic signs from the video frames using Support Vector Machine and Histogram of Oriented Gradient. This system would be immensely useful in the implementation of automated driving systems in Bangladeshi streets. By detecting and recognizing the traffic signs in the streets, the automated driving systems in Bangladesh will be able to effectively navigate the streets. This approach classifies the Bangladeshi traffic signs using Support Vector Machine classifier on the basis of Histogram of Oriented Gradient property. Through image processing techniques such as binarization, contour detection and identifying similarity to circle etc., this article also proposes the actual detection mechanism of traffic signs from the video frames. The proposed approach detects and recognizes traffic signs with 100% precision, 95.83% recall and 96.15% accuracy after running it on 78 Bangladeshi traffic sign videos, which comprise 6 different kinds of Bangladeshi traffic signs. In addition, a public dataset for Bangladeshi traffic signs has been created that can be used for other research purposes.
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Traffic Sign Detection and Recognition Using Yolo Models
Статья научная
With the proliferation of advanced driver assistance systems and continued advances in autonomous vehicle technology, there is a need for accurate, real-time methods of identifying and interpreting traffic signs. The importance of traffic sign detection can't be overstated, as it plays a pivotal role in improving road safety and traffic management. This proposed work suggests a unique real-time traffic sign detection and recognition approach using the YOLOv8 algorithm. Utilizing the integrated webcams of personal computers and laptops, we capture live traffic scenes and train our model using a meticulously curated dataset from Roboflow. Through extensive training, our YOLOv8 version achieves an excellent accuracy rate of 94% compared to YOLOV7 at 90.1% and YOLOv5 at 81.3%, ensuring reliable detection and recognition across various environmental conditions. Additionally, this proposed work introduces an auditory alert feature that notifies the driver with a voice alert upon detecting traffic signs, enhancing driver awareness and safety. Through rigorous experimentation and evaluation, we validate the effectiveness of our approach, highlighting the importance of utilizing available hardware resources to deploy traffic sign detection systems with minimal infrastructure requirements. Our findings underscore the robustness of YOLOv8 in handling challenging traffic sign recognition tasks, paving the way for widespread adoption of intelligent transportation technologies and fostering the introduction of safer and more efficient road networks. In this paper, we compare the unique model of YOLO with YOLOv5, YOLOv7, and YOLOv8, and find that YOLOv8 outperforms its predecessors, YOLOv7 and YOLOv5, in traffic sign detection with an excellent overall mean average precision of 0.945. Notably, it demonstrates advanced precision and recall, especially in essential sign classes like "No overtaking" and "Stop," making it the favored preference for accurate and dependable traffic sign detection tasks.
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