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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: 669
Investigation of Machine Learning Algorithms for Network Intrusion Detection
Статья научная
Network intrusion is an increasing major concern as we are rapidly advancing in technology. To detect network intrusion, Intrusion Detection Systems are required. Among the wide range of intrusion detection technologies, machine learning methods are the most appropriate. In this paper we investigated different machine learning techniques using NSL-KDD dataset, with steps of building a model. We used Decision Tree, Support Vector Machine, Random Forest, Naïve Bayes, Neural network, adaBoost machine leaning algorithms. At step one, one-hot-encoding is applied to convert categorical to numeric features. At step two, different feature scaling techniques, including normalization and standardization, are applied on these six selected machine learning algorithms with the encoded dataset. Further in this step, for each of the six machine learning algorithms, the better scaling technique application outcome is selected for the comparison in the next step. We considered six pairs of better scaling technique with each machine learning algorithm. Among these six scaling-machine learning pairs, one pair (Naïve Bayes) is dropped for having inferior performance. Hence, the outcome of this step is five scaling-machine learning pairs. At step three, different feature reduction techniques, including low variance filter, high correlation filter, Random Forest, Incremental PCA, are applied to the five scaling-machine learning pairs from step two. Further in this step, for each of the five scaling-machine learning pairs, the better feature reduction technique application outcome is selected for the comparison in the next step. The outcome of this step is five feature reduced scaling-machine learning pairs. At step four, different sampling techniques, including SMOTE, Borderline-SMOTE, ADASYN are applied to the five feature reduced scaling-machine learning pairs. The outcome of this step is five over sampled, feature reduced scaling-machine learning pairs. This outcome is then finally compared to find the best pairs to be used for intrusion detection system.
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Investigations of Cellular Automata Game of Life Rules for Noise Filtering and Edge Detection
Статья научная
In digital image processing, edge detection of images is an important and difficult task. Also, if the images are corrupted by noise, it smears some details and thus resulting in inaccurate edge detection. Hence, a pre-processing step must be taken before the edge detection. In this paper a new approach for edge detection with noise filtering of digital images using Cellular Automata Game of Life is presented. This procedure can easily be generalized and used for any type of digital media. To illustrate the proposed method, some experiments have been performed on standard test images and compared with popular methods. The results reveal that the proposed method has relatively desirable performance.
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IoT Based Smart Energy Consumption Prediction for Home Appliances
Статья научная
Optimizing energy management for household appliances is essential for maximizing domestic energy utilization and enabling preventive maintenance. Recent studies indicate that traditional forecasting approaches frequently lack the necessary accuracy and real-time learning capabilities required for effective management of household energy. This study demonstrates the implementation of a comprehensive strategy that integrates Internet of Things (IoT) data, machine learning (ML), and explainable artificial intelligence (XAI) to improve the accuracy and interpretability of predicting energy usage in residential buildings. Our research focuses on the rising issues faced by IoT-based smart systems, partic- ularly the deficiencies in the performance of current solutions. Therefore, as compared to the other 17 models that were examined, polynomial regression demonstrated outstanding performance. Our solution utilizes a non-intrusive sensor to collect data without disrupting its operation. Real-time data collecting is achieved through a Flask-based web page with Ngrok for external access.The efficacy of the proposed system was assessed using many metrics, yielding highly satisfac- tory results: the root mean square error (RMSE) was 0.03, the mean absolute error (MAE) was 0.02, the mean absolute percentage error (MAPE) was 0.04, and the coefficient of determination (R²) was 0.9989. However, modern cutting-edge methods still face considerable hurdles when it comes to interpretability. In order to tackle these problems, we include XAI techniques such as SHAP and LIME. Explainable Artificial Intelligence (XAI) improves the interpretability of the model by elucidating the impact of various variables on energy consumption forecasts. Not only does this increase the effectiveness of the model, but it also promotes comprehension of the data and enables them to identify the elements that influence home energy usage.
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Статья научная
Precision agriculture is revolutionizing the agricultural sector by integrating advanced technologies to enhance productivity and sustainability. In aquaculture, precision agriculture can significantly improve fish farming practices through precise monitoring and data-driven decision-making, addressing challenges such as optimizing resource usage and improving fish health. This paper presents the development and implementation of an IoT-based Fish Recommendation System designed to optimize aquaculture practices through a mobile application. This system uses different sensors for extracting data continuously regarding temperature, PH and Turbidity etc. These parameters can be analysed in real-time to help fish farmers make decisions on when or how much the system should feed and aerate, and what approach of water treatment is best for their fishes. This information is stored to create individual datasets, offering researchers valuable insights into optimal conditions for each fish species. This can enhance their survival rates and promote growth. In this study, we evaluate a series of machine learning algorithms for their ability to predict the optimal fish species based on water quality parameters. Among these algorithms, Random Forest demonstrated superior performance, achieving an accuracy of 92.5%, precision of 93%, recall of 93%, and F1-score of 92%. These findings highlight the effectiveness of our approach in integrating machine learning with IoT for precise aquaculture management. Implemented through a user-friendly mobile application, our system enhances accessibility and usability for fish farmers.
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Статья научная
Precision agriculture transform the agricultural sector by integrating advanced technologies to enhance productivity and sustainability. In crop farming, precision agriculture can significantly improve practices through precise monitoring and data-driven decision-making, addressing challenges such as optimizing resource usage and improving crop health. This study presents the development and implementation of an IoT-based Crop Recommendation System designed to optimize farming practices through a mobile application. This system uses different sensors to continuously extract data regarding the temperature, pH, NPK value and other relevant parameters. These parameters can be analyzed in real-time to help farmers make informed decisions on irrigation, fertilization, and crop selection, tailored to specific field conditions. This information is stored to create individual datasets, offering researchers valuable insights into optimal conditions for various crops. This can improve yield and promote sustainable farming practices. In this study, we evaluated a series of machine learning algorithms for their ability to predict an optimal crop based on environmental parameters. Among these algorithms, Naive Bayes demonstrated superior performance, achieving an accuracy of 99.55%, precision of 99.58%, recall of 99.55%, and F1-score of 99.54%. These findings highlight the effectiveness of our approach in integrating machine learning with the IoT for precise crop management. Implemented through a user-friendly mobile application, the proposed system enhances accessibility and usability for farmers.
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Irregular Function Estimation with LR-MKR
Статья научная
Estimating the irregular function with multi-scale structure is a hard problem. The results achieved by the traditional kernel learning are often unsatisfactory, since underfitting and overfitting cannot be simultaneously avoided, and the performance relative to boundary is often unsatisfactory. In this paper, we investigate the data-based local reweighted regression model under kernel trick and propose an iterative method to solve the kernel regression problem, local reweighted multiple kernel regression (LR-MKR). The new framework of kernel learning approach includes two parts. First, an improved Nadaraya-Watson estimator based on blockwised approach is constructed to organize a data-driven localized reweighted criteria; Second, an iterative kernel learning method is introduced in a series decreased active set. Experiments on simulated and real data sets demonstrate the proposed method can avoid under fitting and over fitting simultaneously and improve the performance relative to the boundary effetely.
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Isochronous and Anisochronous Modulation Schemes in Wireless Optical Communication Systems
Статья научная
The choice of the modulation format is one of the principle factors in realizing a high performance wireless optical communication system at a reasonable cost and acceptable complexity. The purpose of this paper is to make a comparison between isochronous and anisochronous modulation scheme categories from Discrete (digital) pulse time modulations (PTM) through the simplest scheme in each family; PPM and DPIM respectively, in term of bandwidth requirement, power efficiency and transmission capacity. This is done to give a wider view on the performance of such schemas under wide range of design parameters. In this paper, the properties of PPM and DPIM have been analyzed, from this analysis it has been shown that DPIM or anisochronous modulation schemes in general are strong candidates when synchronization and transmission capacity are taken into account, and when it comes to power performance PPM or isochronous modulations are better.
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Iterative Shrinkage Operator for Direction of Arrival Estimation
Статья научная
In this correspondence we present the application of iterative shrinkage (IS) operator to the DOA estimation task. In particular we focus our attention to Stage wise Orthogonal Matching Pursuit (StOMP) algorithm. We compare StOMP against MUSIC, which is state of the art in DOA estimation. StOMP belongs to compressive sensing regime where as MUSIC is parametric technique based upon sub-space processing. To best of our knowledge IS operators have not been analyzed for DOA estimation. The comparison is performed using extensive numerical simulations.
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Статья научная
The growing need for accessible websites cannot be overemphasized as it has posed a major challenge in the world of Information and Communication Technology (ICT). Most businesses have gone online in order to improve their market value; the banking sector is not an exception. In an attempt to satisfying customers, websites developers have violated most of the websites standards. The banking sector is one area that carries out most of its activities online. Therefore, it is important that their websites be accessible to all especially people with visually impaired disability and more so, regardless of the browsing technology being used. This study evaluates the accessibility and usability of Nigeria banking websites using some automated tools and manual inspection method. This is done in order to know the conformance of the banking websites with standard as specified by Web Accessibility Initiate (WAI). Results from the study indicate that some of the websites do not conform to the expected standard. Hence, there is need for substantial improvements on most bank websites in Nigeria
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Knowledge Template Based Multi-perspective Car Recognition Algorithm
Статья научная
In order to solve the problem due to the vehicle-oriented society such as traffic jam or traffic accident, intelligent transportation system(ITS) is raised and become scientist’s research focus, with the purpose of giving people better and safer driving condition and assistance. The core of intelligent transport system is the vehicle recognition and detection, and it’s the prerequisites for other related problems. Many existing vehicle recognition algorithms are aiming at one specific direction perspective, mostly front/back and side view. To make the algorithm more robust, our paper raised a vehicle recognition algorithm for oblique vehicles while also do research on front/back and side ones. The algorithm is designed based on the common knowledge of the car, such as shape, structure and so on. The experimental results of many car images show that our method has fine accuracy in car recognition.
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LDASpike for Recognizing Epileptic Spikes in EEG
Статья научная
Manual processing of recorded EEG data for characteristics like epileptic spikes is very time consuming since the recording of EEG for a longer duration producing enormous amount of data. Therefore, automated systems are required to speed up the processing. In the current work, a classification method has been proposed for detecting the epileptic spikes in the recorded EEG data by using Linear Discriminant Analysis (LDA) and has been named LDASpike. The prerecorded EEG data files were used as input to LDASpike and the output produced was the total number of spikes present in each EEG file. The proposed method results on an average sensitivity 100% and selectivity 95.38%, when the training and testing data were same. However, with four fold cross-validation applied in this work, the sensitivity and selectivity were achieved as 98.45% and 96.06%, respectively, on an average. Though a little time initially is spent to train the system but the result produced by the system is very promising and can be compared with the existing standard methods. This system can also works with the real time recording and processing for a clinical setup.
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Lessons Towards Developing An Integrated Tool-support for Small Team Meetings
Статья научная
Teams within organisations meet regularly to review their progress and engage in collaborative activities within a team setting. However, the uptake of tools to support their activities within team meetings is limited. Research efforts on understanding the reasons for low rates of tool adoption and learning lessons in developing tools that could be readily adopted by team members within team meetings are largely unexplored. This qualitative study focuses on learning lessons towards developing an integrated tool-support for small team meetings within organisations using focus groups. Discussions were based on a tool-kit framework generated by observing their team meetings in an earlier study. The discussions were recorded and the transcripts were analysed using grounded theory approach to generate stories on team processes and potential tools that could assist team members during each process. The lessons derived from the study were based on three aspects of tool-support namely the potential users of the proposed tool-kit, processes within the team meetings that would be influenced by the introduction of the tool-kit and the technological aspects of the tool-kit.
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Linear antenna array pattern synthesis using elephant swarm water search algorithm
Статья научная
Linear antenna array pattern synthesis using computational method is an important task for the electronics engineers and researchers. Suitable optimization techniques are required for solving this kind of problem. In this work, Elephant Swarm Water Search Algorithm (ESWSA) has been used for efficient and accurate designing of linear antenna arrays that generate desired far field radiation pattern by optimizing amplitude, phase and distance of the antenna elements. ESWSA is inspired by water resource search procedure of elephants during drought. Two different fitness functions for two different benchmark problem of linear antenna array have been tested for validation of the proposed methodology. During optimization, three types of synthesis have been used namely: amplitude only, phase only and position only control for all cases antenna array. The results show that ESWSA is very efficient process for achieving desired radiation pattern while amplitude only control performed better compare to the others two controlling process for all benchmark problems.
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M-Commerce in Bangladesh –Status, Potential and Constraints
Статья научная
Mobile Commerce often referred to as "M-Commerce" or "mCommerce" is a new dimension or extension of e-Commerce that is performed by mobile devices and Personal Digital Assistants (PDA) using mobile phone networks. As the number of mobile users is increasing dramatically the prospect of m-commerce is also increasing day by day in developing countries like Bangladesh. Though there are a lot of researchers has written about the prospect and adoption of M-commerce but in my research, I tried to find out the statistical analysis of mobile users, mobile internet users; M-commerce current status in Bangladesh. Research also has been done for a number of visitors of stakeholder's site, using the ranking tools, uses of mobile apps by customers and limitation of mobile commerce adoption in Bangladesh those were not discussed earlier. Here, I collected data through web and phone call from various stakeholders, in the top line m-commerce business, studied and identified the problem, shown the current status and major barriers of M-commerce and suggested a methodological framework.
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Machine Learning and Software Quality Prediction: As an Expert System
Статья научная
To improve the software quality the number of errors from the software must be removed. The research paper presents a study towards machine learning and software quality prediction as an expert system. The purpose of this paper is to apply the machine learning approaches, such as case-based reasoning, to predict software quality. The main objective of this research is to minimize software costs. Predict the error in software module correctly and use the results in future estimation. The novel idea behind this system is that Knowledge base (KBS) building is an important task in CBR and the knowledge base can be built based on world new problems along with world new solutions. Second, reducing the maintenance cost by removing the duplicate record set from the KBS. Third, error prediction with the help of similarity functions. In this research four similarity functions have been used and these are Euclidean, Manhattan, Canberra, and Exponential. We feel that case-based models are particularly useful when it is difficult to define actual rules about a problem domain. For this purpose we have developed a case-based reasoning model and have validated it upon student data. It was observed that, Euclidean and Exponential both are good for error calculation in comparison to Manhattan and Canberra after performing five experiments. In order to obtain a result we have used indigenous tool. For finding the mean and standard deviation, SPSS version 16 and for generating graphs MATLAB 7.10.0 version have been used as an analyzing tool.
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Machine learning based business forecasting
Статья научная
The business sectors directly contribute to the growth of any nation. Moreover, the business is an activity of producing, buying, and selling the goods and services to generate the money. The business directly involves in the gross domestic product (GDP). The business forecasting is the activity of predicting or estimating the feature position of the sales, expenditures, and profits of any business. However, the business forecasting helps to the business sectors for planning, decision making, resource utilization, business success, etc. Therefore, business forecasting is a pressing need for the growth of any business. In recent past, many researches attempt to carry out the business forecasting using different tools. However, this paper presents the business forecasting for sales data using machine learning technique and the obtained results are presented and discussed..
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Статья научная
Malware classification has already been a prominent concern for decades, and malware attacks have proliferated at an astounding rate, constituting a significant threat to cyberspace. Deep learning (DL) and malware image approaches are becoming more prevalent in the field of malware analysis, with spectacular results. This work focuses on the challenge of classifying malware variants that are represented as images. This study employs visualization and proposes a convolutional neural network (CNN) based DL model to effectively and accurately classify malware. The proposed model is trained and tested on a very challenging and heterogeneous dataset, and it achieves accuracy of 98.179%, precision of 97.39%, a F1-score of 97.70%, and a fast classification speed (3 seconds needed to test 934 unseen malware). This demonstrates the proposed model's incredibly quick, effective and accurate performance. The proposed model outperformed existing traditional DL models in terms of various performance measures and demonstrated its usefulness in classifying malware families through visualization. This study and experimental results reveal that small-scale malware images and a simple CNN architecture alone are capable of accurately classifying malware families with high classification accuracy.
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Статья научная
The study observes the metadata identification and analysis for Document, Audio, Image, and Videos. The process uses MapReduce and Match Aggregate Pipeline to identify, classify, and categories for identification purposes. The inputs are FITS array results and processed in form of XML. The works consist of the extraction process, identification and analysis, classification, and metadata information. The objective is establishing the file information based on volume, variety, veracity, and velocity criteria as part of task identification component in Self-Assignment Data Management. Testing is done for all file types with the number of files and the size of the file according to the grouping. The results show that there is a pattern where the match-aggregate-pipeline has a longer processing time than MapReduce on a small block size, shown in a block size of 64 Mb, 128 Mb, and 256 Mb. But once the block size is magnified the match-aggregate-pipeline has faster processing time at 1024 Mb and 2048 Mb. The results have a contribution in the metadata processing for large files can be done by arranging the block sizes in Match Aggregate Pipeline.
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Map-Reduce based Multiple Sub-Graph Enumeration Using Dominating-Set Graph Partition
Статья научная
The purpose of this paper is to find all the instances of a given set of pattern graphs (sub-graphs) in a large data graph using a single round of Map-Reduce. For the simplest pattern graphs like a triangle and rectangle we propose the solution. This paper enumerates complex pattern graphs using the enumeration of simple pattern graphs. We proposed Dominating set based graph partition, it generates non-overlapped sub-graphs. Each sub-graph is processed by one machine in the cluster. We analyze both the communication cost and the total computational cost. Communication cost is reduced by using Map-Reduce based dominating set graph partition. At the same time Multiple pattern (sub-graphs) graphs can be enumerated with the same communication cost. The proposed method is not always superior to the conventional sub-graph enumeration, but in some cases involving large-scale data where this method wins, including (1) Adjacency list representation of the graph is the input (2) Number of partitions are decided based on the graph size. We experimentally show that our approach decreases significantly the computation cost, communication cost and scales the enumeration process with a large graph database.
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Measuring the Effect of CMMI Quality Standard on Agile Scrum Model
Статья научная
Agile development gets more appreciation from the market due to its flexible nature and high productivity. Scrum provides better management of the processes as compared to other agile modes. Scrum model has several features and strengths but still it lacks in engineering practices and quality. This research deals with the improvement of Scrum processes for better management and quality of software using the infusion of different practices from internationally renowned capability maturity model integration (CMMI) quality standard. Survey is used as a research design to validate the proposed framework. Statistical analysis shows that there is a profound effect on Scrum model due to the proposed research developing high quality software.
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