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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: 642
Business Process Re-Engineering in Public Administration of Kingdom of Saudi Arabia
Статья научная
The government of Saudi Arabia is in the phase of transformation. Business process reengineering (BPR) can play a vital role in assessing this conversion. BPR methodologies provide ways to optimize the use of resources while maintaining high-quality services. The aim of this paper is to investigate the introduction of BPR in Saudi Arabia public sector. A framework is proposed to transform change using a knowledge based. The proposed solution is validated through survey. The results of the survey show that Saudi Governmental Agencies acquire the power to implement the BPR successfully especially if it is implemented with knowledge management and the BPR movement started at small scale.
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Business decision support system based on sentiment analysis
Статья научная
Since organizational decisions are vital to organizational development, customers’ views and feedback are equally important to inform good decisions. Given this relevance, this paper seeks to automate a sentiment analysis system - SentDesk- that can aid tracking sentiments in customers’ reviews and feedback. The study was contextualised in some business organisations in Ghana. Three business organizational marketers were made to annotate emotions and as well tag sentiments to each instance in the corpora. Kappa and Krippendoff coefficients were computed to obtain the annotation agreement in the corpora. The SentDesk system was evaluated in the environment alongside comparing the output to that of the average sentiments tagged by the marketers. Also, the SentDesk system was evaluated in the environment by the selected marketers after they had tested the platform. By finding the average kappa value from the corpora (CFR + ISEAR), the average kappa coefficient was found to be 0.40 (40%). The results of evaluating the SentDesk system with humans shows that the system performed as better as humans. The study also revealed that, while annotating emotions and sentiments in the datasets, counsellor’s own emotions influences their perception of emotions.
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CSRFDtool: Automated Detection and Prevention of a Reflected Cross-Site Request Forgery
Статья научная
The number of Internet users is dramatically increased every year. Most of these users are exposed to the dangers of attackers in one way or another. The reason for this lies in the presence of many weaknesses that are not known for ordinary users. In addition, the lack of user awareness is considered as the main reason for falling into the attackers' snares. Cross Site Request Forgery (CSRF) has placed in the list of the most dangerous threats to security in OWASP Top Ten for 2013. CSRF is an attack that forces the user's browser to send or perform unwanted request or action without user awareness by exploiting a valid session between the browser and the server. When CSRF attack success, it leads to many bad consequences. An attacker may reach private and personal information and modify it. This paper aims to detect and prevent a specific type of CSRF, called reflected CSRF. In a reflected CSRF, a malicious code could be injected by the attackers. This paper explores how CSRF Detection Extension prevents the reflected CSRF by checking browser specific information. Our evaluation shows that the proposed solution is successful in preventing this type of attack.
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Статья научная
This paper presents the calculation of the classic-curvature and the intensity-curvature term before interpolation of a bivariate polynomial model function. The classic-curvature is termed as yc (x, y) and the intensity-curvature term before interpolation is termed as E0. The classic-curvature is defined as the sum of the four second order partial derivatives of the bivariate polynomial. The intensity-curvature term before interpolation is defined as the integral of the product between the pixel intensity value termed as f(0, 0) and the classic-curvature calculated at the origin of the coordinate system of the pixel. This paper presents an application of the calculation of classic-curvature and the intensity-curvature term before interpolation using two-dimensional Magnetic Resonance Imaging (MRI) data and reports for the first time in the literature on the behavior of the intensity-curvature term before interpolation.
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Chance for Sustainable Fashion in Jabodetabek
Статья научная
The rise of environmental awareness globally has influenced the clothing industry, prompting the emergence of sustainable fashion as a response to the negative impacts of fast fashion. Despite its positive intentions, sustainable fashion holds a modest 7% of the global market, with fast fashion dominating at 93%. In Indonesia, a growing environmental consciousness has led to an increase in sustainable fashion businesses. An analysis using Porter's five forces model and the Delphi technique for sustainable fashion in Jabodetabek indicates that the current competitive power of sustainable fashion in the broader industry is relatively low. While the competitive landscape is secure and not overly saturated, attention is needed for factors like increasing competitors, significant product differentiation, and high capital requirements, posing notable competitive threats. Key parameters, including the number of buyers and the threat of substitute products, also warrant scrutiny. Challenges persist, such as consumer confusion regarding the pricing of sustainable versus conventional products. Interestingly, the threat from suppliers in the sustainable fashion sector is low, indicating a relatively stable relationship. Despite challenges, the growing awareness of environmental issues in Indonesia presents an opportunity for sustainable fashion businesses to enhance their competitive standing and expand their market share for a more environmentally friendly future. To strengthen the position of sustainable fashion in Indonesia, the focus should be on brand differentiation through storytelling and a unique identity. Innovate products for quality and durability to counter fast fashion trends. Launch educational campaigns, collaborate with like-minded partners, and adopt transparent supply chain practices. Implement competitive pricing, engage customers through loyalty programs, and contribute to the local community, establishing a strong presence in the growing market for sustainable fashion.
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Class Complexity Metric to Predict Understandability
Статья научная
This paper presents a new class complexity metric of an Object-Oriented (OO) program which is used to predict the understandability of classes. The propose complexity metric is evaluated theoretically against Weyuker's properties to analyze the nature of metric and empirically evaluated against three small projects developed by Post Graduate (PG)/Under Graduate (UG) teams. Least Square Regression Analysis technique is performed to arrive at the result and find correlation coefficient of propose metric with the Degree of Understandability. The result indicates that the propose metric is a good predictor of understandability of classes. JHAWK TOOL (Java Code Metrics Tool) were used to evaluate the parameters values involved in propose metric and for analyzing the results of projects, Matlab6.1 and IBM SPSS software were used.
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Статья научная
The aim of this paper is to investigate the performance of time delay neural networks (TDNNs) and the probabilistic neural networks (PNNs) trained with nonlinear features (Lyapunov exponents and Entropy) on electroencephalogram signals (EEG) in a specific pathological state. For this purpose, two types of EEG signals (normal and partial epilepsy) are analyzed. To evaluate the performance of the classifiers, mean square error (MSE) and elapsed time of each classifier are examined. The results show that TDNN with 12 neurons in hidden layer result in a lower MSE with the training time of about 19.69 second. According to the results, when the sigma values are lower than 0.56, the best performance in the proposed probabilistic neural network structure is achieved. The results of present study show that applying the nonlinear features to train these networks can serve as useful tool in classifying of the EEG signals.
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Classification of Multilingual Financial Tweets Using an Ensemble Approach Driven by Transformers
Статья научная
There is a growing interest in multilingual tweet analysis through advanced deep learning techniques. Identifying the sentiments of Twitter (currently known as X) users during the IPO (Initial Public Offering) is an important application area in the financial domain. The number of research works in this domain is less. In this paper, we introduced a multilingual dataset entitled as LIC IPO dataset. This work also offers a modified majority voting-based ensemble technique in addition to our proposed dataset. This test-time ensembling technique is driven by fine-tuning of state-of-the-art transformer-based pretrained language models used in multilingual natural language processing (NLP) research. Our technique has been employed to perform sentiment analysis over LIC IPO dataset. Performance evaluation of our technique along with five transformer-based multilingual NLP models over this dataset has been reported in this paper. These five models are namely a) Bernice, b) TwHIN-BERT, c) MuRIL, d) mBERT, and e) XLM-RoBERTa. It is found that our test-time ensemble technique solves this multi-class sentiment classification problem defined over the proposed dataset in a better way as compared to individual transformer models. Encouraging experimental outcomes confirms the efficacy of the proposed approach.
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Classification of the User's Intent Detection in E-commerce systems – Survey and Recommendations
Статья научная
The personalized experience gets more and more attention these days. Many e-commerce businesses are looking for methods to deliver personalized service. Consumers are expecting, if not demanding, highly personalized experiences. Moreover, customers are typically willing to spend more when they receive such a custom-tailored service. A prerequisite to provide a genuinely personalized experience is to understand the customer. Intent detection is a new and challenging approach in modern e-commerce to understand the customer. We find that various aspects of customer intent detection can be tackled by leveraging tremendous recent recommendation systems' progress. In this work, we review existing works from different domains that can be re-used for customer intent detection in the e-commerce. Even though many methods are used, there is no comparison of available approaches. Based on a review of nearly 100 articles from 2015 until 2019, we propose a categorization of types of intent detection, personalization context, building a customer profile, and dynamic changes in user interests handling. We also summarize existing methods from applicability in the e-commerce domain, including the aspect of the General Data Protection Regulation requirements. The paper aims at the classification of applied techniques and highlights their advantages and disadvantages.
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Classifying IoT Device’s Traffic Traces Using Network Traffic Characteristics
Статья научная
The escalating proliferation of devices, including both Internet of Things (IoT) and non-IoT devices, has triggered a suite of emergent security challenges in cyberspace, such as accurate device identification and authentication. The wide array of device types, protocols, and usability exacerbates these challenges. While conventional addressing schemes such as the logical Internet Protocol addressing and physical Media Access Control addressing schemes are integral for communication, they are susceptible to spoofing attacks. Device fingerprinting can be used to address the issue of identifying devices and traffic types using only implicit identifiers such as network traffic characteristics. In this paper, supervised machine learning based a device fingerprinting model has been proposed for the classification of both IoT and non-IoT devices on three levels based on their communication traffic characteristics. A meticulous feature selection process, employing two attribute evaluators, identified a subset of twenty features crucial for generating unique fingerprints from a large set of features pool. Three publicly available datasets and two supervised classifiers were utilized for evaluation purposes. Experimental results illustrated that the proposed model attained a classification accuracy exceeding 99% in discerning between known and unknown traffic traces (Level-1) on both the UNSW IoT and D-Link IoT datasets using the Random Forest (RF) classifier, and 99.74% accuracy in classifying network traffic types (Level-2) on the UNSW dataset. Individual device identification (Level-3) proves equally robust, with the RF and J48 classifiers achieving 99.03% and 98.14% accuracies on the UNSW non-IoT and IoT datasets, respectively. These findings underscore the potential of the device fingerprinting model in enhancing network security. The model’s robust classification capabilities across various datasets and identification levels make it a valuable asset in tackling modern security challenges in networked environments.
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Classifying Similarity and Defect Fabric Textures based on GLCM and Binary Pattern Schemes
Статья научная
Textures are one of the basic features in visual searching,computational vision and also a general property of any surface having ambiguity. This paper presents a texture classification system which has high tolerance against illumination variation. A Gray Level Co-occurrence Matrix (GLCM) and binary pattern based automated similarity identification and defect detection model is presented. Different features are calculated from both GLCM and binary patterns (LBP, LLBP, and SLBP). Then a new rotation-invariant, scale invariant steerable decomposition filter is applied to filter the four orientation sub bands of the image. The experimental results are evaluated and a comparative analysis has been performed for the four different feature types. Finally the texture is classified by different classifiers (PNN, K-NN and SVM) and the classification performance of each classifier is compared. The experimental results have shown that the proposed method produces more accuracy and better classification accuracy over other methods.
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Client Server iOS in Player versus Player (PVP) of “Borneo Snap”
Статья научная
Borneo Snap is a Kalimantan’s animals snap card game. Play proceeds with the players taking it in turns to remove a card from the top of their deck and place it face-up on a central pile. If two cards placed consecutively on the pile are identical (same picture) then the first player to shout "Snap!"will get all of it. This game is built for the iOS platform. Game Player Versus Player (PVP) Borneo Snap using peer-to-peer API from Game Kit (XCode Framework) and Wi-Fi or Bluetooth, but actually, Borneo Snap uses client-server architecture model, each player in a player versus player game session only can communicate with server intermediaries. If the player sends data updates when playing cards to other players, this data update will first be through the server, then forwarded to all other players. The result of this research, with client-server framework Borneo Snap can be played by more than 1 player and more iOS gadget too
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Статья научная
The goal of the question-answering system is to respond to user queries expressed in natural language. Unlike search engines, the closed domain question answering systems are specialized to specific domains, providing concise and precise answers often derived from structured data. This paper focuses on a question-answering system tailored for crime events, capable of addressing both statistical and contextual inquiries. In terms of crime statistics, the fine-tuned GPT-3 model outperforms the USE, TAPAS, TAPEX, and GPT-3 models, while for context-based crime-related queries, the fine-tuned RoBERTa model surpasses the BERT and RoBERTa models. This system is capable of providing the responses in natural language format, supplemented with relevant data visualizations. The models are train on Q2A and NewsQA datasets while it is tested on NCRB and NewsTimes datasets. The Q2A and NCRB datasets are used for statistical queries while NewsQA and NewsTimes datasets are used for contextual inquiries. The paper presents an analysis of various models and showcases results for sample case studies. Such a system can prove valuable in applications where users seek to study criminal cases or gather pertinent insights for specific cases. Furthermore, it can assist in understanding patterns and trends in criminal events, particularly concerning geospatial information. Linking crime event-based question-answering systems to geospatial information facilitates exploration of niche areas and furnishes precise details about local crime with minimal hype and hence worth exploring.
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Cloud and On-premises Based Security Solution for Industrial IoT
Статья научная
In this paper, we take Industrial IoT (IloT) as a main point, where we touch on the direction of Industrial IoT concepts and connectivity protocols used by Industrial IoT devices. Moreover, we go into deep security challenges the Industrial ecosystem faces. Nowadays, most industries focus on specific protocols in their smart IoT devices. In return, we mainly focus on Message Queuing Telemetry Transport (MQTT) protocol, MQTT server where IoT devices are connected to it, and secure connectivity among server, cloud, and end user. Our purpose here is to describe the security approach for server and cloud-based environments and the utilization of cloud security tools such as IoT-hub, Network Security Group (NSG) and virtual private network (VPN). In more detail, here we have indicated proposed solution by separating into device, on-premises and cloud zone sections, proper technologies which are being used in modern security approaches and comparison with traditional IoT security approaches. This article enables readers to obtain fundamental knowledges on available technologies which are utilized in industrial areas and real-time scenarios where this solution is deployed.
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Статья научная
In this paper, we present the design and development of a collaborative knowledge-sharing platform with Blockchain based smart contracts (CodeBlockS) to help increase the trust and efficiency of how developers find the solution to their problems or try to learn new things. The popularity of Question-and-Answer websites such as StackOverflow, Ask, and Yahoo, as well as online course websites like as Udemy, is gradually expanding. Given this increased popularity, the quality and efficiency of user interaction must be improved such that users can try to connect with each other, ask questions about technical problems they are experiencing, or if they want to learn a topic in exchange for a fee and potentially collaborate on a project, or simply share their thoughts on a topic and improve their knowledge and network at the same time. Because these contracts will contain money, CodeBlockS has employed Ethereum Blockchain-based smart contracts to manage the data and money, as blockchain-based smart contracts are immutable and handle payments very securely. In general, social networking websites there are very few people sharing valuable knowledge and many people sharing worthless, time-consuming content that creates distraction. With the CodeBlockS system, developers find the solution to their problems or try to learn new things, and users can share their thoughts and learning on the platform. The platform also provides inbuilt smart contracts functionality using which two users can create a contract where one user will teach or solve doubt of the other user and receive fees towards service rendered.
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Coffee Leaf Disease Recognition Using Gist Feature
Статья научная
Coffee leaf disease recognition is important as its quality can be affected by the disease like –rust. This paper presents a coffee leaf disease recognition system with the help of gist feature. This research can help coffee producers in diagnosis of coffee plants in initial stage. Rocole coffee leaf dataset is considered in this study. Input image is pre-processed first. Resize and filtering is used as pre-processing work. Gist feature is extracted from pre-processed image. Extracted features are trained with machine learning algorithm. In testing phase, features are extracted and tested with trained ML model. Simulation is done with 10 fold cross validation. Different ML models are used and selected the best among them based on performance. SVM achieved overall 99.8% accuracy in recognizing coffee leaf disease.
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Color Histogram and DBC Co-Occurrence Matrix for Content Based Image Retrieval
Статья научная
This paper presents the integration of color histogram and DBC co-occurrence matrix for content based image retrieval. The exit DBC collect the directional edges which are calculated by applying the first-order derivatives in 0º, 45º, 90º and 135º directions. The feature vector length of DBC for a particular direction is 512 which are more for image retrieval. To avoid this problem, we collect the directional edges by excluding the center pixel and further applied the rotation invariant property. Further, we calculated the co-occurrence matrix to form the feature vector. Finally, the HSV color histogram and the DBC co-occurrence matrix are integrated to form the feature database. The retrieval results of the proposed method have been tested by conducting three experiments on Brodatz, MIT VisTex texture databases and Corel-1000 natural database. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP, DBC and other transform domain features.
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Common Fixed Point Theorem in Fuzzy Metric Spaces using weakly compatible maps
Статья научная
The aim of this paper is to prove a common fixed theorem for four mappings under weakly compatible condition in fuzzy metric space. While proving our results we utilize the idea of weakly compatible maps due to Jungck and Rhoades. Our results substantially generalize and improve a multitude of relevant common fixed point theorems of the existing literature in metric as well as fuzzy metric space.
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Статья научная
Knowledge processing is the prime area of information retrieval in the current era. However knowledge is subjected to the meaning of discretion in any natural language. Intelligent search in various Natural Languages is required in the huge repository of information available online. Language is the integral part for any form of communication but the language has to be meaningful. Semantics is a field of linguistics that deals with the meaning of the linguistic expressions through discovery of knowledge. In this research paper, the dictionary based approach for semantics is studied and implemented. The dictionary based proposal relies on the formalization of sentence across SVO (Subject-Verb-Object) format. Rule-based classifier helps to define the rules that are checked against the dictionary which contains sequence of Subject, Verbs and Object available in English Language. By looking at the accuracy measures, recall and precision the results obtained by the proposed approach is proven good.
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Comparative Advancements and Challenges of Branding Strategy in Business-to-Business Scenarios
Статья научная
Businesses have evolved with digitization and automation of manual processes through ERP solutions. These solutions fall under the category of being Business-To-Business (B2B) and help its clients to grow with a centralized solution to manage all business functions. It is easier for B2C companies to expand their reach using targeted branding strategies but takes a different approach for B2B companies to expand and grow as a Brand. It not only impacts how to reach a potential market but also impacts talent attraction and evolution into a trusted partner for potential market segments. Considering the case of an ERP company as large as SAP, various strategies are observed to target and attract potential customers as well as retain the existing clientele. This paper explores the concepts of Branding in a B2B scenario for an ERP company with a focus on SAP. It evaluates brand value, positioning, architecture, and expense comparison over the years and delves into a comparative study between SAP and one of its customers (B2C), Nestle, in terms of expenses and profits acquired. It also explores changes in Brand Strategy through Customer Advocacy, the importance of sales and marketing, and response strategies to the COVID-19 pandemic.
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