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<![CDATA[BUSINESS DEVELOPMENT IN THE DIGITAL AGE ]]>
<![CDATA[Helisia Marganaha]]>
<![CDATA[ International Journal of Advanced Science and Computer Applications Vol. 3 No. 2 (2024): September 2024 ]]>
Publisher : <![CDATA[Utan Kayu Publishins ]]>
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DOI: 10.47679/ijasca.v3i2.55
<![CDATA[In today's digital era, old systems and processes must be rethought, and new technologies must be implemented to keep businesses competitive and growing. High global competition provides its own demands for business people to continue to improve product innovation by utilising existing technology to face this global challenge. Data collection techniques on business development in the digital era are conducted through online data analysis, literature studies obtained from Google Scholar, online surveys and social media monitoring to collect information on digital businesses. The use of technologies such as big data and sentiment analysis can also help in understanding the changes that occur in the digital business ecosystem. Technology and the internet have opened up new opportunities for businesses to reach a wider market, improve operational efficiency, and accelerate business growth. High global competition puts its own demands on businesses to continuously improve product innovation by utilising existing technology to face these global challenges. Businesses that succeed in the digital era are those that can adapt quickly and remain responsive to changes in the market and technology. Businesses need to adapt to technological developments and utilise them to improve business quality and expand market reach.]]>
<![CDATA[The Multi-factor based Regression Test Case Prioritization using Fuzzy Logic ]]>
<![CDATA[Muhammad Waqar Arshad waqar]]>;
<![CDATA[Muhammad Bilal Bashir]]>;
<![CDATA[Yaser Hafeez]]>
<![CDATA[ International Journal of Advanced Science and Computer Applications Vol. 3 No. 1 (2024): March 2024 ]]>
Publisher : <![CDATA[Utan Kayu Publishins ]]>
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DOI: 10.47679/ijasca.v3i1.56
<![CDATA[The maintenance level activity generally done after the modification in the software to check whether it is functioning right or not is termed as regression testing. Test case prioritization, a key practice, involves strategically ordering test cases based on specific criteria to enhance the efficiency of fault detection within a condensed time frame. The fuzzy rule base serves as an alternative to the conventional crisp value set, offering a nuanced approach beyond binary outcomes (Yes or No). The primary objective of this research is to address critical factors often overlooked in existing literature on prioritization. Notably, prevalent approaches focus on singular factors during test case prioritization, highlighting the need for a comprehensive technique. To enhance the prioritization of test cases, there is a demand for a method that considers multi-factors or combinations thereof, ultimately increasing effectiveness. This paper introduces an innovative approach a multi-factors regression test-case prioritization technique utilizing fuzzy rules. The methodology aims to optimize the prioritization of test cases, striking a balance between effectiveness and time efficiency. Fuzzy rules are formulated to assess the effectiveness of a prioritized set of test cases in developing the proposed approach. A user-friendly tool has been developed to facilitate the application of this technique, allowing users to input relevant factors and subsequently prioritize test cases accordingly. Through extensive experiments using the developed tool, the effectiveness of the proposed approach has been validated. The results demonstrate that the priority lists of test cases generated for different projects, considering multi-factors, show greater promise compared to techniques relying solely on a single factor for prioritization.]]>
<![CDATA[Enhanced Vigenere And Affine Ciphers Surrounded By Dual Genetic Crossover Mechanisms For Encrypting Color Images ]]>
<![CDATA[EL BOURAKKADI, Hamid]]>;
<![CDATA[TABTI, Hassan]]>;
<![CDATA[CHEMLAL, Abdelhakim]]>;
<![CDATA[KATTASS, Mourad]]>;
<![CDATA[JARJAR, Abdellatif]]>;
<![CDATA[BENAZZI, Abdelhamid]]>
<![CDATA[ International Journal of Advanced Science and Computer Applications Vol. 4 No. 1 (2025): March 2025 ]]>
Publisher : <![CDATA[Utan Kayu Publishins ]]>
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DOI: 10.47679/ijasca.v4i1.57
<![CDATA[This paper introduces an enhanced technique for encrypting color images, surpassing the effectiveness of genetic crossover and substitution methods. The approach integrates dynamic random functions to bolster the integrity of the resulting vector, elevating temporal complexity to deter potential attacks. The enhancement entails amalgamating genetic crossover using two extensive pseudorandom replacement tables derived from established chaotic maps in cryptography. Following the controlled vectorization of the original image, our method commences with an initial genetic crossover inspired by DNA behavior at the pixel level. This process is followed by a confusion-diffusion lap, strengthening the relationship between encrypted pixels and their neighboring counterparts. The confusion-diffusion mechanism employs dynamic pseudorandom affine functions at the pixel level. Subsequently, a second genetic crossover operator is applied. Simulations conducted on various images with varying sizes and formats demonstrate the resilience of our approach against statistical and differential attacks.]]>
<![CDATA[Crop yield prediction by Mestrial Environ Netsual Network (MENN) ]]>
<![CDATA[Kumar, R.Mathusoothana]]>
<![CDATA[ International Journal of Advanced Science and Computer Applications Vol. 4 No. 1 (2025): March 2025 ]]>
Publisher : <![CDATA[Utan Kayu Publishins ]]>
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DOI: 10.47679/ijasca.v4i1.59
<![CDATA[Crop yield prediction methods can roughly predict actual yield, although better yield prediction performance is still sought. In the existing methodologies the crop yield prediction outcomes are based on the past experience data and failed to predict the exact outcomes of the crop yield. Hence, a hybrid approach namely Crop yield prediction by Mestrial Environ Netsual Network (MENN) has been proposed to overcome the challenges in the existing approaches and to predict the crop yield with impeccable manner. In previous techniques, the change in phenotype as well as genes in the seed and the plant pathology are not combined as a new model. Hence, Mestrial Neural Network (MNN) has been proposed which consist of Task allocation layer, Subset-net layer and Integrated yield estimation layer to predict the sowing seed gene along with the phenotype and pathology. Also, incorporated pathology module examines the phenotype of respected sowing seed selected for the prediction of yield value. Moreover, while combining the statistical data and image data for the prediction, the generalization ability of prediction model was affected by reason of the images that shared the same timestamp as the statistical data were eliminated as part of the procedure for creating the dataset utilized in the existing approaches. Hence, a novel, Yield Environ Netsual Network (YENN) has been proposed which is consists of two deep networks; (i) Deep Q network (DQN) and (ii) VGG16 for the generalization ability as well as the elimination of data caused by the same timestamp is rectified. Here, VGG-16 is utilized for processing the given input images. As a result, the proposed model well examine the potential disease based on the gene and environment conditions and effectively predict the yield value of crops.]]>
<![CDATA[Automated Handwritten Equation Solver ]]>
<![CDATA[Hussien, Shereen A.]]>;
<![CDATA[Azim, Ahmed M. Abd el]]>;
<![CDATA[Hagag, Ahmed]]>
<![CDATA[ International Journal of Advanced Science and Computer Applications Vol. 4 No. 1 (2025): March 2025 ]]>
Publisher : <![CDATA[Utan Kayu Publishins ]]>
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DOI: 10.47679/ijasca.v4i1.60
<![CDATA[Mathematics has an important role in person’s life, so solving the mathematical equations is an essential. Solving mathematical expressions is not restricted to just students but also for mathematicians, physicists and scientists. Solving the mathematical equations is an interesting process.The traditional method of solving math expressions is unsatisfactory as the user should learn different rules and approaches for each mathematical equation. Also, these methods may take long time in complex or obscure problems which makes them subject to user errors and mistakes. The challenging in mathematical expressions must be written in a specific format, users prefer to write them on paper as an easy entering way than other computerized tools. This paper used the technology to introduce a new method over the traditional one using pen and paper. The equation handwriting easiness is blended (merge/integrate) with the advanced computer technologies speed to solve the equations with flexible robust way. An interface introduces that allows capturing the equations contained in an image then solving it without making the user dive into the complex rules. Various types of equations could be entered to this application (linear/nonlinear/quadratic) with achieving a convenient accuracy 95.7%.]]>
<![CDATA[a Proposed Machine learning model for predicting Egyptian Parliament Election Results ]]>
<![CDATA[doaa alkhiary]]>;
<![CDATA[Samir Saleh]]>;
<![CDATA[Mohamd Marie]]>
<![CDATA[ International Journal of Advanced Science and Computer Applications Vol. 4 No. 1 (2025): March 2025 ]]>
Publisher : <![CDATA[Utan Kayu Publishins ]]>
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DOI: 10.47679/ijasca.v4i1.61
<![CDATA[Political life and election have become one of the most important comments on social media sites. Governments have shown a keen interest in predicting the results of elections, whether presidential or parliamentary. The purpose of this study is to predict the results of the Egyptian Parliament elections using sentiment analysis, specifically Support Vector Machines (SVM), Naive Bayes, Decision Trees, and Random Forests in the context of machine learning. In this study, a sentiment analysis approach is employed to analyze public sentiment towards political parties and candidates leading up to Parliament elections. The sentiment analysis techniques are utilized to classify sentiment from textual data collected from Tweeter; Data were obtained in November 2020 before and during election days. The study utilizes a machine learning framework to train and test the models using a labeled dataset of sentiment-labeled political texts. The findings of this study reveal that sentiment analysis using machine learning can effectively predict the results of Parliament elections. The accuracy and performance of each technique are evaluated and compared to determine the most accurate and reliable predictor of election outcomes. This study contributes to the existing literature by applying sentiment analysis techniques to predict Parliament election results. The use of machine learning algorithms in combination with sentiment analysis, offers a novel approach to election forecasting. The findings of this study can be valuable for political analysts, election strategists, and policymakers seeking to understand public sentiment and predict election outcomes accurately.]]>
<![CDATA[UAV Formation Control Using Enhanced Behavior Mechanism And Artificial Potential Field ]]>
<![CDATA[Deng, Luke]]>;
<![CDATA[Yan, Jie]]>;
<![CDATA[Zhao, Mingyang]]>;
<![CDATA[Pan, Jianheng]]>;
<![CDATA[Bu, Xiaoting]]>
<![CDATA[ International Journal of Advanced Science and Computer Applications Vol. 4 No. 2 (2025): September 2025 ]]>
Publisher : <![CDATA[Utan Kayu Publishins ]]>
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DOI: 10.47679/ijasca.v4i2.62
<![CDATA[Inspired by formation flight of pigeon flock, this paper proposes a enhanced method of autonomous formation control of multiple Unmanned Aerial Vehicles (UAVs) that can maintain high symmetry based on pigeon flock behavior mechanism. Addressing the instability of formation in the original method, the follow improvements have been made. Firstly, improve leadership of top three UAVs, Secondly, modify artificial potential field strategies for top two followers. Finally, through a series of simulation experiments, it is verified that the UAVs can form the expected formation under the autonomous formation control, and can maintain the formation under the complex motion of leader UAV.]]>
<![CDATA[A New Agricultural Drought Index to Characterize Agricultural Drought Using Data Mining Techniques ]]>
<![CDATA[Wankhede, Shubhangi]]>;
<![CDATA[Armstrong, Leisa]]>;
<![CDATA[Tripathy , Amiya Kumar]]>
<![CDATA[ International Journal of Advanced Science and Computer Applications Vol. 4 No. 1 (2025): March 2025 ]]>
Publisher : <![CDATA[Utan Kayu Publishins ]]>
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DOI: 10.47679/ijasca.v4i1.63
<![CDATA[Drought monitoring is a critical task as its occurrence and extent vary according to many factors like drought type, risk, agricultural losses, and impact. Monitoring drought is important because the footprint of this hazard is larger than that of other natural hazards. Many drought indices are developed to monitor complex drought conditions. The intensity and severity of drought in a particular region and at a particular time can be tracked by the drought indicator. In this research, a new agricultural drought index, Yield-Evapotranspiration Drought Index (YEDI) is developed using crop yield, potential, and reference crop evapotranspiration. Data mining and Neural Network techniques have been used to model the drought index. The agricultural and climatic data used is selected from the year 1983 to 2015 (33 years) from the period of June to October (Kharif period) for Maharashtra state in India. The drought index generates the positive values which are further divided into a range of high, medium, and low intensities of drought. SPI and SPEI indices are used for validation against YEDI. Results show that there is a correlation between YEDI and SPEI whereas a low correlation is between YEDI and SPI. YEDI proves to be useful for agricultural drought monitoring.]]>
<![CDATA[Artificial Intelligence for Human Learning & Behaviour Change ]]>
<![CDATA[Divya, Sadarangani]]>;
<![CDATA[Desai, Arju.K.]]>;
<![CDATA[Dave, Vivek]]>
<![CDATA[ International Journal of Advanced Science and Computer Applications Vol. 4 No. 2 (2025): September 2025 ]]>
Publisher : <![CDATA[Utan Kayu Publishins ]]>
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DOI: 10.47679/ijasca.v4i2.68
<![CDATA[This paper explores the potential of artificial intelligence (AI) in facilitating human learning and promoting behaviour change. By employing machine learning algorithms, natural language processing, and data analysis, AI systems can provide personalized learning experiences, identify learning gaps, and adapt to individual learning styles. Furthermore, AI can be utilized to create nudges and interventions that encourage positive behaviour change, offering promising applications in fields such as health, finance, and environmental conservation. The paper also discusses ethical considerations and challenges, emphasizing the importance of transparency, fairness, and privacy in AI-driven learning and behaviour change systems]]>
<![CDATA[A Secure Storage For Medical Information Scheme Using Blockchain ]]>
<![CDATA[Adoni, Kadjo Mathias]]>;
<![CDATA[XU, Yuan]]>;
<![CDATA[TUO, SIELE JEAN]]>
<![CDATA[ International Journal of Advanced Science and Computer Applications Vol. 4 No. 2 (2025): September 2025 ]]>
Publisher : <![CDATA[Utan Kayu Publishins ]]>
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DOI: 10.47679/ijasca.v4i2.71
<![CDATA[Nowadays, many companies, organizations, hospitals and individuals have adopted centralized data storage systems to store and share data. However, these systems create a single point of failure and involve a centralized entity or third party, which can cause concern for users. Decentralized storage systems are therefore needed to overcome the drawbacks of the traditional approach. However, in the face of centralization issues, this paper proposes a combination of Hyperledger Fabric, InterPlanetary File System (IPFS), Attribute-Based Access Control (ABAC), and proxy re-encryption to enhance the security and transparency features of decentralized storage systems. Thus, the proposed scheme provides a secure decentralized system storage of medical information using a consortium blockchain]]>
