Luke Deng, Jie Yan, Mingyang Zhao, Jianheng Pan, Xiaoting Bu
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.
{"title":"UAV formation control using enhanced behavior mechanism and artificial potential field","authors":"Luke Deng, Jie Yan, Mingyang Zhao, Jianheng Pan, Xiaoting Bu","doi":"10.47679/ijasca.v4i2.62","DOIUrl":"https://doi.org/10.47679/ijasca.v4i2.62","url":null,"abstract":"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.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"14 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140976066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"A New Agricultural Drought Index to Characterize Agricultural Drought Using Data Mining Techniques","authors":"Shubhangi S. Wankhede","doi":"10.47679/ijasca.v4i1.63","DOIUrl":"https://doi.org/10.47679/ijasca.v4i1.63","url":null,"abstract":"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.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"46 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140972893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
{"title":"A Secure Storage For Medical Information Scheme Using Blockchain","authors":"Kadjo Mathias Adoni, Yuan Xu, Siele Jean Tuo","doi":"10.47679/ijasca.v4i2.71","DOIUrl":"https://doi.org/10.47679/ijasca.v4i2.71","url":null,"abstract":"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","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"138 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140976828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"AI For Human Learning & Behaviour Change","authors":"Divya Divya","doi":"10.47679/ijasca.v4i2.68","DOIUrl":"https://doi.org/10.47679/ijasca.v4i2.68","url":null,"abstract":"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.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"72 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140973716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Muhammad Waqar Arshad Waqar, Dr. Muhammad Bilal Bashir, Dr. Yaser Hafeez
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.
{"title":"Multi-factor based Regression Test Case Prioritization using Fuzzy Logic","authors":"Muhammad Waqar Arshad Waqar, Dr. Muhammad Bilal Bashir, Dr. Yaser Hafeez","doi":"10.47679/ijasca.v3i1.56","DOIUrl":"https://doi.org/10.47679/ijasca.v3i1.56","url":null,"abstract":"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.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":" 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140219203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Every nation is interested in creating "smart cities," making this a hot topic requiring extensive scientific study. Given the current state of affairs, it is essential to conduct a systematic review to thoroughly understand the current research trends and patterns in this field. It is applied to a corpus of 9,131 papers published between 2010 and 2022 from the Scopus publications database to test the string's effectiveness. This research utilizes text mining and Latent Semantic Analysis (LSA) to delve into the current state-of-the-art study concerning IoT Smart cities research. KNIME was used to conduct the analysis. Predicting the study domain is done with the help of the K-means clustering method. Future researchers can build upon these patterns to strengthen security in various sectors. This report also reviews the history of smart city research, its current position, and its prospects. We help institutions and authors collaborate globally in science. This research analyses Smart City IoT integration trends and patterns in a graphical overview. According to the data, the identified areas are developing and need more research.
{"title":"A Comprehensive Analysis and Visualization of Trends and Research Patterns in the Field of IoT Smart Cities","authors":"Chetan Sharma, Shamneesh Sharma, Mehdi Gheisari","doi":"10.47679/ijasca.v4i1.58","DOIUrl":"https://doi.org/10.47679/ijasca.v4i1.58","url":null,"abstract":"Every nation is interested in creating \"smart cities,\" making this a hot topic requiring extensive scientific study. Given the current state of affairs, it is essential to conduct a systematic review to thoroughly understand the current research trends and patterns in this field. It is applied to a corpus of 9,131 papers published between 2010 and 2022 from the Scopus publications database to test the string's effectiveness. This research utilizes text mining and Latent Semantic Analysis (LSA) to delve into the current state-of-the-art study concerning IoT Smart cities research. KNIME was used to conduct the analysis. Predicting the study domain is done with the help of the K-means clustering method. Future researchers can build upon these patterns to strengthen security in various sectors. This report also reviews the history of smart city research, its current position, and its prospects. We help institutions and authors collaborate globally in science. This research analyses Smart City IoT integration trends and patterns in a graphical overview. According to the data, the identified areas are developing and need more research.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"14 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140244007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dr. R.Mathusoothana Kumar Dr. R.Mathusoothana Kumar
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.
作物产量预测方法可以大致预测实际产量，但仍在寻求更好的产量预测性能。在现有方法中，作物产量预测结果是基于过去的经验数据，无法预测作物产量的准确结果。因此，人们提出了一种混合方法，即通过 Mestrial Environ Netsual Network（MENN）进行作物产量预测，以克服现有方法所面临的挑战，并以无懈可击的方式预测作物产量。在以往的技术中，种子的表型和基因变化与植物病理学并没有结合成一个新的模型。因此，我们提出了由任务分配层、子集网络层和综合产量估算层组成的 Mestrial 神经网络（MNN），用于预测播种基因以及表型和病理。此外，综合病理学模块还检查了为预测产量值而选择的受尊重播种种子的表型。此外，在结合统计数据和图像数据进行预测时，由于现有方法在创建数据集时剔除了与统计数据具有相同时间戳的图像，从而影响了预测模型的泛化能力。因此，我们提出了一种新颖的 Yield Environ Netsual Network (YENN)，它由两个深度网络组成：(i) Deep Q network (DQN) 和 (ii) VGG16。其中，VGG-16 用于处理给定的输入图像。因此，所提出的模型能根据基因和环境条件很好地检测潜在的疾病，并有效地预测作物的产量值。
{"title":"Crop yield prediction by Mestrial Environ Netsual Network (MENN)","authors":"Dr. R.Mathusoothana Kumar Dr. R.Mathusoothana Kumar","doi":"10.47679/ijasca.v4i1.59","DOIUrl":"https://doi.org/10.47679/ijasca.v4i1.59","url":null,"abstract":"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.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"21 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Proposed Machine learning model for predicting Egyptian Parliament Election Results","authors":"Doaa Alkhiary, Samir Saleh, Mohamd Marie","doi":"10.47679/ijasca.v4i1.61","DOIUrl":"https://doi.org/10.47679/ijasca.v4i1.61","url":null,"abstract":"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.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"9 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140241371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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%.
{"title":"Automated Handwritten Equation Solver","authors":"Shereen A. Hussien","doi":"10.47679/ijasca.v4i1.60","DOIUrl":"https://doi.org/10.47679/ijasca.v4i1.60","url":null,"abstract":"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. \u0000This 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%.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"22 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140243146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hamid EL BOURAKKADI, Hassan Tabti, Abdelhakim Chemlal, Mourad Kattass, A. Jarjar, A. Benazzi
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.
本文介绍了一种用于加密彩色图像的增强型技术，其效果超过了基因交叉和替换方法。该方法整合了动态随机函数，以加强生成向量的完整性，提高时间复杂性，从而阻止潜在的攻击。这种改进方法需要将基因交叉与两个广泛的伪随机替换表结合起来，而这两个伪随机替换表是从密码学中已有的混沌图中衍生出来的。在对原始图像进行受控矢量化后，我们的方法首先从像素级的 DNA 行为中获得灵感，进行初始遗传交叉。这一过程之后是混淆扩散圈，加强加密像素与其相邻像素之间的关系。混淆扩散机制采用像素级动态伪随机仿射函数。随后，应用第二个遗传交叉算子。在不同大小和格式的各种图像上进行的仿真表明，我们的方法能够抵御统计攻击和差分攻击。
{"title":"Enhanced Vigenere and affine ciphers surrounded by dual genetic crossover mechanisms for encrypting color images","authors":"Hamid EL BOURAKKADI, Hassan Tabti, Abdelhakim Chemlal, Mourad Kattass, A. Jarjar, A. Benazzi","doi":"10.47679/ijasca.v4i1.57","DOIUrl":"https://doi.org/10.47679/ijasca.v4i1.57","url":null,"abstract":"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.","PeriodicalId":507177,"journal":{"name":"International Journal of Advanced Science and Computer Applications","volume":"10 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：