The most traditional policy models which do not consider dynamic nature of distribute systems and the limitation in addressing issues like adaptability, extensibility, and reasoning over security policies. The main cause of the flexibility and scalability issues in the environments of the Internet and dynamic networks is that there is no central control over the environments, and users are not predetermined. As a result, security and trust issues become critical in the various systems; enhancing the security of these environments would require adding trust to the existing security infrastructures. Few trust models have taken into account the semantic relationship for pervasive elements, despite the fact that numerous models have been proposed to address trust issues in dynamic environments; especially those who related to trust categories. In our work, we solve issues resulted from security and tracking the dynamics of participating communication devices in dynamic distributed networks. Through using ontology for trust management which it is define vocabularies used to described and represented an area of knowledge. For representation, we used semantic web's tools to represent the domain of the dynamic environment and we improv that the reasoning succusses in inference the trusted device and user exactly where we do query.
{"title":"Enhancement the Security by creating ontology-based Trust Management using Semantic Web tools","authors":"Wurood Al-shadood","doi":"10.61710/akjs.v1i2.58","DOIUrl":"https://doi.org/10.61710/akjs.v1i2.58","url":null,"abstract":"The most traditional policy models which do not consider dynamic nature of distribute systems and the limitation in addressing issues like adaptability, extensibility, and reasoning over security policies. The main cause of the flexibility and scalability issues in the environments of the Internet and dynamic networks is that there is no central control over the environments, and users are not predetermined. As a result, security and trust issues become critical in the various systems; enhancing the security of these environments would require adding trust to the existing security infrastructures. Few trust models have taken into account the semantic relationship for pervasive elements, despite the fact that numerous models have been proposed to address trust issues in dynamic environments; especially those who related to trust categories. In our work, we solve issues resulted from security and tracking the dynamics of participating communication devices in dynamic distributed networks. Through using ontology for trust management which it is define vocabularies used to described and represented an area of knowledge. For representation, we used semantic web's tools to represent the domain of the dynamic environment and we improv that the reasoning succusses in inference the trusted device and user exactly where we do query.","PeriodicalId":502336,"journal":{"name":"AlKadhum Journal of Science","volume":"357 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139180041","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}
Sahar R. Abdul Kadeem, Ali Naser, Ahmed R. Hassan, Ghufran Abbas Betti
Autonomous cars are now possible due to significant advances in robotics and intelligent control systems. Before these vehicles can safely operate in traffic and other hostile environments, there are many navigation, vision, and control issues. We want techniques that are both cost-effective and efficient, so that the field of research and academia may fully embrace self-driving cars. Within this scenario, we need something that can convert people to autonomous automobiles and include existing vehicles so that academics and explorers can access them. This study proposes a flexible mechanical layout that can be assembled in a short time and installed in most modern automobiles; it can also be used as a stepping stone in the development of autonomous vehicles. Using various actuators, conventional automobiles can be converted into autonomous vehicles. In the context of motor vehicle automation, motors are often used as actuators. In addition to motors, a pneumatic system was developed to automate the predetermined steps. An autonomous vehicle's mechanical arrangement is crucial, and it must be regularly updated and built to be robust in the face of dynamic conditions. We re-implemented two additional convolutional neural networks in an effort to conduct an objective test of their proposed network and compare our system's structure, technical complexity, and performance test during autonomous driving with theirs. This predicted network is around 250 times larger than the Alex Net network and four times larger than Pilot Net after training. Although the complexity and measurement of the publication's system are lower than other models that contribute lower latency and greater speed throughout inference, the operation was claimed by our system, which achieved autonomous driving with an equivalent efficacy as that achieved with two other models. The projected deep neural system reduces the need to infer ultra-fast computational hardware. This is important for cost efficiency, scale, and cost.
由于机器人技术和智能控制系统的巨大进步，自动驾驶汽车现已成为可能。在这些汽车能够在交通和其他恶劣环境中安全行驶之前，还存在许多导航、视觉和控制问题。我们需要既经济又高效的技术，以便研究领域和学术界能够全面接受自动驾驶汽车。在这种情况下，我们需要一种既能将人们转换为自动驾驶汽车，又能将现有车辆纳入其中，以便学术界和探险家能够使用这些车辆的技术。本研究提出了一种灵活的机械布局，可在短时间内组装并安装在大多数现代汽车上；它还可用作开发自动驾驶汽车的垫脚石。利用各种执行器，传统汽车可转变为自动驾驶汽车。在机动车自动化方面，电机通常被用作执行器。除电机外，还开发了气动系统，以实现预定步骤的自动化。自动驾驶汽车的机械布置至关重要，必须定期更新和建造，以便在动态条件下保持稳健。我们重新实施了两个额外的卷积神经网络，试图对他们提出的网络进行客观测试，并将我们的系统结构、技术复杂性以及自动驾驶期间的性能测试与他们的系统进行比较。经过训练后，这个预测网络比 Alex Net 网络大 250 倍左右，比 Pilot Net 大四倍。虽然该出版物的系统复杂度和测量值低于其他模型，但我们的系统在整个推理过程中的延迟更低，速度更快，实现了与其他两个模型同等功效的自动驾驶。预计的深度神经系统减少了对超高速计算硬件推理的需求。这对成本效率、规模和成本都很重要。
{"title":"Artificial Neural Network-Powered, Driverless Vehicle Concept Development","authors":"Sahar R. Abdul Kadeem, Ali Naser, Ahmed R. Hassan, Ghufran Abbas Betti","doi":"10.61710/akjs.v1i2.63","DOIUrl":"https://doi.org/10.61710/akjs.v1i2.63","url":null,"abstract":"Autonomous cars are now possible due to significant advances in robotics and intelligent control systems. Before these vehicles can safely operate in traffic and other hostile environments, there are many navigation, vision, and control issues. We want techniques that are both cost-effective and efficient, so that the field of research and academia may fully embrace self-driving cars. Within this scenario, we need something that can convert people to autonomous automobiles and include existing vehicles so that academics and explorers can access them. This study proposes a flexible mechanical layout that can be assembled in a short time and installed in most modern automobiles; it can also be used as a stepping stone in the development of autonomous vehicles. Using various actuators, conventional automobiles can be converted into autonomous vehicles. In the context of motor vehicle automation, motors are often used as actuators. In addition to motors, a pneumatic system was developed to automate the predetermined steps. An autonomous vehicle's mechanical arrangement is crucial, and it must be regularly updated and built to be robust in the face of dynamic conditions. We re-implemented two additional convolutional neural networks in an effort to conduct an objective test of their proposed network and compare our system's structure, technical complexity, and performance test during autonomous driving with theirs. This predicted network is around 250 times larger than the Alex Net network and four times larger than Pilot Net after training. Although the complexity and measurement of the publication's system are lower than other models that contribute lower latency and greater speed throughout inference, the operation was claimed by our system, which achieved autonomous driving with an equivalent efficacy as that achieved with two other models. The projected deep neural system reduces the need to infer ultra-fast computational hardware. This is important for cost efficiency, scale, and cost.","PeriodicalId":502336,"journal":{"name":"AlKadhum Journal of Science","volume":"514 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139179152","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}
The large number of courses offered in universities and online studies made it difficult for students to choose the courses that suit their interests and career goals, which led students to lose many opportunities to be employed in the job they wanted. To keep pace with the rapid development of technology, and instead of relying on the job title as was previously done, the employers began to identify the skills required for a job. The competencies of the candidates are then examined and evaluated according to those requirements. Thus, it has become necessary for students to take courses that suit their future professional interests, ensuring that they are employed in the job they desire and supporting their long-term career success. Fortunately, the emergence of skills-based employment has provided an opportunity for universities and colleges to create a clearer path to the courses offered to allow students to take courses that match their future career interests. In this study, we used K-Mean clustering algorithm, TF-idf approach, and content-based filtering algorithm to provide relevant courses for students based on the required job with an explanation of why these courses are recommended. Our result illustrates that our method offers many advantages compared with other recommender systems. our system converts a simple course recommendation into a tool for discovering skills. Since many recommendation systems work as black boxes, we designed our system to recommend the relevant course with explaining why these courses are recommended, which will add a factor of transparency to our system and confirms the reliability of the system to the students.
{"title":"An Explainable Content-Based Course Recommender Using Job Skills","authors":"Yasir Mahmood Younus","doi":"10.61710/akjs.v1i2.62","DOIUrl":"https://doi.org/10.61710/akjs.v1i2.62","url":null,"abstract":"The large number of courses offered in universities and online studies made it difficult for students to choose the courses that suit their interests and career goals, which led students to lose many opportunities to be employed in the job they wanted. To keep pace with the rapid development of technology, and instead of relying on the job title as was previously done, the employers began to identify the skills required for a job. The competencies of the candidates are then examined and evaluated according to those requirements. Thus, it has become necessary for students to take courses that suit their future professional interests, ensuring that they are employed in the job they desire and supporting their long-term career success. Fortunately, the emergence of skills-based employment has provided an opportunity for universities and colleges to create a clearer path to the courses offered to allow students to take courses that match their future career interests. In this study, we used K-Mean clustering algorithm, TF-idf approach, and content-based filtering algorithm to provide relevant courses for students based on the required job with an explanation of why these courses are recommended. Our result illustrates that our method offers many advantages compared with other recommender systems. our system converts a simple course recommendation into a tool for discovering skills. Since many recommendation systems work as black boxes, we designed our system to recommend the relevant course with explaining why these courses are recommended, which will add a factor of transparency to our system and confirms the reliability of the system to the students.","PeriodicalId":502336,"journal":{"name":"AlKadhum Journal of Science","volume":"125 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139180019","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}
To this day, tuberculosis remains one of the most severe threats to public health on a global scale, which is why there is a pressing need for the development of diagnostic techniques that combine high levels of precision, speed in producing findings, mobility, and risk reduction. This work's planned scope is constructing a photonic crystal fiber sensor with a susceptible non-complex core intended to detect tuberculosis at wavelengths ranging from 1 µm to 2.2 µm. This study introduces an innovative biomedical photonic crystal fiber sensor capable of accurately detecting tuberculosis bacteria across all four strains and effectively distinguishing between them. To carry out numerical studies, the proposed structure uses a technique known the full-vector finite element method (FV-FEM). Compared to earlier biomedical sensors based on photonic crystal fiber, the sensor that has been developed demonstrates an exceptionally high relative sensitivity in detecting various kinds while also displaying a deficient level of loss. The proposed sensor has an effective size of 38 µm2, a sensitivity of 99.9%, and a low confinement loss of 10-11 dB/m. To validate the usefulness of the proposed layout and establish its integrity, a detailed analysis is performed by contrasting the results of this study with the most current research published on photonic crystal fiber.
{"title":"Development of an Optical Crystal Fiber Sensor for Early Detection of Tuberculosis","authors":"R. Jebur, R. H. Thaher","doi":"10.61710/akjs.v1i2.59","DOIUrl":"https://doi.org/10.61710/akjs.v1i2.59","url":null,"abstract":"To this day, tuberculosis remains one of the most severe threats to public health on a global scale, which is why there is a pressing need for the development of diagnostic techniques that combine high levels of precision, speed in producing findings, mobility, and risk reduction. This work's planned scope is constructing a photonic crystal fiber sensor with a susceptible non-complex core intended to detect tuberculosis at wavelengths ranging from 1 µm to 2.2 µm. This study introduces an innovative biomedical photonic crystal fiber sensor capable of accurately detecting tuberculosis bacteria across all four strains and effectively distinguishing between them. To carry out numerical studies, the proposed structure uses a technique known the full-vector finite element method (FV-FEM). Compared to earlier biomedical sensors based on photonic crystal fiber, the sensor that has been developed demonstrates an exceptionally high relative sensitivity in detecting various kinds while also displaying a deficient level of loss. The proposed sensor has an effective size of 38 µm2, a sensitivity of 99.9%, and a low confinement loss of 10-11 dB/m. To validate the usefulness of the proposed layout and establish its integrity, a detailed analysis is performed by contrasting the results of this study with the most current research published on photonic crystal fiber.","PeriodicalId":502336,"journal":{"name":"AlKadhum Journal of Science","volume":"305 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139179392","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}
Many semi-empirical methods are available in Gaussian 16. This patch enables analytical gradients and frequencies in addition to increasing efficiency by replacing the code from MOPAC open source, AM1 and PM3 technologies. In the case of a pure molecule, the values of total energy, bond energy, electronic energy, and nuclear energy (-170.893, -1496.855, -4332.708, 45) Kcal/mol have been successively added. After adding Si and P to the free Phthalocyanine molecule, the values transformed to (-133.30, -4987.486, -1294.027, 11.607) Kcal/mol. For the two resulting molecules (PcSi and PcP), the values became (-136.108, -4858.63, -9354.14, 79.930) Kcal/mol. Notably, the first number indicated an increase. Specifically, for total energy numbers, there was a decrease from -170.893 to -133.3 Kcal/mol when adding Si, and a decrease to -136.108 Kcal/mol when adding P. Overall, the energy value increased with both additions, but the bonding energy notably decreased with Si (-1496.855 to -4987.486 Kcal/mol) and P (-4858.63 Kcal/mol). Electronic energy increased from -4332.708 to -1294.027 Kcal/mol when Si was added. Nuclear energy decreased from 45.036 to 11.607 Kcal/mol when Si was added (increasing to 79.930 Kcal/mol with P). The Heat of Formation (H.o.F.) in Kcal/mol equaled 1565.04 when P was added, 1193.384 when Si was added, and 1531.528 when P was added again. The substantial impact of silicon on Phthalocyanine was evident. Furthermore, the dipole moment of the Phthalocyanine molecule, initially at D 3.687, decreased to D 2.093 and D 4.137 when Si was added first and P the second time, showcasing the significant impact of P due to its high atomic number. Determining the HOMO and LUMO and computing the values of Wavenumber, Wavelength, and Symmetry for the three molecules provided a clear illustration. The computation of electrical potential, electronic orbitals, and energy gap revealed an electronic density of 0.346 eV in the case of the free molecule H2Pc, 5.006 eV in the molecule PPc, and 5.660 eV in the case of SiPc. This offers a comprehensive understanding of the impact of adding P and Si to H2Pc.
高斯 16 中有许多半经验方法。该补丁除了通过替换 MOPAC 开源、AM1 和 PM3 技术的代码来提高效率外，还可以分析梯度和频率。在纯分子的情况下，先后加入了总能、键能、电子能和核能值（-170.893, -1496.855, -4332.708, 45）Kcal/mol。在游离的酞菁分子中加入 Si 和 P 后，数值转变为 (-133.30, -4987.486, -1294.027, 11.607) Kcal/mol。由此产生的两个分子（PcSi 和 PcP）的数值变为 (-136.108, -4858.63, -9354.14, 79.930) Kcal/mol。值得注意的是，第一个数字显示了增加。具体地说，在总能量方面，加入硅时，能量值从 -170.893 Kcal/mol 降至 -133.3 Kcal/mol，加入 P 时，能量值降至 -136.108 Kcal/mol。总体而言，加入两种物质时，能量值都有所增加，但加入硅（从 -1496.855 Kcal/mol 降至 -4987.486 Kcal/mol）和 P（从 -4858.63 Kcal/mol）时，键能明显降低。加入 Si 后，电子能从 -4332.708 Kcal/mol 增加到 -1294.027 Kcal/mol。加入硅时，核能从 45.036 Kcal/mol 降至 11.607 Kcal/mol（加入 P 时增至 79.930 Kcal/mol）。以 Kcal/mol 为单位的形成热（H.o.F.）在添加 P 时为 1565.04，添加 Si 时为 1193.384，再次添加 P 时为 1531.528。硅对酞菁的影响显而易见。此外，酞菁分子的偶极矩最初为 D 3.687，当第一次加入硅和第二次加入 P 时，偶极矩分别下降到 D 2.093 和 D 4.137，这表明 P 的原子序数高，对酞菁的影响很大。确定这三种分子的 HOMO 和 LUMO 以及计算它们的文波数、波长和对称性值都提供了清晰的说明。对电势、电子轨道和能隙的计算显示，自由分子 H2Pc 的电子密度为 0.346 eV，分子 PPc 为 5.006 eV，而 SiPc 为 5.660 eV。这有助于全面了解在 H2Pc 中添加 P 和 Si 的影响。
{"title":"Calculation of the Electronic Properties Phthalocyanine (H2Pc), Silicon Phthalocyanine (Sipc), Phosphorus Phthalocyanine (PPc) and Energy Gap by the AM1 Method","authors":"Ahmed mohammed Farhan","doi":"10.61710/akjs.v1i2.61","DOIUrl":"https://doi.org/10.61710/akjs.v1i2.61","url":null,"abstract":"Many semi-empirical methods are available in Gaussian 16. This patch enables analytical gradients and frequencies in addition to increasing efficiency by replacing the code from MOPAC open source, AM1 and PM3 technologies. In the case of a pure molecule, the values of total energy, bond energy, electronic energy, and nuclear energy (-170.893, -1496.855, -4332.708, 45) Kcal/mol have been successively added. After adding Si and P to the free Phthalocyanine molecule, the values transformed to (-133.30, -4987.486, -1294.027, 11.607) Kcal/mol. For the two resulting molecules (PcSi and PcP), the values became (-136.108, -4858.63, -9354.14, 79.930) Kcal/mol. Notably, the first number indicated an increase. Specifically, for total energy numbers, there was a decrease from -170.893 to -133.3 Kcal/mol when adding Si, and a decrease to -136.108 Kcal/mol when adding P. Overall, the energy value increased with both additions, but the bonding energy notably decreased with Si (-1496.855 to -4987.486 Kcal/mol) and P (-4858.63 Kcal/mol). Electronic energy increased from -4332.708 to -1294.027 Kcal/mol when Si was added. Nuclear energy decreased from 45.036 to 11.607 Kcal/mol when Si was added (increasing to 79.930 Kcal/mol with P). The Heat of Formation (H.o.F.) in Kcal/mol equaled 1565.04 when P was added, 1193.384 when Si was added, and 1531.528 when P was added again. The substantial impact of silicon on Phthalocyanine was evident. Furthermore, the dipole moment of the Phthalocyanine molecule, initially at D 3.687, decreased to D 2.093 and D 4.137 when Si was added first and P the second time, showcasing the significant impact of P due to its high atomic number. Determining the HOMO and LUMO and computing the values of Wavenumber, Wavelength, and Symmetry for the three molecules provided a clear illustration. The computation of electrical potential, electronic orbitals, and energy gap revealed an electronic density of 0.346 eV in the case of the free molecule H2Pc, 5.006 eV in the molecule PPc, and 5.660 eV in the case of SiPc. This offers a comprehensive understanding of the impact of adding P and Si to H2Pc.","PeriodicalId":502336,"journal":{"name":"AlKadhum Journal of Science","volume":"365 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139180033","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}
Murteza Hanoon Tuama, Wahhab Muslim Mashloosh, Hayder Albehadili, Murtadha A. Alazzawi, Mahmood A. Al-Shareeda
Opinion mining and emotion detection are two important techniques in natural language processing that have gained significant attention in recent years. Opinion mining is the process of identifying and extracting subjective information from text, such as opinions, attitudes, and emotions, while emotion detection is the process of identifying and extracting emotions from text. These techniques have a wide range of applications in various domains, including social media analysis, customer feedback analysis, and product reviews. This paper provides an overview of opinion mining and emotion detection techniques in natural language processing. We discuss the various approaches and methods used in opinion mining and emotion detection, including machine learning, deep learning, and natural language processing techniques. We also explore the challenges and limitations of these techniques, including the subjectivity of language, cultural differences, and the lack of labeled data. Furthermore, we examine the current state of the art in opinion mining and emotion detection, highlighting recent research and developments in these areas. We also discuss the potential applications of these techniques in various domains, including marketing, healthcare, and social media analysis. Overall, this paper provides a comprehensive overview of opinion mining and emotion detection in natural language processing. It provides insights into the methods, challenges, and potential applications of these techniques, and highlights the importance of these techniques in understanding and analyzing subjective information in text.
{"title":"Beyond Polarity: The Potential Applications and Impacts of Sentiment Analysis and Emotion Detection","authors":"Murteza Hanoon Tuama, Wahhab Muslim Mashloosh, Hayder Albehadili, Murtadha A. Alazzawi, Mahmood A. Al-Shareeda","doi":"10.61710/akjs.v1i2.51","DOIUrl":"https://doi.org/10.61710/akjs.v1i2.51","url":null,"abstract":"Opinion mining and emotion detection are two important techniques in natural language processing that have gained significant attention in recent years. Opinion mining is the process of identifying and extracting subjective information from text, such as opinions, attitudes, and emotions, while emotion detection is the process of identifying and extracting emotions from text. These techniques have a wide range of applications in various domains, including social media analysis, customer feedback analysis, and product reviews. This paper provides an overview of opinion mining and emotion detection techniques in natural language processing. We discuss the various approaches and methods used in opinion mining and emotion detection, including machine learning, deep learning, and natural language processing techniques. We also explore the challenges and limitations of these techniques, including the subjectivity of language, cultural differences, and the lack of labeled data. Furthermore, we examine the current state of the art in opinion mining and emotion detection, highlighting recent research and developments in these areas. We also discuss the potential applications of these techniques in various domains, including marketing, healthcare, and social media analysis. Overall, this paper provides a comprehensive overview of opinion mining and emotion detection in natural language processing. It provides insights into the methods, challenges, and potential applications of these techniques, and highlights the importance of these techniques in understanding and analyzing subjective information in text.","PeriodicalId":502336,"journal":{"name":"AlKadhum Journal of Science","volume":"76 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139178959","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}
Since December 2019, the world has been struggling Against the discovered virus called Covid-19, which Its symptoms are similar to pneumonia. Being highly contagious, it is It spread all over the world, hence the World Health Organization By declaring this disease as a global pandemic. some Patients infected with this virus suffer from severe symptoms And deadly. Hence the importance of early detection of Coronavirus (COVID-19). COVID-19 is a disease that affects the respiratory system of the human body, and detecting this disease is complex and one of the main challenges. This work proposed a technique to detect COVID-19 by integrating multifocal images based on wavelet transduction. So, to achieve the detection of COVID-19, Magnet resonant imagery (MRI) and computation tomography (CT) have been used. The multifocal image was included to support the diagnosis made by the clinicians. The seven wave-based algorithms bior2.2, coif2, db2, dmey, rbio2.2, sym4, and haar, respectively, were used to achieve a range of results. This approach effectively combines the data obtained from CT and MRI scans to produce a merged image that improves disease diagnosis efficiency by using MATLAB to determine the efficiency of the algorithm. The signal-to-noise ratio (PSNR) and the entropy factor are used to measure the image fusion efficiency. The statistical analysis of the final images demonstrated the superiority of the image attributes over both the CT image and the MRI.
{"title":"Detection of COVID-19 using wavelet transform","authors":"Falah A. Bida, Hadi R. Ali","doi":"10.61710/akjs.v1i2.49","DOIUrl":"https://doi.org/10.61710/akjs.v1i2.49","url":null,"abstract":"Since December 2019, the world has been struggling Against the discovered virus called Covid-19, which Its symptoms are similar to pneumonia. Being highly contagious, it is It spread all over the world, hence the World Health Organization By declaring this disease as a global pandemic. some Patients infected with this virus suffer from severe symptoms And deadly. Hence the importance of early detection of Coronavirus (COVID-19). COVID-19 is a disease that affects the respiratory system of the human body, and detecting this disease is complex and one of the main challenges. This work proposed a technique to detect COVID-19 by integrating multifocal images based on wavelet transduction. So, to achieve the detection of COVID-19, Magnet resonant imagery (MRI) and computation tomography (CT) have been used. The multifocal image was included to support the diagnosis made by the clinicians. The seven wave-based algorithms bior2.2, coif2, db2, dmey, rbio2.2, sym4, and haar, respectively, were used to achieve a range of results. This approach effectively combines the data obtained from CT and MRI scans to produce a merged image that improves disease diagnosis efficiency by using MATLAB to determine the efficiency of the algorithm. The signal-to-noise ratio (PSNR) and the entropy factor are used to measure the image fusion efficiency. The statistical analysis of the final images demonstrated the superiority of the image attributes over both the CT image and the MRI.","PeriodicalId":502336,"journal":{"name":"AlKadhum Journal of Science","volume":"66 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139179851","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}
Ali Abbas Hadi Al-Shukrawi, Layla safwat Jamil, Israa Akram Alzuabidi, Ahmed Salman Al-Gamal, S. A. M. Noah, Mohammed Kamrul Hasan, S. M. Al-Ghuribi, Rabiu Aliyu, Zainab Kadhim Jabal, A. A. Ahmed
In this paper, a systematic literature review was provided that investigated the present evidence regarding extremist words in Arabic opinion mining methods. This study aimed to perform a Systematic Literature Review (SLR) in order to detect, evaluate, and synthesize the existing evidence regarding opinion mining techniques for extremist Arabic text. From the SLR, it is evident that opinion-mining techniques have several opportunities for detecting extremism in the Arabic text. Over the past few years, multimedia sentiment analysis has gained traction as visual content is becoming more incorporated into social media networking. Opinion mining is the process of identifying, extracting, and categorizing views about anything. It is a sort of Natural Language Processing (NLP) used to track public sentiment about a certain law, policy, or marketing, for example. It entails the creation of a method for collecting and analyzing comments and opinions concerning legislation, regulations, policies, and so on that are posted on social media. The process of information extraction is critical since it is both a beneficial tool and a difficult undertaking. In this article, we have examined the recent and advanced methodologies to extract sentiment from a web-wide item, opinion-mining methods must be automated. Also, we have analyzed the novel Artificial Intelligence and lexical-based algorithms for sentiment analysis. These methodologies find better applications in the customer feedback analysis of any organization.
{"title":"Opinion Mining in Arabic Extremism Texts: A Systematic Literature Review","authors":"Ali Abbas Hadi Al-Shukrawi, Layla safwat Jamil, Israa Akram Alzuabidi, Ahmed Salman Al-Gamal, S. A. M. Noah, Mohammed Kamrul Hasan, S. M. Al-Ghuribi, Rabiu Aliyu, Zainab Kadhim Jabal, A. A. Ahmed","doi":"10.61710/akjs.v1i2.60","DOIUrl":"https://doi.org/10.61710/akjs.v1i2.60","url":null,"abstract":"In this paper, a systematic literature review was provided that investigated the present evidence regarding extremist words in Arabic opinion mining methods. This study aimed to perform a Systematic Literature Review (SLR) in order to detect, evaluate, and synthesize the existing evidence regarding opinion mining techniques for extremist Arabic text. From the SLR, it is evident that opinion-mining techniques have several opportunities for detecting extremism in the Arabic text. Over the past few years, multimedia sentiment analysis has gained traction as visual content is becoming more incorporated into social media networking. Opinion mining is the process of identifying, extracting, and categorizing views about anything. It is a sort of Natural Language Processing (NLP) used to track public sentiment about a certain law, policy, or marketing, for example. It entails the creation of a method for collecting and analyzing comments and opinions concerning legislation, regulations, policies, and so on that are posted on social media. The process of information extraction is critical since it is both a beneficial tool and a difficult undertaking. In this article, we have examined the recent and advanced methodologies to extract sentiment from a web-wide item, opinion-mining methods must be automated. Also, we have analyzed the novel Artificial Intelligence and lexical-based algorithms for sentiment analysis. These methodologies find better applications in the customer feedback analysis of any organization.","PeriodicalId":502336,"journal":{"name":"AlKadhum Journal of Science","volume":"168 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139179710","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}
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