Omar Abdullatif Jassim, Mohammed Jawad Abed, Zenah Hadi Saied Saied
With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor sets, resulting in four trained models. The test sets are used to evaluate the trained models using many evaluation metrics (accuracy, TPR, FNR, PPR, FDR). Results of Google Net model indicate the high performance of the designed models with 99.34% and 99.76% accuracies for indoor and outdoor datasets, respectively. For Mobile Net models, the result accuracies are 99.27% and 99.68% for indoor and outdoor sets, respectively. The proposed methodology is compared with similar ones in the field of object recognition and image classification, and the comparative study proves the transcendence of the propsed system.
{"title":"Indoor/Outdoor Deep Learning Based Image Classification for Object Recognition Applications","authors":"Omar Abdullatif Jassim, Mohammed Jawad Abed, Zenah Hadi Saied Saied","doi":"10.21123/bsj.2023.8177","DOIUrl":"https://doi.org/10.21123/bsj.2023.8177","url":null,"abstract":"With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor sets, resulting in four trained models. The test sets are used to evaluate the trained models using many evaluation metrics (accuracy, TPR, FNR, PPR, FDR). Results of Google Net model indicate the high performance of the designed models with 99.34% and 99.76% accuracies for indoor and outdoor datasets, respectively. For Mobile Net models, the result accuracies are 99.27% and 99.68% for indoor and outdoor sets, respectively. The proposed methodology is compared with similar ones in the field of object recognition and image classification, and the comparative study proves the transcendence of the propsed system.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"92 13","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138600174","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}
Ira Aini Dania, A. Rambe, Urip Harahap, E. Effendy, Tuti Wahmurti, Syafruddin Ilyas, M. Rusda, M. Amin
There is a correlation between the occurrence of anxiety and the production of inflammatory mediators, and red ginger rhizome is a well-known herbal product with a high content of phenolic and flavonoid compounds that can be used as anti-inflammatories and antioxidants. The aim of study to evaluate the effect of red ginger as antianxiety in mice (Mus musculus) BALB/c strain by measuring levels of TNF-α, IL-6 and IL-10. Anxiety model mice were carried out by giving treatment with the Forced Swimming Test (FST) for 7 days then assessed by carrying out the Elevated Plus Maze for Mice (EPM) test for one day. After the treatment, the anxiety mice model was made, followed by administration of red ginger ethanol extract therapy for 14 days. The distribution of the experimental animal model groups was divided into control groups (KN, K-, K+) and treatment groups (P, P2, P3).There was a significant difference the decreased of the TNF-α levels at the all of treatment groups with red ginger rhizome extract (P1, P2, P3) compared with the control groups (KN, K-) (p<0.05), the significantly decreased of IL-6 levels in the three doses treatment group (P1, P2, P3) compared to the control group (K-, K+) (p <0.05) and an increase in IL-10 levels in the 50 mg treatment group compared to group K -, statistically not significant (p>0.05).In overall, this study suggests that FST stimulation will create anxiety symptoms and behavior as well as impact cytokine levels, namely elevated levels of TNF-α and IL-6. Giving red ginger ethanol extract has the potential to be researched further for reducing anxiety symptoms because it can block pro-inflammatory cytokines by significantly decreased levels of TNF-α, IL-6, and increased IL-10 cytokines a brief abstract about your paper’s subject of study.
{"title":"Red Ginger's Anti-Anxiety Effect on BALB/c Strain Mice (Mus musculus) Pro-Inflammatory and Anti-Inflammatory Measurements as Anxiety Model","authors":"Ira Aini Dania, A. Rambe, Urip Harahap, E. Effendy, Tuti Wahmurti, Syafruddin Ilyas, M. Rusda, M. Amin","doi":"10.21123/bsj.2023.9035","DOIUrl":"https://doi.org/10.21123/bsj.2023.9035","url":null,"abstract":"There is a correlation between the occurrence of anxiety and the production of inflammatory mediators, and red ginger rhizome is a well-known herbal product with a high content of phenolic and flavonoid compounds that can be used as anti-inflammatories and antioxidants. The aim of study to evaluate the effect of red ginger as antianxiety in mice (Mus musculus) BALB/c strain by measuring levels of TNF-α, IL-6 and IL-10. Anxiety model mice were carried out by giving treatment with the Forced Swimming Test (FST) for 7 days then assessed by carrying out the Elevated Plus Maze for Mice (EPM) test for one day. After the treatment, the anxiety mice model was made, followed by administration of red ginger ethanol extract therapy for 14 days. The distribution of the experimental animal model groups was divided into control groups (KN, K-, K+) and treatment groups (P, P2, P3).There was a significant difference the decreased of the TNF-α levels at the all of treatment groups with red ginger rhizome extract (P1, P2, P3) compared with the control groups (KN, K-) (p<0.05), the significantly decreased of IL-6 levels in the three doses treatment group (P1, P2, P3) compared to the control group (K-, K+) (p <0.05) and an increase in IL-10 levels in the 50 mg treatment group compared to group K -, statistically not significant (p>0.05).In overall, this study suggests that FST stimulation will create anxiety symptoms and behavior as well as impact cytokine levels, namely elevated levels of TNF-α and IL-6. Giving red ginger ethanol extract has the potential to be researched further for reducing anxiety symptoms because it can block pro-inflammatory cytokines by significantly decreased levels of TNF-α, IL-6, and increased IL-10 cytokines a brief abstract about your paper’s subject of study.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"62 7","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138598434","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}
Sevara Khazratkulova, Nodira T. Zokirova, Busora Mukhamedova, Basant Lal, Orifjon Khamidov, E. Berdimurodov, Guloy K Alieva, Ahmad Hosseini-Bandegharaei, Nizomiddin Aliev
In this research work, the novel polymer base on acrylamide N-methylene lactic and glycolic acid was synthesized and its structural performances were identified by the IR, 1H NMR and 13C NMR spectroscopic investigations. The influencing factors and kinetics of polymerization, viscosity performance were studied and quantum chemical calculations were used to identify the correlation between the structure and properties. It was determined that the polymerization rate of the examined monomers in an aqueous solution, in the presence of DAA, adheres to the standard rules for radical polymerization of acrylamide monomers in solution. An investigation into the pH solution's impact on the kinetics of radical polymerization of acrylamido-N-methylene glycolic and acrylamido-N-methylene lactic acids revealed an extreme dependence with a minimum in a neutral medium. It was found the linear correlation between pH and viscosity. The physical and chemical performance of this polymer depends on the structural parameters related the results of quantum chemical calculation. Biological tests conducted on polyacrylamido-N-methylene lactic acid indicated its potential as a plant growth stimulator. The polymeric form of lactic acid was found to enhance the growth of Dustlik variety wheat seedlings by 40% more efficiently than lactic acid alone.
本研究合成了以丙烯酰胺n -亚甲基乳酸和乙醇酸为基料的新型聚合物，并通过IR、1H NMR和13C NMR对其结构性能进行了表征。研究了聚合动力学、粘度性能的影响因素，并利用量子化学计算确定了结构与性能之间的相关性。经测定，在DAA存在的情况下，所测单体在水溶液中的聚合速率符合丙烯酰胺单体在溶液中自由基聚合的标准规则。对pH溶液对丙烯酰胺- n -亚甲基乙醇酸和丙烯酰胺- n -亚甲基乳酸自由基聚合动力学影响的研究表明，在中性介质中，pH溶液对丙烯酰胺- n -亚甲基乳酸自由基聚合动力学的影响极小。发现pH值与粘度呈线性相关。该聚合物的物理和化学性能取决于与量子化学计算结果相关的结构参数。对聚丙烯酰胺- n -亚甲基乳酸进行的生物学试验表明，聚丙烯酰胺- n -亚甲基乳酸具有促进植物生长的潜力。研究发现，与单独使用乳酸相比，聚合物形式的乳酸对Dustlik品种小麦幼苗的生长促进效率提高了40%。
{"title":"Polymerization of Acrylamide N-methylene Lactic and Glycolic Acid","authors":"Sevara Khazratkulova, Nodira T. Zokirova, Busora Mukhamedova, Basant Lal, Orifjon Khamidov, E. Berdimurodov, Guloy K Alieva, Ahmad Hosseini-Bandegharaei, Nizomiddin Aliev","doi":"10.21123/bsj.2023.9076","DOIUrl":"https://doi.org/10.21123/bsj.2023.9076","url":null,"abstract":"In this research work, the novel polymer base on acrylamide N-methylene lactic and glycolic acid was synthesized and its structural performances were identified by the IR, 1H NMR and 13C NMR spectroscopic investigations. The influencing factors and kinetics of polymerization, viscosity performance were studied and quantum chemical calculations were used to identify the correlation between the structure and properties. It was determined that the polymerization rate of the examined monomers in an aqueous solution, in the presence of DAA, adheres to the standard rules for radical polymerization of acrylamide monomers in solution. An investigation into the pH solution's impact on the kinetics of radical polymerization of acrylamido-N-methylene glycolic and acrylamido-N-methylene lactic acids revealed an extreme dependence with a minimum in a neutral medium. It was found the linear correlation between pH and viscosity. The physical and chemical performance of this polymer depends on the structural parameters related the results of quantum chemical calculation. Biological tests conducted on polyacrylamido-N-methylene lactic acid indicated its potential as a plant growth stimulator. The polymeric form of lactic acid was found to enhance the growth of Dustlik variety wheat seedlings by 40% more efficiently than lactic acid alone.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"60 4","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138598385","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 Wang-Ball polynomials operational matrices of the derivatives are used in this study to solve singular perturbed second-order differential equations (SPSODEs) with boundary conditions. Using the matrix of Wang-Ball polynomials, the main singular perturbation problem is converted into linear algebraic equation systems. The coefficients of the required approximate solution are obtained from the solution of this system. The residual correction approach was also used to improve an error, and the results were compared to other reported numerical methods. Several examples are used to illustrate both the reliability and usefulness of the Wang-Ball operational matrices. The Wang Ball approach has the ability to improve the outcomes by minimizing the degree of error between approximate and exact solutions. The Wang-Ball series has shown its usefulness in solving any real-life scenario model as first- or second-order differential equations (DEs).
{"title":"A Numerical scheme to Solve Boundary Value Problems Involving Singular Perturbation","authors":"Hussain A. Alaidroos, A. Kherd, Salim F. Bamsaoud","doi":"10.21123/bsj.2023.6409","DOIUrl":"https://doi.org/10.21123/bsj.2023.6409","url":null,"abstract":"The Wang-Ball polynomials operational matrices of the derivatives are used in this study to solve singular perturbed second-order differential equations (SPSODEs) with boundary conditions. Using the matrix of Wang-Ball polynomials, the main singular perturbation problem is converted into linear algebraic equation systems. The coefficients of the required approximate solution are obtained from the solution of this system. The residual correction approach was also used to improve an error, and the results were compared to other reported numerical methods. Several examples are used to illustrate both the reliability and usefulness of the Wang-Ball operational matrices. The Wang Ball approach has the ability to improve the outcomes by minimizing the degree of error between approximate and exact solutions. The Wang-Ball series has shown its usefulness in solving any real-life scenario model as first- or second-order differential equations (DEs).","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"137 31","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138598808","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}
Asmah Indrawati, Abdul Rahman, Erwin Pane, Muhathir
The general health of palm trees, encompassing the roots, stems, and leaves, significantly impacts palm oil production, therefore, meticulous attention is needed to achieve optimal yield. One of the challenges encountered in sustaining productive crops is the prevalence of pests and diseases afflicting oil palm plants. These diseases can detrimentally influence growth and development, leading to decreased productivity. Oil palm productivity is closely related to the conditions of its leaves, which play a vital role in photosynthesis. This research employed a comprehensive dataset of 1,230 images, consisting of 410 showing leaves, another 410 depicting bagworm infestations, and an additional 410 displaying caterpillar infestations. Furthermore, the major objective was to formulate a deep learning model for the identification of diseases and pests affecting oil palm leaves, using image analysis techniques to facilitate pest management practices. To address the core problem under investigation, the GoogLeNet deep learning approach was applied, alongside various hyperparameters. The classification experiments were executed across 16 trials, each capped at a computational timeframe of 10 minutes, and the predominant duration spanned from 2 to 7 minutes. The results, particularly derived from the superior performance in Model 4 (M4), showed evaluation accuracy, precision, recall, and F1-score rates of 93.22%, 93.33%, 93.95%, and 93.15%, respectively. These were highly satisfactory, warranting their application in oil palm companies to enhance the management of pest and disease attacks.
{"title":"Classification of Diseases in Oil Palm Leaves Using the GoogLeNet Model","authors":"Asmah Indrawati, Abdul Rahman, Erwin Pane, Muhathir","doi":"10.21123/bsj.2023.8547","DOIUrl":"https://doi.org/10.21123/bsj.2023.8547","url":null,"abstract":"The general health of palm trees, encompassing the roots, stems, and leaves, significantly impacts palm oil production, therefore, meticulous attention is needed to achieve optimal yield. One of the challenges encountered in sustaining productive crops is the prevalence of pests and diseases afflicting oil palm plants. These diseases can detrimentally influence growth and development, leading to decreased productivity. Oil palm productivity is closely related to the conditions of its leaves, which play a vital role in photosynthesis. This research employed a comprehensive dataset of 1,230 images, consisting of 410 showing leaves, another 410 depicting bagworm infestations, and an additional 410 displaying caterpillar infestations. Furthermore, the major objective was to formulate a deep learning model for the identification of diseases and pests affecting oil palm leaves, using image analysis techniques to facilitate pest management practices. To address the core problem under investigation, the GoogLeNet deep learning approach was applied, alongside various hyperparameters. The classification experiments were executed across 16 trials, each capped at a computational timeframe of 10 minutes, and the predominant duration spanned from 2 to 7 minutes. The results, particularly derived from the superior performance in Model 4 (M4), showed evaluation accuracy, precision, recall, and F1-score rates of 93.22%, 93.33%, 93.95%, and 93.15%, respectively. These were highly satisfactory, warranting their application in oil palm companies to enhance the management of pest and disease attacks.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"118 47","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138599519","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}
H. M. Abdulhadi, Y. A. A. S. Aldeen, Maryam A. Yousif, Mays jalal Jaseem, Syed Hamid Hussain Madni
Smart cities have recently undergone a fundamental evolution that has greatly increased their potentials. In reality, recent advances in the Internet of Things (IoT) have created new opportunities by solving a number of critical issues that are allowing innovations for smart cities as well as the creation and computerization of cutting-edge services and applications for the many city partners. In order to further the development of smart cities toward compelling sharing and connection, this study will explore the information innovation in smart cities in light of the Internet of Things (IoT) and cloud computing (CC). IoT data is first collected in the context of smart cities. The data that is gathered is uniform. The Internet of Things, which enables gadgets to connect with one another mostly without human involvement, is made possible by AI. In line with this, The Ad Hoc Routing Function (ARF) AI computation is used for multi-rule simplification, the use of Adaptive Cloud Computing Virtual Machine Asset Allotment Technique (ACC-VMRA) is advised. To confirm its viability, the applied developments of IoT and CC in smart cities is examined and duplicated. The experiment results show that the recommended enhancement calculation is more productive than other currently used methods.
智慧城市最近经历了一场根本性的演变，极大地增加了它们的潜力。实际上，物联网(IoT)的最新进展通过解决一些关键问题创造了新的机会，这些问题使智慧城市能够创新，并为许多城市合作伙伴创建和计算机化尖端服务和应用程序。为了进一步推动智慧城市向共享和连接的方向发展，本研究将探讨物联网(IoT)和云计算(CC)在智慧城市中的信息创新。物联网数据首先是在智慧城市的背景下收集的。收集的数据是统一的。物联网(Internet of Things)是一种无需人工干预就能让设备相互连接的技术，它是由人工智能实现的。因此，采用Ad Hoc Routing Function (ARF) AI计算进行多规则简化，建议采用自适应云计算虚拟机资产分配技术(ACC-VMRA)。为了确认其可行性，对物联网和CC在智慧城市中的应用发展进行了检查和复制。实验结果表明，所推荐的增强计算比目前使用的其他方法效率更高。
{"title":"Enhancing Smart Cities with IoT and Cloud Computing: A Study on Integrating Wireless Ad Hoc Networks for Efficient Communication","authors":"H. M. Abdulhadi, Y. A. A. S. Aldeen, Maryam A. Yousif, Mays jalal Jaseem, Syed Hamid Hussain Madni","doi":"10.21123/bsj.2023.9277","DOIUrl":"https://doi.org/10.21123/bsj.2023.9277","url":null,"abstract":"Smart cities have recently undergone a fundamental evolution that has greatly increased their potentials. In reality, recent advances in the Internet of Things (IoT) have created new opportunities by solving a number of critical issues that are allowing innovations for smart cities as well as the creation and computerization of cutting-edge services and applications for the many city partners. In order to further the development of smart cities toward compelling sharing and connection, this study will explore the information innovation in smart cities in light of the Internet of Things (IoT) and cloud computing (CC). IoT data is first collected in the context of smart cities. The data that is gathered is uniform. The Internet of Things, which enables gadgets to connect with one another mostly without human involvement, is made possible by AI. In line with this, The Ad Hoc Routing Function (ARF) AI computation is used for multi-rule simplification, the use of Adaptive Cloud Computing Virtual Machine Asset Allotment Technique (ACC-VMRA) is advised. To confirm its viability, the applied developments of IoT and CC in smart cities is examined and duplicated. The experiment results show that the recommended enhancement calculation is more productive than other currently used methods.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"96 2","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138599790","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}
Khayit Turaev, Dilnoza Shavkatova, N. Amanova, Mohanad Hatem Shadhar, E. Berdimurodov, Nessipkhan Bektenov, Ahmad Hosseini-Bandegharae
In this investigative endeavor, a novel concrete variety incorporating sulfur-2,4-dinitrophenylhydrazine modification was developed, and its diverse attributes were explored. This innovative concrete was produced using sulfur-2,4-dinitrophenylhydrazine modification and an array of components. The newly created sulfur-2,4-dinitrophenylhydrazine modifier was synthesized. The surface texture resulting from this modifier was examined using SEM and EDS techniques. The component ratios within concrete, chemical and physical traits derived from the sulfur-2,4-dinitrophenylhydrazine modifier, chemical and corrosion resistance of concrete, concrete stability against water absorption, concrete resilience against freezing, physical and mechanical properties, durability, elastic modulus, and thermal expansion coefficient of the examined sulfur-infused concrete were assessed. The acquired results also substantiated that the thermal expansion coefficient value for sulfur-2,4-dinitrophenylhydrazine modified concrete was 14.8×10-6/0C. The average deformation of the analyzed concrete was 0.0026-0.0051, indicating a superior deformation performance compared to conventional concretes. Concrete with smaller aggregate sizes exhibited greater density, specifically 2283 kg/m3. The concrete density decreased gradually with an increase in aggregate size. The stability of sulfur-2,4-dinitrophenylhydrazine modified concrete was remarkably high in various aggressive environments. EDS analysis revealed that carbon atoms constituted 56.63% of the total mass, while sulfur made up 33.91% of the total mass. The obtained SEM outcomes demonstrated that the sulfur-2,4-dinitrophenylhydrazine modifier exhibited a more porous structure, devoid of crystalline formations. The sulfur-2,4-dinitrophenylhydrazine modification experienced a single-stage thermal mass loss, with the mass loss events being endothermic in nature. The IR findings verified the presence of amino functional groups (connected melamine ring) and the establishment of polymer sulfur chains.
{"title":"Application of Sulfur-2,4-dinitrophenylhydrazine as Modifier for Producing an Advantageous Concrete","authors":"Khayit Turaev, Dilnoza Shavkatova, N. Amanova, Mohanad Hatem Shadhar, E. Berdimurodov, Nessipkhan Bektenov, Ahmad Hosseini-Bandegharae","doi":"10.21123/bsj.2023.9038","DOIUrl":"https://doi.org/10.21123/bsj.2023.9038","url":null,"abstract":"In this investigative endeavor, a novel concrete variety incorporating sulfur-2,4-dinitrophenylhydrazine modification was developed, and its diverse attributes were explored. This innovative concrete was produced using sulfur-2,4-dinitrophenylhydrazine modification and an array of components. The newly created sulfur-2,4-dinitrophenylhydrazine modifier was synthesized. The surface texture resulting from this modifier was examined using SEM and EDS techniques. The component ratios within concrete, chemical and physical traits derived from the sulfur-2,4-dinitrophenylhydrazine modifier, chemical and corrosion resistance of concrete, concrete stability against water absorption, concrete resilience against freezing, physical and mechanical properties, durability, elastic modulus, and thermal expansion coefficient of the examined sulfur-infused concrete were assessed. The acquired results also substantiated that the thermal expansion coefficient value for sulfur-2,4-dinitrophenylhydrazine modified concrete was 14.8×10-6/0C. The average deformation of the analyzed concrete was 0.0026-0.0051, indicating a superior deformation performance compared to conventional concretes. Concrete with smaller aggregate sizes exhibited greater density, specifically 2283 kg/m3. The concrete density decreased gradually with an increase in aggregate size. The stability of sulfur-2,4-dinitrophenylhydrazine modified concrete was remarkably high in various aggressive environments. EDS analysis revealed that carbon atoms constituted 56.63% of the total mass, while sulfur made up 33.91% of the total mass. The obtained SEM outcomes demonstrated that the sulfur-2,4-dinitrophenylhydrazine modifier exhibited a more porous structure, devoid of crystalline formations. The sulfur-2,4-dinitrophenylhydrazine modification experienced a single-stage thermal mass loss, with the mass loss events being endothermic in nature. The IR findings verified the presence of amino functional groups (connected melamine ring) and the establishment of polymer sulfur chains.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"9 12","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138598278","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}
In information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compared to traditional image filtering techniques. This paper aimed to utilize a specific CNN architecture known as AlexNet for the fingerprint-matching task. Using such an architecture, this study has extracted the significant features of the fingerprint image, generated a key based on such a biometric feature of the image, and stored it in a reference database. Then, using Cosine similarity and Hamming Distance measures, the testing fingerprints have been matched with a reference. Using the FVC2002 database, the proposed method showed a False Acceptance Rate (FAR) of 2.09% and a False Rejection Rate (FRR) of 2.81%. Comparing these results against other studies that utilized traditional approaches such as the Fuzzy Vault has demonstrated the efficacy of CNN in terms of fingerprint matching. It is also emphasizing the usefulness of using Cosine similarity and Hamming Distance in terms of matching.
{"title":"AlexNet Convolutional Neural Network Architecture with Cosine and Hamming Similarity/Distance Measures for Fingerprint Biometric Matching","authors":"Ahmed Al-jumaili, Huda Kadhim Tayyeh, A. Alsadoon","doi":"10.21123/bsj.2023.8362","DOIUrl":"https://doi.org/10.21123/bsj.2023.8362","url":null,"abstract":"In information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compared to traditional image filtering techniques. This paper aimed to utilize a specific CNN architecture known as AlexNet for the fingerprint-matching task. Using such an architecture, this study has extracted the significant features of the fingerprint image, generated a key based on such a biometric feature of the image, and stored it in a reference database. Then, using Cosine similarity and Hamming Distance measures, the testing fingerprints have been matched with a reference. Using the FVC2002 database, the proposed method showed a False Acceptance Rate (FAR) of 2.09% and a False Rejection Rate (FRR) of 2.81%. Comparing these results against other studies that utilized traditional approaches such as the Fuzzy Vault has demonstrated the efficacy of CNN in terms of fingerprint matching. It is also emphasizing the usefulness of using Cosine similarity and Hamming Distance in terms of matching.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"13 9","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138598576","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}
Anton S. Chepurnenko, Tatiana N. Kondratieva, Ebrahim Al-Wali
Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of the study is the generated data sets obtained on the basis of theoretical stress relaxation curves. Tables of initial data for training models for all samples are presented, a statistical analysis of the characteristics of the initial data sets is carried out. The total number of numerical experiments for all samples was 346020 variations. When developing the models, CatBoost artificial intelligence methods were used, regularization methods (Weight Decay, Decoupled Weight Decay Regularization, Augmentation) were used to improve the accuracy of the model, and the Z-Score method was used to normalize the data. As a result of the study, intelligent models were developed to determine the rheological parameters of polymers included in the generalized non-linear Maxwell-Gurevich equation (initial relaxation viscosity, velocity modulus) using generated data sets for the EDT-10 epoxy binder as an example. Based on the results of testing the models, the quality of the models was assessed, graphs of forecasts for trainees and test samples, graphs of forecast errors were plotted. Intelligent models are based on the CatBoost algorithm and implemented in the Jupyter Notebook environment in Python. The constructed models have passed the quality assessment according to the following metrics: MAE, MSE, RMSE, MAPE. The maximum value of model error predictions was 0.86 for the MAPE metric, and the minimum value of model error predictions was 0.001 for the MSE metric. Model performance estimates obtained during testing are valid.
{"title":"Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods","authors":"Anton S. Chepurnenko, Tatiana N. Kondratieva, Ebrahim Al-Wali","doi":"10.21123/bsj.2023.8819","DOIUrl":"https://doi.org/10.21123/bsj.2023.8819","url":null,"abstract":"Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of the study is the generated data sets obtained on the basis of theoretical stress relaxation curves. Tables of initial data for training models for all samples are presented, a statistical analysis of the characteristics of the initial data sets is carried out. The total number of numerical experiments for all samples was 346020 variations. When developing the models, CatBoost artificial intelligence methods were used, regularization methods (Weight Decay, Decoupled Weight Decay Regularization, Augmentation) were used to improve the accuracy of the model, and the Z-Score method was used to normalize the data. As a result of the study, intelligent models were developed to determine the rheological parameters of polymers included in the generalized non-linear Maxwell-Gurevich equation (initial relaxation viscosity, velocity modulus) using generated data sets for the EDT-10 epoxy binder as an example. Based on the results of testing the models, the quality of the models was assessed, graphs of forecasts for trainees and test samples, graphs of forecast errors were plotted. Intelligent models are based on the CatBoost algorithm and implemented in the Jupyter Notebook environment in Python. The constructed models have passed the quality assessment according to the following metrics: MAE, MSE, RMSE, MAPE. The maximum value of model error predictions was 0.86 for the MAPE metric, and the minimum value of model error predictions was 0.001 for the MSE metric. Model performance estimates obtained during testing are valid.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"13 2","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138598583","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}
Secure storage of confidential medical information is critical to healthcare organizations seeking to protect patient's privacy and comply with regulatory requirements. This paper presents a new scheme for secure storage of medical data using Chaskey cryptography and blockchain technology. The system uses Chaskey encryption to ensure integrity and confidentiality of medical data, blockchain technology to provide a scalable and decentralized storage solution. The system also uses Bflow segmentation and vertical segmentation technologies to enhance scalability and manage the stored data. In addition, the system uses smart contracts to enforce access control policies and other security measures. The description of the system detailing and provide an analysis of its security and performance characteristics. The resulting images were tested against a number of important metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), bit error rate (BER), Signal-to-Noise Ratio (SNR), Normalization Correlation (NC) and Structural Similarity Index (SSIM). Our results showing that the system provides a highly secure and scalable solution for storing confidential medical data, with potential applications in a wide range of healthcare settings.
{"title":"A Novel System for Confidential Medical Data Storage Using Chaskey Encryption and Blockchain Technology","authors":"Aymen Mudheher Badr, L. Fourati, Samiha Ayed","doi":"10.21123/bsj.2023.9203","DOIUrl":"https://doi.org/10.21123/bsj.2023.9203","url":null,"abstract":"Secure storage of confidential medical information is critical to healthcare organizations seeking to protect patient's privacy and comply with regulatory requirements. This paper presents a new scheme for secure storage of medical data using Chaskey cryptography and blockchain technology. The system uses Chaskey encryption to ensure integrity and confidentiality of medical data, blockchain technology to provide a scalable and decentralized storage solution. The system also uses Bflow segmentation and vertical segmentation technologies to enhance scalability and manage the stored data. In addition, the system uses smart contracts to enforce access control policies and other security measures. The description of the system detailing and provide an analysis of its security and performance characteristics. The resulting images were tested against a number of important metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), bit error rate (BER), Signal-to-Noise Ratio (SNR), Normalization Correlation (NC) and Structural Similarity Index (SSIM). Our results showing that the system provides a highly secure and scalable solution for storing confidential medical data, with potential applications in a wide range of healthcare settings.","PeriodicalId":8687,"journal":{"name":"Baghdad Science Journal","volume":"94 14","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138599843","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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