This study gains insight into landfill sites with the observance of all the political, economic and environmental difficulties for the implementing appropriate site measures by adopting a collection of geospatial technique and weighted linear combination (WLC) in TqaTaq sub-district. In the current study, there are several areas determined as appropriate sites for landfill location. In this study, the criteria of distance from the roads, the city center, rivers, surface water, and land use map were used. According to this analysis, only 25.21% of the TaqTaq sub district is suitable for a landfill. Thus, basing on the findings, 20.93% of the concerned sub-district is regarded as least adequate site for this mission, whereas only 3.25% of the area is regarded as moderate suitable. Thus, this study has found out that 1.03% area is the most suitable. The majority of suitable area was located in the North of the Town, where waste production is more than other locations. It should be noted that based on the outcome of this study, the amount of waste produced in the TaqTaq Town for the next 10 years, from 2022 to 2032, is predicted to be about 4080 tons. According to the density calculated for the waste of this area and considering the height of 4 m for the landfill center, in the next 10 years, about 3000 m2 of land is required for the landfill location. Since the suitable area found in this research is about 15 hectares.
{"title":"Landfill Site Selection for Solid Waste Using GIS-based Multi-Criteria Spatial Modeling","authors":"Rostam S. Aziz","doi":"10.14500/aro.11017","DOIUrl":"https://doi.org/10.14500/aro.11017","url":null,"abstract":"This study gains insight into landfill sites with the observance of all the political, economic and environmental difficulties for the implementing appropriate site measures by adopting a collection of geospatial technique and weighted linear combination (WLC) in TqaTaq sub-district. In the current study, there are several areas determined as appropriate sites for landfill location. In this study, the criteria of distance from the roads, the city center, rivers, surface water, and land use map were used. According to this analysis, only 25.21% of the TaqTaq sub district is suitable for a landfill. Thus, basing on the findings, 20.93% of the concerned sub-district is regarded as least adequate site for this mission, whereas only 3.25% of the area is regarded as moderate suitable. Thus, this study has found out that 1.03% area is the most suitable. The majority of suitable area was located in the North of the Town, where waste production is more than other locations. It should be noted that based on the outcome of this study, the amount of waste produced in the TaqTaq Town for the next 10 years, from 2022 to 2032, is predicted to be about 4080 tons. According to the density calculated for the waste of this area and considering the height of 4 m for the landfill center, in the next 10 years, about 3000 m2 of land is required for the landfill location. Since the suitable area found in this research is about 15 hectares.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":"64 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84830547","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}
M. Abdulrahman, Fattma Z. Mohammed, S. Hamad, H. Hama, A. A. Lema
Coronaviruses are infectious respiratory tract illnesses, but they can also affect the digestive tract and infect both humans and animals. The new coronavirus results in complicated health problems all over the world. The most urgent concern of all researchers around the world has been the treatment of the virus. The following study aimed to use quantitative ethnobotany to help scientist in addressing the deadly virus. Expert sampling method was adopted with the aid of an in-depth interview guide. Thirty-nine respondents were interviewed. Eighty-one medicinal plant species from 35 families were documented. Males 25 (64.1%) constitute the greater percentage of the total respondents. Majority of the respondents had formal education. Eighty-one medicinal plant species from 35 families were documented. Leaves are the most utilized 25.8 followed by seed 17.7 and fruits 12.1%, respectively. Relative frequency of citation ranged from 0.5 to 0.9, whereas the FL value ranged from 0.4 to 0.85, revealing how effective the documented plant species are in the management of COVID-19 in the region. A greater amount of research into documented medicinal plants is warranted because of the high likelihood that they contain many active ingredients.
{"title":"Medicinal Plants Traditionally Used in the Management of COVID-19 in Kurdistan Region of Iraq","authors":"M. Abdulrahman, Fattma Z. Mohammed, S. Hamad, H. Hama, A. A. Lema","doi":"10.14500/aro.11042","DOIUrl":"https://doi.org/10.14500/aro.11042","url":null,"abstract":"Coronaviruses are infectious respiratory tract illnesses, but they can also affect the digestive tract and infect both humans and animals. The new coronavirus results in complicated health problems all over the world. The most urgent concern of all researchers around the world has been the treatment of the virus. The following study aimed to use quantitative ethnobotany to help scientist in addressing the deadly virus. Expert sampling method was adopted with the aid of an in-depth interview guide. Thirty-nine respondents were interviewed. Eighty-one medicinal plant species from 35 families were documented. Males 25 (64.1%) constitute the greater percentage of the total respondents. Majority of the respondents had formal education. Eighty-one medicinal plant species from 35 families were documented. Leaves are the most utilized 25.8 followed by seed 17.7 and fruits 12.1%, respectively. Relative frequency of citation ranged from 0.5 to 0.9, whereas the FL value ranged from 0.4 to 0.85, revealing how effective the documented plant species are in the management of COVID-19 in the region. A greater amount of research into documented medicinal plants is warranted because of the high likelihood that they contain many active ingredients.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":"3 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87438107","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}
Bacterial persistence is recognized as a major cause of antibiotic therapy failure, causing biofilms, and chronic intractable infections. The emergence of persisters in Klebsiella pneumoniae isolates has become a worldwide public health concern. The goal of the present study is to investigate the formation of persister cells beside filaments in Iraqi K. pneumoniae isolates. A total of fifty clinical K. pneumoniae isolates were collected from different clinical specimens and identified using the genotypic identification by using specific primer (rpoB gene) from housekeeping genes. Persister cells investigation is performed by exposure of stationary phase K. pneumoniae isolates to a high concentration of ciprofloxacin (×10 MIC) and counting the number of viable persister cells by CFU counts. Bacterial filament formation is detected and measured by light microscope scanning electron microscope. The results show the bility of these pathogenic bacteria to form persister cells to survive the bactericidal antibiotics and to cause chronic infection.Furthermore, persistent isolates have the ability to change in shape and size extensively, about 4 times increase in cell length than their normal length. These phenomena are possibly the initial stages of bacterial resistance prevalence.
{"title":"Investigation of Bacterial Persistence and Filaments Formation in Clinical Klebsiella pneumoniae","authors":"Sarah N. Aziz, M. A. Al Marjani","doi":"10.14500/aro.10895","DOIUrl":"https://doi.org/10.14500/aro.10895","url":null,"abstract":"Bacterial persistence is recognized as a major cause of antibiotic therapy failure, causing biofilms, and chronic intractable infections. The emergence of persisters in Klebsiella pneumoniae isolates has become a worldwide public health concern. The goal of the present study is to investigate the formation of persister cells beside filaments in Iraqi K. pneumoniae isolates. A total of fifty clinical K. pneumoniae isolates were collected from different clinical specimens and identified using the genotypic identification by using specific primer (rpoB gene) from housekeeping genes. Persister cells investigation is performed by exposure of stationary phase K. pneumoniae isolates to a high concentration of ciprofloxacin (×10 MIC) and counting the number of viable persister cells by CFU counts. Bacterial filament formation is detected and measured by light microscope scanning electron microscope. The results show the bility of these pathogenic bacteria to form persister cells to survive the bactericidal antibiotics and to cause chronic infection.Furthermore, persistent isolates have the ability to change in shape and size extensively, about 4 times increase in cell length than their normal length. These phenomena are possibly the initial stages of bacterial resistance prevalence.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":"34 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85594354","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}
Huda M. Radha, A. A. Abdul Hassan, Ali H. Al-Timemy
Abstract—The upper limb amputation exerts a significant burden on the amputee, limiting their ability to perform everyday activities, and degrading their quality of life. Amputee patients’ quality of life can be improved if they have natural control over their prosthetic hands. Among the biological signals, most commonly used to predict upper limb motor intentions, surface electromyography (sEMG), and axial acceleration sensor signals are essential components of shoulder-level upper limb prosthetic hand control systems. In this work, a pattern recognition system is proposed to create a plan for categorizing high-level upper limb prostheses in seven various types of shoulder girdle motions. Thus, combining seven feature groups, which are root mean square, four-order autoregressive, wavelength, slope sign change, zero crossing (ZC), mean absolute value, and cardinality. In this article, the time-domain features were first extracted from the EMG and acceleration signals. Then, the spectral regression (SR) and principal component analysis dimensionality reduction methods are employed to identify the most salient features, which are then passed to the linear discriminant analysis (LDA) classifier. EMG and axial acceleration signal datasets from six intact-limbed and four amputee participants exhibited an average classification error of 15.68 % based on SR dimensionality reduction using the LDA classifier.
{"title":"Classification of Different Shoulder Girdle Motions for Prosthesis Control Using a Time-Domain Feature Extraction Technique","authors":"Huda M. Radha, A. A. Abdul Hassan, Ali H. Al-Timemy","doi":"10.14500/aro.11064","DOIUrl":"https://doi.org/10.14500/aro.11064","url":null,"abstract":"Abstract—The upper limb amputation exerts a significant burden on the amputee, limiting their ability to perform everyday activities, and degrading their quality of life. Amputee patients’ quality of life can be improved if they have natural control over their prosthetic hands. Among the biological signals, most commonly used to predict upper limb motor intentions, surface electromyography (sEMG), and axial acceleration sensor signals are essential components of shoulder-level upper limb prosthetic hand control systems. In this work, a pattern recognition system is proposed to create a plan for categorizing high-level upper limb prostheses in seven various types of shoulder girdle motions. Thus, combining seven feature groups, which are root mean square, four-order autoregressive, wavelength, slope sign change, zero crossing (ZC), mean absolute value, and cardinality. In this article, the time-domain features were first extracted from the EMG and acceleration signals. Then, the spectral regression (SR) and principal component analysis dimensionality reduction methods are employed to identify the most salient features, which are then passed to the linear discriminant analysis (LDA) classifier. EMG and axial acceleration signal datasets from six intact-limbed and four amputee participants exhibited an average classification error of 15.68 % based on SR dimensionality reduction using the LDA classifier.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":"44 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89240686","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}
Dedawan S. Saleh, Saddon T. Ahmad, Sarmad R. Kareem
In this study, the activity concentration of 40K and its’ concentrations in 24 different types of fruits were determined using high purity germanium (HPGe) and sodium iodide scintillation (NaI) detectors. The results of the two measurements are consistent. The Maximum and minimum activities of 40K in dry samples were 750.61 ± 11.88 and 15.64 ± 0.86 Bq kg−1 in apricot and olive, respectively, while in fresh samples they were 152.27 ± 2.12 and 1.99 ± 0.11 Bq kg−1 in dates and olive, respectively. The highest and lowest potassium contents were 489.81 and 6.42 mg/100gm in fresh dates and olives, respectively. Drupe and Tropical fruits, as a fruit family, typically had the highest level of 40K activity and potassium concentration, whereas pome fruits showed the lowest levels. Many of these commonly consumed fresh fruits with rich potassium and water contents are lowering hypertension and improving the hydration status (HS) in people's nutrition. The rate of potassium-40 and total potassium concentration intake for a single unit or portion of the fruits was calculated.
本研究采用高纯锗(HPGe)和碘化钠闪烁(NaI)检测器测定了24种不同类型水果中40K的活性浓度及其浓度。两次测量的结果是一致的。干燥样品中40K的最大和最小活性在杏和橄榄中分别为750.61±11.88和15.64±0.86 Bq kg - 1，新鲜样品中枣和橄榄的40K活性分别为152.27±2.12和1.99±0.11 Bq kg - 1。鲜枣和橄榄的钾含量最高和最低分别为489.81和6.42 mg/100gm。核果和热带水果的40K活性和钾浓度最高，而梨果的40K活性和钾浓度最低。这些常被食用的富含钾和水的新鲜水果在人们的营养中具有降低高血压和改善水合状态(HS)的作用。计算了单单位或部分水果的钾-40率和总钾浓度摄入量。
{"title":"Determination of the Potassium Content in Fruit Samples by Gamma Spectrometry to Emphasize its Health Implications","authors":"Dedawan S. Saleh, Saddon T. Ahmad, Sarmad R. Kareem","doi":"10.14500/aro.11053","DOIUrl":"https://doi.org/10.14500/aro.11053","url":null,"abstract":"In this study, the activity concentration of 40K and its’ concentrations in 24 different types of fruits were determined using high purity germanium (HPGe) and sodium iodide scintillation (NaI) detectors. The results of the two measurements are consistent. The Maximum and minimum activities of 40K in dry samples were 750.61 ± 11.88 and 15.64 ± 0.86 Bq kg−1 in apricot and olive, respectively, while in fresh samples they were 152.27 ± 2.12 and 1.99 ± 0.11 Bq kg−1 in dates and olive, respectively. The highest and lowest potassium contents were 489.81 and 6.42 mg/100gm in fresh dates and olives, respectively. Drupe and Tropical fruits, as a fruit family, typically had the highest level of 40K activity and potassium concentration, whereas pome fruits showed the lowest levels. Many of these commonly consumed fresh fruits with rich potassium and water contents are lowering hypertension and improving the hydration status (HS) in people's nutrition. The rate of potassium-40 and total potassium concentration intake for a single unit or portion of the fruits was calculated. ","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":"182 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76083555","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}
Bashdar I. Meena, Tara F. Tahir, Shalaw Z. Sdeeq, Khalid N. Sediq
Cosmetic marketing is one of the most profitable and fast increasing markets in Kurdistan Region of Iraq. In recent years, the use of cosmetics has witnessed a rapid increase, especially with the emergence of social media and its impact on this trade. The market is full of different cosmetic brands and nail products. Moderate and low-quality brands of cosmetic samples that available in the local markets were selected to investigate their heavy metals and chemical composition. Samples from face foundation, eye shadow, and nail polish products were taken and examined to evaluate the concentration of metals, that is, Hg, Pb, Cd, As, Mn, Cr, Ni, Co, Fe, Zn, Cu, and Al ions, using X-ray diffraction and X-ray fluorescence techniques. The examination results show high concentrations of Fe and Al metals in the lipstick samples whereas the Hg, Cd, Cr, and Ni were out of detection limit. Moreover, the results show contamination of Hg heavy metal in one of the examined nail polishes brands, whereas the rest of foundation and eye shadow samples show a higher concentration of Al and Fe. Curcumin, as a natural bio-friendly chelate, has been used to deplete metal ions using ultraviolet-visible Spectrophotometer.
{"title":"Toxic Metals in Some Decorative Cosmetics and Nail Products","authors":"Bashdar I. Meena, Tara F. Tahir, Shalaw Z. Sdeeq, Khalid N. Sediq","doi":"10.14500/aro.11067","DOIUrl":"https://doi.org/10.14500/aro.11067","url":null,"abstract":"Cosmetic marketing is one of the most profitable and fast increasing markets in Kurdistan Region of Iraq. In recent years, the use of cosmetics has witnessed a rapid increase, especially with the emergence of social media and its impact on this trade. The market is full of different cosmetic brands and nail products. Moderate and low-quality brands of cosmetic samples that available in the local markets were selected to investigate their heavy metals and chemical composition. Samples from face foundation, eye shadow, and nail polish products were taken and examined to evaluate the concentration of metals, that is, Hg, Pb, Cd, As, Mn, Cr, Ni, Co, Fe, Zn, Cu, and Al ions, using X-ray diffraction and X-ray fluorescence techniques. The examination results show high concentrations of Fe and Al metals in the lipstick samples whereas the Hg, Cd, Cr, and Ni were out of detection limit. Moreover, the results show contamination of Hg heavy metal in one of the examined nail polishes brands, whereas the rest of foundation and eye shadow samples show a higher concentration of Al and Fe. Curcumin, as a natural bio-friendly chelate, has been used to deplete metal ions using ultraviolet-visible Spectrophotometer.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":"11 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88604226","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}
Big data of different types, such as texts and images, are rapidly generated from the internet and other applications. Dealing with this data using traditional methods is not practical since it is available in various sizes, types, and processing speed requirements. Therefore, data analytics has become an important tool because only meaningful information is analyzed and extracted, which makes it essential for big data applications to analyze and extract useful information. This paper presents several innovative methods that use data analytics techniques to improve the analysis process and data management. Furthermore, this paper discusses how the revolution of data analytics based on artificial intelligence algorithms might provide improvements for many applications. In addition, critical challenges and research issues were provided based on published paper limitations to help researchers distinguish between various analytics techniques to develop highly consistent, logical, and information-rich analyses based on valuable features. Furthermore, the findings of this paper may be used to identify the best methods in each sector used in these publications, assist future researchers in their studies for more systematic and comprehensive analysis and identify areas for developing a unique or hybrid technique for data analysis.
{"title":"Data Analytics and Techniques","authors":"Safa S. Abdul-Jabbar, Alaa K. Farhan","doi":"10.14500/aro.10975","DOIUrl":"https://doi.org/10.14500/aro.10975","url":null,"abstract":"Big data of different types, such as texts and images, are rapidly generated from the internet and other applications. Dealing with this data using traditional methods is not practical since it is available in various sizes, types, and processing speed requirements. Therefore, data analytics has become an important tool because only meaningful information is analyzed and extracted, which makes it essential for big data applications to analyze and extract useful information. This paper presents several innovative methods that use data analytics techniques to improve the analysis process and data management. Furthermore, this paper discusses how the revolution of data analytics based on artificial intelligence algorithms might provide improvements for many applications. In addition, critical challenges and research issues were provided based on published paper limitations to help researchers distinguish between various analytics techniques to develop highly consistent, logical, and information-rich analyses based on valuable features. Furthermore, the findings of this paper may be used to identify the best methods in each sector used in these publications, assist future researchers in their studies for more systematic and comprehensive analysis and identify areas for developing a unique or hybrid technique for data analysis.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":"12 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76668454","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}
Describing heat transfer in biological organs is absolutely challenging because it is involved with many complex phenomena. Therefore, understanding the optical and thermal properties of living system during external irradiation sources such as laser interstitial thermal therapy (LITT) are too important for therapeutic purposes, especially for hyperthermia treatments. The purpose of this study was to determine a proper laser power and irradiation time for LITT applicator to irradiate liver tissue during hyperthermia treatment. For this aim, bioheat equation in one-dimensional spherical coordinate is solved by Green function method to simulate temperature distribution and rate of damage around irradiated target and how thermal and optical properties such as laser power, laser exposure time, and blood perfusion rate affect the rate of temperature distribution. Guiding equations according to the suggested boundary conditions are written and solved by MATLAB software. The outcomes show that increasing laser exposure time and power increase the temperature, especially at the nearest distance from the center of diffusion. Accordingly, a decrease in blood perfusion rate leads to decrease temperature distribution. The findings show that the model is useful to help the physicians to monitor the amount of heat diffusion by laser power during the treatment to protect healthy cells.
{"title":"A Computational Model for Temperature Monitoring During Human Liver Treatment by Nd:YaG Laser Interstitial Thermal Therapy (LITT)","authors":"Bazhdar N. Mohammed, Dilshad S. Ismael","doi":"10.14500/aro.10949","DOIUrl":"https://doi.org/10.14500/aro.10949","url":null,"abstract":"Describing heat transfer in biological organs is absolutely challenging because it is involved with many complex phenomena. Therefore, understanding the optical and thermal properties of living system during external irradiation sources such as laser interstitial thermal therapy (LITT) are too important for therapeutic purposes, especially for hyperthermia treatments. The purpose of this study was to determine a proper laser power and irradiation time for LITT applicator to irradiate liver tissue during hyperthermia treatment. For this aim, bioheat equation in one-dimensional spherical coordinate is solved by Green function method to simulate temperature distribution and rate of damage around irradiated target and how thermal and optical properties such as laser power, laser exposure time, and blood perfusion rate affect the rate of temperature distribution. Guiding equations according to the suggested boundary conditions are written and solved by MATLAB software. The outcomes show that increasing laser exposure time and power increase the temperature, especially at the nearest distance from the center of diffusion. Accordingly, a decrease in blood perfusion rate leads to decrease temperature distribution. The findings show that the model is useful to help the physicians to monitor the amount of heat diffusion by laser power during the treatment to protect healthy cells.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":"59 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74347010","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}
Haval A. Ahmed, Peshawa J. Muhammad Ali, Abdulbasit K. Faeq, Saman M. Abdullah
Data normalization can be useful in eliminating the effect of inconsistent ranges in some machine learning (ML) techniques and in speeding up the optimization process in others. Many studies apply different methods of data normalization with an aim to reduce or eliminate the impact of data variance on the accuracy rate of ML-based models. However, the significance of this impact aligning with the mathematical concept of the ML algorithms still needs more investigation and tests. To identify that, this work proposes an investigation methodology involving three different ML algorithms, which are support vector machine (SVM), artificial neural network (ANN), and Euclidean-based K-nearest neighbor (E-KNN). Throughout this work, five different datasets have been utilized, and each has been taken from different application fields with different statistical properties. Although there are many data normalization methods available, this work focuses on the min-max method, because it actively eliminates the effect of inconsistent ranges of the datasets. Moreover, other factors that are challenging the process of min-max normalization, such as including or excluding outliers or the least significant feature, have also been considered in this work. The finding of this work shows that each ML technique responds differently to the min-max normalization. The performance of SVM models has been improved, while no significant improvement happened to the performance of ANN models. It is been concluded that the performance of E-KNN models may improve or degrade with the min-max normalization, and it depends on the statistical properties of the dataset.
{"title":"An Investigation on Disparity Responds of Machine Learning Algorithms to Data Normalization Method","authors":"Haval A. Ahmed, Peshawa J. Muhammad Ali, Abdulbasit K. Faeq, Saman M. Abdullah","doi":"10.14500/aro.10970","DOIUrl":"https://doi.org/10.14500/aro.10970","url":null,"abstract":"Data normalization can be useful in eliminating the effect of inconsistent ranges in some machine learning (ML) techniques and in speeding up the optimization process in others. Many studies apply different methods of data normalization with an aim to reduce or eliminate the impact of data variance on the accuracy rate of ML-based models. However, the significance of this impact aligning with the mathematical concept of the ML algorithms still needs more investigation and tests. To identify that, this work proposes an investigation methodology involving three different ML algorithms, which are support vector machine (SVM), artificial neural network (ANN), and Euclidean-based K-nearest neighbor (E-KNN). Throughout this work, five different datasets have been utilized, and each has been taken from different application fields with different statistical properties. Although there are many data normalization methods available, this work focuses on the min-max method, because it actively eliminates the effect of inconsistent ranges of the datasets. Moreover, other factors that are challenging the process of min-max normalization, such as including or excluding outliers or the least significant feature, have also been considered in this work. The finding of this work shows that each ML technique responds differently to the min-max normalization. The performance of SVM models has been improved, while no significant improvement happened to the performance of ANN models. It is been concluded that the performance of E-KNN models may improve or degrade with the min-max normalization, and it depends on the statistical properties of the dataset.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":"46 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74052906","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}
Social media is internet-based technology and an electronic form of communication that facilitates sharing of ideas, documents, and personal information. Twitter is a microblogging platform and is the most effective social service for posting microblogs and likings, commenting, sharing, and communicating with others. The problem we are shedding light on in this paper is the misuse of bots on Twitter. The purpose of bots is to automate specific repetitive tasks instead of human interaction. However, bots are misused to influence people’s minds by spreading rumors and conspiracy related to controversial topics. In this paper, we initiate a new benchmark created on a 1.5M Twitter profile. We train different supervised machine learning on our benchmark to detect bots on Twitter. In addition to increasing benchmark scalability, various autofeature selections are utilized to identify the most influential features and remove the less influential ones. Furthermore, over-under-sampling is applied to reduce the imbalance effect on the benchmark. Finally, our benchmark compared with other stateof-the-art benchmarks and achieved a 6% higher area under the curve than other datasets in the case of generalization, improving the model performance by at least 2% by applying over-/undersampling.
{"title":"Machine Learning Algorithms for Detecting and Analyzing Social Bots Using a Novel Dataset","authors":"Niyaz Jalal, K. Ghafoor","doi":"10.14500/aro.101032","DOIUrl":"https://doi.org/10.14500/aro.101032","url":null,"abstract":"Social media is internet-based technology and an electronic form of communication that facilitates sharing of ideas, documents, and personal information. Twitter is a microblogging platform and is the most effective social service for posting microblogs and likings, commenting, sharing, and communicating with others. The problem we are shedding light on in this paper is the misuse of bots on Twitter. The purpose of bots is to automate specific repetitive tasks instead of human interaction. However, bots are misused to influence people’s minds by spreading rumors and conspiracy related to controversial topics. In this paper, we initiate a new benchmark created on a 1.5M Twitter profile. We train different supervised machine learning on our benchmark to detect bots on Twitter. In addition to increasing benchmark scalability, various autofeature selections are utilized to identify the most influential features and remove the less influential ones. Furthermore, over-under-sampling is applied to reduce the imbalance effect on the benchmark. Finally, our benchmark compared with other stateof-the-art benchmarks and achieved a 6% higher area under the curve than other datasets in the case of generalization, improving the model performance by at least 2% by applying over-/undersampling.","PeriodicalId":8398,"journal":{"name":"ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY","volume":"164 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84641632","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：