This paper offers an interpretation and discussion of the later Foucault’s multifaceted concept of ‘critique’. It argues that critique for Foucault is composed of three main elements: the ‘spirit’ (though not all of the substance) of Kant’s understanding of the Enlightenment; the practice of parrhesia that emerged in Ancient Greece and became central to Christian subjectivity; and the transfigurative aesthetic experience of modernity that was most richly depicted by Baudelaire. In the second section, there is a discussion of Foucault’s view (...) of an event that continues to perplex Western observers, the 1979 Iranian Revolution, juxtaposed with a Marxist understanding of the upheaval. Rejecting both historical materialist and liberal historiography, Foucault offers a unique perspective on the Iranian Revolution, deeming it to be a practical manifestation of critique in an ‘irreducibly’ religious context and based on a reformation of the self situated within a wider ‘political spirituality’. However, the trajectory of politics in Iran since 1979 bears resemblance to those of other, resolutely secular post-revolutionary societies, and thus raises the questions of whether Foucault ignores the universal in privileging the particular and in refusing synthesis between the West and the Orient, adopts an Orientalist epistemology. (shrink)

Los más diversos teóricos de la modernidad suelen presentar este período como un proceso continuo de desarrollo tecnológico y económico, progreso en las artes y las ciencias, expansión de la democracia y racionalización de las instituciones y las costumbres. Sin embargo, persisten las contradicciones del capitalismo, el colapso de la civilización es inminente y el Pueblo no vive ni ha vivido en la modernidad tal y como sus teóricos la describen. Esto es tanto más cierto en nuestro país, habitado por (...) una suerte de magdaleniense o pre-Tartessos lleno de cacharritos con iconos, llamadas, avisos, avatares, tamagotchies, móviles y juegos online integrados sin la menor discrepancia sobre una capa de rituales del año de la polka, fetiches, incienso, lágrimas, sangre, joyas, ofrendas, sacrificios, reliquias de miembros de santos y matriarcas fenicio-neolíticas devenidas madres de Cristo al tiempo que supporters del equipo de fútbol local. El ultrarracionalismo se presenta como un nuevo método y movimiento filosófico cuya voluntad es afrontar los desafíos del futuro sin dejar de hacer justicia a esta auténtica marejada de lo grotesco. (shrink)

Throughout this research, imposing the training of an Artificial Neural Network (ANN) to play tic-tac-toe bored game, by training the ANN to play the tic-tac-toe logic using the set of mathematical combination of the sequences that could be played by the system and using both the Gradient Descent Algorithm explicitly and the Elimination theory rules implicitly. And so on the system should be able to produce imunate amalgamations to solve every state within the game course to make better of results (...) of winnings or getting draw. (shrink)

Socio-Economic Impacts of Co-operative Societies: An Empirical Study -/- Author / Authors :Md. Ruhul Amin and Mohammed Mahin Uddin Page no.179-193 Discipline : Applied Economics/ Management/ Commerce Script/language : English/Roman Category : Research paper Keywords: Co-operative, Development, Society, Constrains, Constitution, Comilla.

In this paper an Artificial Neural Network (ANN) model, for predicting the category of a tumor was developed and tested. Taking patients’ tests, a number of information gained that influence the classification of the tumor. Such information as age, sex, histologic-type, degree-of-diffe, status of bone, bone-marrow, lung, pleura, peritoneum, liver, brain, skin, neck, supraclavicular, axillar, mediastinum, and abdominal. They were used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was established and trained using (...) data set which its title is “primary tumor” and was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia Test data evaluation shows that the ANN model is able to correctly predict the tumor category with 76.67 % accuracy. (shrink)

Abstract: This research employs a Random Forest classification model to predict and assess obesity and cardiovascular disease (CVD) risk based on a comprehensive dataset collected from individuals in Mexico, Peru, and Colombia. The dataset comprises 17 attributes, including information on eating habits, physical condition, gender, age, height, and weight. The study focuses on classifying individuals into different health risk categories using machine learning algorithms. Our Random Forest model achieved remarkable performance with an accuracy, F1-score, recall, and precision all reaching 97.23%. (...) The model's success is attributed to its ability to effectively leverage attributes related to eating habits, physical condition, and demographic factors. Feature importance analysis reveals key factors contributing to accurate predictions. This paper contributes to the field of health analytics by demonstrating the effectiveness of Random Forest in predicting and categorizing health risks. The insights gained from this research have the potential to inform personalized health interventions and contribute to the advancement of precision health strategies. Furthermore, the study highlights the importance of leveraging machine learning techniques for health risk assessment in diverse populations. (shrink)

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a (...) dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%. (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

Abstract Heart Attack is the Cardiovascular Disease (CVD) which causes the most deaths among CVDs. We collected a dataset from Kaggle website. In this paper, we propose an ANN model for the predicting whether a patient has a heart attack or not that. The dataset set consists of 9 features with 1000 samples. We split the dataset into training, validation, and testing. After training and validating the proposed model, we tested it with testing dataset. The proposed model reached an accuracy (...) of 98.01% on the Heart Disease Dataset. (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

Abstract: As the integration of Artificial Intelligence (AI) continues to permeate various sectors, the legal domain stands on the cusp of a transformative era. This research paper delves into the multifaceted relationship between AI and the law, scrutinizing the profound implications and innovative applications that emerge at the intersection of these two realms. The study commences with an examination of the current landscape, assessing the challenges and opportunities that AI presents within legal frameworks. With an emphasis on efficiency, accuracy, and (...) accessibility, AI technologies are reshaping traditional legal processes, ranging from document analysis and contract review to predictive legal analytics. Furthermore, the paper scrutinizes the ethical considerations and potential biases inherent in AI algorithms, exploring the delicate balance between technological advancements and the preservation of legal principles such as fairness, accountability, and transparency. The research also delves into the evolving role of legal professionals in navigating and overseeing AI applications, emphasizing the importance of responsible AI deployment. Drawing on case studies and real-world examples, this paper showcases instances where AI has already demonstrated its efficacy in legal contexts, highlighting successful implementations and identifying areas for improvement. The discussion extends to the evolving regulatory landscape, as legal systems grapple with the need to adapt and establish frameworks that ensure the responsible and ethical use of AI technologies. In conclusion, this research contributes to the growing discourse on the dynamic interplay between AI and the legal domain. By illuminating the potential benefits, ethical considerations, and regulatory challenges, it provides a comprehensive overview for legal practitioners, policymakers, and technologists alike, fostering a nuanced understanding of the evolving landscape where artificial intelligence intersects with the law. (shrink)

In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is “survey lung (...) cancer”. Model evaluation showed that the ANN model is able to detect the absence or presence of lung cancer with 96.67 % accuracy. (shrink)

Abstract: The emergence of the novel coronavirus (COVID-19) in 2019 has presented the world with an unprecedented global health crisis. The rapid and widespread transmission of the virus has strained healthcare systems, disrupted economies, and challenged societies. In response to this monumental challenge, the intersection of technology and healthcare has become a focal point for innovation. This research endeavors to leverage the capabilities of Artificial Neural Networks (ANNs) to develop an advanced predictive model for forecasting the spread of COVID-19. It (...) involves the collection, analysis, and integration of diverse datasets encompassing epidemiological, clinical, and social factors that influence the virus's dissemination. (shrink)

Abstract: This research explores the application of artificial neural networks (ANNs) in predicting cancer using a synthetically generated dataset designed for research purposes. The dataset comprises 10,000 pseudo-patient records, each characterized by gender, age, smoking history, fatigue, and allergy status, along with a binary indicator for the presence or absence of cancer. The 'Gender,' 'Smoking,' 'Fatigue,' and 'Allergy' attributes are binary, while 'Age' spans a range from 18 to 100 years. The study employs a three-layer ANN architecture to develop a (...) predictive model. The achieved accuracy of 97.24% and a low loss value of 0.02 indicate promising performance. Further analysis involves a comprehensive examination of the confusion matrix, precision, recall, and F1 score metrics, along with the ROC curve and AUC score, to provide a detailed understanding of the model's strengths and weaknesses. Additionally, considerations for cross-validation, hyperparameter tuning, and interpretability are discussed. It is emphasized that while the synthetic nature of the dataset allows for controlled experimentation, results should be cautiously interpreted and validated on authentic clinical data before extrapolation to real-world medical scenarios. This research serves as a valuable contribution to the exploration of predictive models for cancer detection, encouraging continued investigation and development in the field. (shrink)

Abstract: Online book review platforms generate vast user data, making accurate rating prediction crucial for personalized recommendations. This research explores neural networks as simple models for predicting book ratings without complex algorithms. Our novel approach uses neural networks to predict ratings solely from user-book interactions, eliminating manual feature engineering. The model processes data, learns patterns, and predicts ratings. We discuss data preprocessing, neural network design, and training techniques. Real-world data experiments show the model's effectiveness, surpassing traditional methods. This research can (...) enhance user experience, book catalog organization, and aid publishers, simplifying recommendation processes and providing tailored suggestions based on user preferences. (shrink)

A longstanding orthodoxy holds that the Mohists regard the promotion of li (benefit, 利) as their ultimate normative criterion, meaning that they measure what is yi (just, 義) or buyi (unjust, 不義) depending on whether it maximizes li or not. This orthodoxy dates back at least to Joseph Edkins (1859), who saw Mozi as a utilitarian and an ally of Bentham. In this paper, we will argue that this orthodoxy should be reconsidered because it does not square with several passages (...) from the Mozi. That the Mohists place a strong weight on the promotion of ‘li for the whole world (tianxia zhi li, 天下之利)’ is uncontroversial. We argue, however, that in certain cases the Mohist moral calculus diverges in its rationale or outcome from li-promotionalism. This position rejects the orthodoxy by showing that Mohism and li-promotionalism are not entirely coterminous. (shrink)

Cardiotocography data uncertainty is a critical task for the classification in biomedical field. Constructing good and efficient classifier via machine learning algorithms is necessary to help doctors in diagnosing the state of fetus heart rate. *e proposed neutrosophic diagnostic system is an Interval Neutrosophic Rough Neural Network framework based on the backpropagation algorithm. It benefits from the advantages of neutrosophic set theory not only to improve the performance of rough neural networks but also to achieve a better performance than the (...) other algorithms. *e experimental results visualize the data using the boxplot for better understanding of attribute distribution. *e performance measurement of the confusion matrix for the proposed framework is 95.1, 94.95, 95.2, and 95.1 concerning accuracy rate, precision, recall, and F1-score, respectively. WEKA application is used to analyse cardiotocography data performance measurement of different algorithms, e.g., neural network, decision table, the nearest neighbor, and rough neural network. *e comparison with other algorithms shows that the proposed framework is both feasible and efficient classifier. Additionally, the receiver operation characteristic curve displays the proposed framework classifications of the pathologic, normal, and suspicious states by 0.93, 0.90, and 0.85 areas that are considered high and acceptable under the curve, respectively. Improving the performance measurements of the proposed framework by removing ineffective attributes via feature selection would be suitable advancement in the future. Moreover, the proposed framework can also be used in various real-life problems such as classification of coronavirus, social media, and satellite image. (shrink)

It is crucial to actively detect the risks of transactions in a financial company to improve customer experience and minimize financial loss. In this study, we compare different machine learning algorithms to effectively and efficiently predict the legitimacy of financial transactions. The algorithms used in this study were: MLP Repressor, Random Forest Classifier, Complement NB, MLP Classifier, Gaussian NB, Bernoulli NB, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Bagging Classifier, Decision Tree Classifier and Deep Learning. The dataset (...) was collected from Kaggle depository. It consists of 6362620 rows and 10 columns. The best classifier with unbalanced dataset was the Random Forest Classifier. The Accuracy 99.97%, precession 99.96%, Recall 99.97% and the F1-score 99.96%. However, the best classifier with balanced dataset was the Bagging Classifier. The Accuracy 99.96%, precession 99.95%, Recall 99.98% and the F1-score 99.96%. (shrink)

Code-switching is explained as switching between two languages at the same time while conversing in the same discourse. The aim of this study is to explore the perceptions of Afghan EFL lecturers toward code- switching. This study used a qualitative research approach in which the data was collected via semi-structured interviews with five EFL lecturers. The lecturers were interviewed to explore their perceptions and reasons for code-switching, and the extent to which they practice code-switching in their classrooms. The results revealed (...) that the lecturers in the study had positive perceptions of code-switching and believed it as a beneficial strategy in facilitating the learning process. However, they also conveyed that an excessive practice of code-switching should be avoided. The study results also disclosed that the lecturers practiced code-switching for various functions such as explaining difficult concepts (translation), explaining complex grammatical points, clarifications, and giving instructions. Finally, the paper provides recommendation for further research. (shrink)

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the proposed model, (...) it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers. (shrink)

The computer science (CS) graduated students suffered from documenting their projects and specially from coordinating it. In addition, students’ supervisors faced difficulties with guiding their students to an efficient process of documenting. In this paper, we will offer a suggestion as a solution to the mentioned problems; that is an application to make the process of documenting computer science (CS) student graduation project easy and time-cost efficient. This solution will decrease the possibility of human mistakes and reduce the effort of (...) documenting process. (shrink)

Despite the growing support for the importance of volunteer leadership development activities across nonprofit organizations, little is known about volunteer leadership development in the field of human resource development. This narrative literature review highlighted the main challenges in running a nonprofit organization, emphasized the value of volunteer leadership development, and stressed the important role of HRD to develop more capable and trained volunteers. Due to the shortage of skilled volunteers, special efforts are needed to concentrate on volunteers' development; proper guidance (...) and assistance are required to do this effectively and efficiently. This study opens discussion on this topic and offers overall implications for volunteer leadership development. Our findings can help professionals design thoughtful leadership development programs for volunteers and may provide a foundation for future research in the field of HRD. (shrink)

As a type of evidence glass can be very useful contact trace material in a wide range of offences including burglaries and robberies, hit-and-run accidents, murders, assaults, ram-raids, criminal damage and thefts of and from motor vehicles. All of that offer the potential for glass fragments to be transferred from anything made of glass which breaks, to whoever or whatever was responsible. Variation in manufacture of glass allows considerable discrimination even with tiny fragments. In this study, we worked glass classification (...) and testing of artificial neural network model created by the JustNN. The aim of the study is help investigator in identifying the type of glass found in arena of the crime. The Neural Network model was trained and validated using the type of glass dataset. The accuracy of model in predicting the type of glass reached 96.7%. Thus neural network is suitable for predicating type of glasses. (shrink)

In this paper, we investigated multiple machine learning classifiers which are, Multinomial Naive Bayes, Support Vector Machine, Decision Tree, K Nearest Neighbors, and Random Forest in a text classification problem. The data we used contains real and fake job posts. We cleaned and pre-processed our data, then we applied TF-IDF for feature extraction. After we implemented the classifiers, we trained and evaluated them. Evaluation metrics used are precision, recall, f-measure, and accuracy. For each classifier, results were summarized and compared with (...) others. (shrink)

In this paper, an artificial neural network (ANN) model was developed and validated to predict whether a couple is going to get divorced or not. Prediction is done based on some questions that the couple answered, answers of those questions were used as the input to the ANN. The model went through multiple learning-validation cycles until it got 100% accuracy.

The scholarly search engine Google Scholar (G.S.) has problems that make it not a 100% trusted search engine. In this research, we discussed a few drawbacks that we noticed in Google Scholar, one of them is related to how does it perform (add articles) option for adding new articles that are related to the registered researchers. Our suggestion is an attempt for making G.S. more efficient by improving the searching method that it uses and finally having trusted statistical results.

In this paper, a predictive artificial neural network (ANN) model was developed and validated for the purpose of prediction whether a watermelon is good or bad, the model was developed using JUSTNN software environment. Prediction is done based on some watermelon attributes that are chosen to be input data to the ANN. Attributes like color, density, sugar rate, and some others. The model went through multiple learning-validation cycles until the error is zero, so the model is 100% percent accurate for (...) the purpose of prediction whether good or bad the watermelon is. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Abstract: This research paper presents the development and evaluation of a neural network-based model for predicting fire alarms in smoke detection systems. Using a dataset from Kaggle containing 15 features and 3487 samples, we trained and validated a neural network with a three-layer architecture. The model achieved an accuracy of 100% and an average error of 0.0000003. Additionally, we identified the most influential features in predicting fire alarms.

European colonialism is an extremely controversial affair in world history that is also discussed today. This paper explores the influence of these happenings as the world incorporates the culture of European in every step of life. Europe dominated almost the entire world and its people were leaders in science and technology. European languages, literature, and culture spread all over the globe. Decisions in Europe largely determined global events for centuries. The other continents did not approach European power until after WW1 (...) or WW2. Europe consisted of constantly competing nations, and this competition spurred development. Europe achieved world hegemony in the years after 1500 A.D., primarily due to technological advancements, scientific research, political development of nations with stable succession and continuity, and a culture dominated by Christianity. These advantages were translated into military power, projected all over the world. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method is (...) suitable for the evaluation of car acceptability forecasting, can also be extended to all other areas. (shrink)

The study has been presented the role of home environment to learn English language particularly in Bangladesh. Parental as well as siblings role is very essential to grow the sense of learning and the learners can grow a great influence by it. Parents’ as well as siblings’ positive attitude, education and awareness according to individual requirements and needs are provided constant encouragement and support for the learners. Parents make the greatest difference to achievement through supporting their learning at home rather (...) than supporting activities in school. A learner, whose family members are habituated to use English at home, feels encouraged to learn English which facilitates learning process. My findings, on the other hand, show that children of those parents who are ignorant of providing sound family atmosphere, lack confidence as well as self-esteem. This study also inspects the impact of home environment on children’s achievement in English language. The findings reveal that there is a constant relationship between the role of family and students’ academic achievement. (shrink)

Parkinson's Disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Doctors do not know what causes it and finds difficulty in early diagnosing the presence of Parkinson’s disease. An artificial neural network system with back propagation algorithm is presented in this paper for helping doctors in identifying (...) PD. Previous research with regards to predict the presence of the PD has shown accuracy rates up to 93% [1]; however, accuracy of prediction for small classes is reduced. The proposed design of the neural network system causes a significant increase of robustness. It is also has shown that networks recognition rates reached 100%. (shrink)

The use of technology has become an important part of the learning process in and out of the class. Technology continues to grow in importance as a tool to help teachers facilitate language learning for their learners. This study focuses on the role of using new technologies in learning English as a second/foreign language. It discussed different attitudes which support English language learners to increase their learning skills through using technologies. In this paper, the researcher defined the term technology and (...) technology integration, explained the use of technology in language classroom, reviewed previous studies on using technologies in improving language learning skills, and stated certain recommendations for the better use of these technologies, which assist learners in improving their learning skills. The literature review indicated that the effective use of new technologies improves learners’ language learning skills. (shrink)

The COVID -19 is one of the most contagious and fatal diseases known today, which had spread in communities very fast. This deadly virus affecting the lives of many people and causing fatalities. Besides the strong practice of praying and putting trust in Allah for the cure of disease, Islam also recommends taking medications and adopt sufficient precautions for the safety and well-being of the community. Before 1400 years ago, the Prophet Muhammad (peace be upon Him) introduced the quarantining and (...) hygiene practice, at a time there was very little known to combat the contagious diseases. Currently, modern societies are emphasizing on hand hygiene, social distancing, quarantining, and social isolation to combat COVOD-19. Islam has superseded modern societies by establishing the idea of quarantines and hygienic practice for contagious and fatal diseases. The commandments to prevent the spread of COVID-19 by worldwide public health organizations are almost in line with the teachings of Islam. (shrink)

We investigate the association between controlling shareholders' ownership (CS_Own) and firms' leverage decisions in the Singaporean context. We examine whether the impact of ownership concentration on leverage differs across excess and lower control. We report that shareholders with excess control prefer leverage financing for an optimal capital structure and focus on value maximisation rather using leverage as a tool of minority shareholders' expropriation. Our analysis shows that firms capital structure significantly influences by the coalition of shareholders particularly decisions about leverage (...) financing in addition to the firms' specific characteristics and institutional arrangements. Our empirical evidence shows that controlling shareholders with a lower fraction of equity are more concerned about limited holding thus prefer leverage over equity financing to inflate their equity stake to protect them from the potential takeovers and mergers. We report that capital structure decisions in Singapore are linked with the trade-off between the controlling shareholders' target of mitigating firm risk and their non-dilution entrenchment needs. Further, we found an inverted U-shaped association between control ownership and leverage financing. In terms of moderating effect of family-controlled ownership, our findings exhibit that leverage financing is less pronounced for family firms in Singapore due to the under-diversified investment portfolio. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City with 97.50 (...) % accuracy. The factor of DriveTrain has the most influence on MPG_City evaluation. Similar studies can be carried out for the evaluation of other characteristics of cars. (shrink)

The motto of this research article is to present how Bangladeshi University students use the mixture of both English and Bengali language as their spoken language. It can be said undoubtedly that English is the vast spoken language in the whole world as well as spreading to a large extent. Bangladesh is not different from other countries as well as English has been taught every level an every medium of education here and in recent times it is seen that English (...) is used by the university students when they talk with each other I mean we can see a mixture of two languages. Each language has a social function which no other language could possibly fulfill (Hudson, 1996). Perhaps that is why code switching has become a common phenomenon in our everyday conversations in the recent years in Bangladesh. Young people randomly style switch in their daily interactions. This research paper also explores the possible propelling factors as well as stereotypical patterns behind this scenario. The data collected for the study reveals different factors as the causes behind this practice of code-switching and its impact on language behavior. (shrink)

We investigate the firms’ specific attributes that determine the difference in speed of adjustment (SOA) towards the cash holdings target in the Scandinavian countries: Denmark, Norway and Sweden. We examine whether Scandinavian firms maintain an optimal level of cash holdings and determine if the active cash holdings management is associated with the firms’ higher SOA and lower adjustment costs. Our findings substantiate that a higher level of off-target cost induces professional managers to rebalance their cash level towards the optimal balance (...) of cash holdings. Our results reveal that Scandinavian firms accelerate SOA towards cash targets primarily for the precautionary motive. Moreover, our results show that SOA is heterogeneous across Scandinavian firms based on adjustment cost and deviate cash holdings towards the target mainly with the support of internal financing. Furthermore, our empirical findings show that the SOA of Norwegian firms is significantly higher than the Danish and Swedish firms. (shrink)

It has a long history of teaching English literature and language in this sub-continent. The European colonial rulers introduced this teaching with the motive of serving their own purpose, but paradoxically this teaching has benefited the people of this soil as well in many ways. From the very beginning, the English department of any college or university has been treated as one of the most prestigious departments that have produced the best citizens of this soil. But recently the teaching scenario (...) of these departments in Bangladesh is being severely affected owing to different kinds of changes that have occurred in the social and academic arenas of the country. It is now the demand of the present time that the English departments of the universities of our country should take proper steps to address those problems properly and bring back its reputation. The present study attempts to find out the problems that are affecting the teaching and learning process of these departments and thereby damaging the glory and reputation of them, and also to provide some suggestions to cope with those problems. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, History_score, score, and Risk. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model was trained and validated, achieving (...) an impressive accuracy of 100% and an average error of 0.000015, indicating its robust predictive capability. Moreover, we conducted feature importance analysis to identify the most influential features for predicting audit risk. The key features found to be critical for classifying fraudulent activities in audit risk prediction are Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, and History_score. This research contributes to the field of audit risk prediction by demonstrating the effectiveness of a neural network-based approach and highlighting the importance of specific features in detecting fraudulent activities. The findings have significant implications for auditors and organizations seeking to enhance their audit risk assessment processes, ultimately leading to improved financial transparency and fraud detection. (shrink)

In this paper, we introduce the integer programming in neutrosophic environment, by considering coffecients of problem as a triangulare neutrosophic numbers. The degrees of acceptance, indeterminacy and rejection of objectives are simultaneously considered. The Neutrosophic Integer Programming Problem (NIP) is transformed into a crisp programming model, using truth membership (T), indeterminacy membership (I), and falsity membership (F) functions as well as single valued triangular neutrosophic numbers. To measure the efficiency of the model, we solved several numerical examples.

Older people are encouraged to participate more in the economic, social, and governmental sectors as part of progressive aging policies worldwide. Very little is known about applying engagement techniques or carrying out strategies in Bangladesh that are important for involving seniors in social engagement. Therefore, this qualitative research was conducted in Dhaka, Bangladesh, among 385 people aged sixty or above to assess their understanding of the quality of life and the gap in government policy. Results showed that between 60 and (...) 65 were more mobile, creative, engaged in productive activity, and had significantly higher decision-making power (p<0.000). Aging greatly enhances dependency (p<0.000). Lower-educated people were significantly dependent on their families (p<0.000).Moreover, elderly income and the cost of treatment were significantly correlated to abuse (p<0.001). Furthermore, 10% of participants reported negative relationships with family members (p<0.031). However, most (61.6%) were unaware of the 2013 Parent's Care Act. Though most participants (73.2%) knew of the government-funded Old Age Allowance, 92.5% received no non-governmental assistance. The research concluded that education and economic condition have a long-term relationship with reducing dependency. In recommendation, Government can increase the retirement age from 59 to 65 years which may increase self of security, respect, and self-esteem and reduce harassment. Parent's Care Act 2013 should implement. Government should include Community Health and Social Service centers for the quality of life of the elderly. (shrink)

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a (...) large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%. (shrink)

The objective of Working Group 4 of the COST Action NET4Age-Friendly is to examine existing policies, advocacy, and funding opportunities and to build up relations with policy makers and funding organisations. Also, to synthesize and improve existing knowledge and models to develop from effective business and evaluation models, as well as to guarantee quality and education, proper dissemination and ensure the future of the Action. The Working Group further aims to enable capacity building to improve interdisciplinary participation, to promote knowledge (...) exchange and to foster a cross-European interdisciplinary research capacity, to improve cooperation and co-creation with cross-sectors stakeholders and to introduce and educate students SHAFE implementation and sustainability. To enable the achievement of the objectives of Working Group 4, the Leader of the Working Group, the Chair and Vice-Chair, in close cooperation with the Science Communication Coordinator, developed a template to map the current state of SHAFE policies, funding opportunities and networking in the COST member countries of the Action. On invitation, the Working Group lead received contributions from 37 countries, in a total of 85 Action members. The contributions provide an overview of the diversity of SHAFE policies and opportunities in Europe and beyond. These were not edited or revised and are a result of the main areas of expertise and knowledge of the contributors; thus, gaps in areas or content are possible and these shall be further explored in the following works and reports of this WG. But this preliminary mapping is of huge importance to proceed with the WG activities. In the following chapters, an introduction on the need of SHAFE policies is presented, followed by a summary of the main approaches to be pursued for the next period of work. The deliverable finishes with the opportunities of capacity building, networking and funding that will be relevant to undertake within the frame of Working Group 4 and the total COST Action. The total of country contributions is presented in the annex of this deliverable. (shrink)

Abstract: In this study, we explore the possibility of classifying the plant species. We collected the plant species from Kaggle website. This dataset encompasses 544 samples, encompassing 136 distinct plant species. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing plant Species classification accuracy and efficiency. This research explores plant Species classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 544 entries, we develop and evaluate a neural network model. Our neural network, (...) featuring a single hidden layer, achieves remarkable results—a staggering 100% accuracy and a minute average error rate of 0.002. Beyond performance metrics, we delve into the intricacies of plant Species classification through feature importance analysis. The most influential features— Vegsout, durflow, semiros, pdias, begflow, wind, leafy, autopoll and insects— uncover the physiological traits underpinning accurate rice classification. This research contributes to advancing rice classification methods and highlights the potential of ANNs in optimizing agricultural practices, ensuring plant safety, and bolstering global trade. (shrink)

According to the literature review, there is much room for improvement of college student retention. The aim of this research is to evaluate the possibility of using deep and machine learning algorithms to predict whether students continue to attend college or will stop attending college. In this research a feature assessment is done on the dataset available from Kaggle depository. The performance of 20 learning supervised machine learning algorithms and one deep learning algorithm is evaluated. The algorithms are trained using (...) 11 features from 1000 records of previous student registrations that have been enrolled in college. The best performing classifier after tuning the parameters was NuSVC. It achieved Accuracy (91.00%), Precision (91.00%), Recall (91.00%), F1-score (91.00%), and time required for training and testing (0.04 second). Additionally, the proposed DL algorithm scored: Accuracy (93.00%), Precision (93.00%), Recall (93.00%), F1-score (93.00%), time required for training and testing (0.66 second) for predicting whether student will continue to attend college or not. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City with 97.50 (...) % accuracy. The factor of DriveTrain has the most influence on MPG_City evaluation. Similar studies can be carried out for the evaluation of other characteristics of cars. (shrink)