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)

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)

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)

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 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)

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)

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)

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)

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)

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)

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)

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: The intelligent embedded agricultural robotic system is a low cost and efficient microcontroller robot which include; A soil moisture monitoring system which monitors the moisture content of the soil in the various parts of the field and the measured data to a microcontroller unit which in turn displays the received data on a Liquid Crystal Display to determine when to irrigate or spray the farm field. An automatic car, which follows a path designed in the field, i.e., a white (...) line on a black surface integrated with a spraying and irrigating mechanism for carrying out agricultural spray and irrigation. An automatic solar tracking system that tracks the sun’s position for maximum light intensity was used to charge the DC 12V battery which was used to power to the robotic system. A refilling system with obstacle detection feature was used for refilling water, chemical or fertilizer spray in the spraying and irrigating tank integrated with the line follower robotic system. This embedded system is useful for farmers who wish to monitor the soil moisture content of the field, and automate spraying and irrigation purposes at low costs and higher efficiency. (shrink)

This study aimed to identify the availability of business process re-engineering requirements in Palestinian industrial companies, and to identify the nature of relationship and direction between business process re-engineering requirements and product quality in these companies, in addition to determine whether the market orientation plays a mediating role in the relationship between business process re-engineering requirements and product quality. To achieve study objectives, the researcher designed a questionnaire as a study tool, was distributed on sample of (231) employee selected from (...) the senior management in Palestinian industrial companies. The results shows: business process re-engineering requirements (Organizational, Technological, Human) are available in Palestinian industrial companies with a large degree. There is a positive impact with a statistical significance the availability of business process re-engineering requirements on market orientation and achieving product quality in Palestinian industrial companies. There is a positive impact with a statistical significance the market orientation adoption and achieving product quality in Palestinian industrial companies. Results revealed that market orientation mediating the relationship between business process re-engineering requirements and achieving product quality. The Main recommendations: Needing of senior management to make attention about modern management methods, as well as relying on scientific and field approach to solve and tackle problems. Senior management should be recognize the importance of market orientation, and works on linking it with information systems, and it's basic center should be concentrates on meeting customer needs. Key words: strategic orientation, modern management methods, operational performance, product quality, marketing . (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)

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)

Objective: to evaluate CANLPH score and systemic immune inflammation index (SII) in patients with symptomatic rheumatic mitral stenosis (MS) undergoing percutaneous mitral balloon valvuloplasty (PMBV). -/- Methods: 62 patients who underwent PMBV in our clinic between 2018 and 2021 were included retrospectively. The patients were divided into 2 groups according to echo score. The CANLPH score was calculated from the cut-off values of C-reactive protein to albumin ratio (CAR), neutrophil to lymphocyte ratio (NLR) and platelet to hemoglobin ratio (PHR), determined (...) with the Youden index and SII by the formula platelet x neutrophil/lymphocyte. P <0.05 was considered statistically significant. -/- Results: The mean age of the patients was 44.5±10.4 years (40 female, 64.5%). The mean values of SII and CANLPH scores were higher in the Echo score >8 group (p <0.001, both). The mean mitral gradient before and after PMBV was 12.6±5.7mm Hg and 5.0±2.4 mm Hg, and the mean valve area was 1.12±0.27 cm2 and 1.85±0.29 cm2. A statistically significant and negative correlation was observed between the gradient decrease after the procedure and the CANLPH score and SII (r=-0.426, p=0.001 and r=-0.418, p=0.001, respectively). In receiver operating characteristic (ROC) curve analysis, it was concluded that the ability of CANPLH score to predict the higher Echo scores was noninferior to SII with an area under curve (AUC:0.820 (0.701-0.906) and AUC:0.786 (0.664-0.880), z statistics 0.576 and p=0.564. -/- Conclusion: SII and CANLPH scores are correlated with Echo score. A significant negative relationship was found with both biomarkers and gradient decrease after the PMBV. (shrink)

Abstract: The analytical, comparative cross-sectional study was conducted to assess the thyroid profiles and modified lipid profiles levels among Sudanese patients with coronary heart disease performed on forty-one patients with coronary heart disease as test group collected from Sudan Heart Center, Al rebat teaching hospital and Al mawada hospital in Khartoum state, during the period between November 2017 and May 2018. Furthermore, the test group compared with forty-one apparently healthy volunteers as control group was selected with the same inclusion criteria. (...) Spectrophotometeric methods were used for measurement of lipid profile. Thyroid hormones (T3 &T4) and thyroid stimulating hormone were measured by using Sandwich Enzyme-Linked Immunosorbent Assay (ELISA). Moreover, statistical package for social science (SPSS version 23) computer software was used for data analysis. The study clearly demonstrated that there was statistically significant decrease in T3, and significant increase in T4 levels in test group compared to control with (P. value 0.047) for T3, and (P. value 0.002) for T4.Nonetheless, the result of this study indicated a significant elevation in mean of total cholesterol, low density lipoprotein-cholesterol levels and significant decrease in high density lipoprotein –cholesterol in test group compared to control (P.value 0.000). In conclusion Coronary heart diseases patients have significantly low Triiodothyronine, elevated Thyroxin, and normal Thyroid Stimulating Hormone levels, with significantly higher Total Cholesterol, Low Density Lipoprotein-Cholesterol concentrations, and lower HighDensity Lipoprotein-Cholesterol concentrations. (shrink)

[...] Rousseau bir yandan çağının yükselen değerlerinden yararlanırken diğer yandan bu değerlerin içeriden eleştirisini yapmayı başarabilen düşünürlerden biri olduğu için fikirleri ölümünden asırlar sonra bile önemini yitirmemiştir. Demokratik devletlerin meşruiyet krizinin giderek derinleştiği ve çoğunlukçu, majoritarian, ideolojilerin etraflıca sorgulanmaya başlandığı çağımızda, demokrasiyi çoğunluk kararına ek olarak “rıza”, “Yurttaşlık”, “sivil özgürlük”, “kamusal uzlaşı” ve “Genel İrade” kavramlarıyla birlikte ele alan Rousseau’yu yeniden okumak önemlidir [...] Rousseau-demokrasi ilişkisinin kazılıp ortaya çıkartılacağı bu metinde uğranılacak olan kavramsal duraklar sırasıyla: Eşitsizlik (doğal ve toplumsal), özgürlük (...) (doğal ve sivil), politik bütün (bodypolitic), Genel İrade, ortak iyi (common good) ve Egemen olmalıdır. Söz konusu kavramlar, Rousseau’nun onlara yüklediği özgün anlamları gözden kaçırılmadan sanki ilk defa karşılaşılıyormuşçasına bir zihin açıklığı ile okundukları zaman, onun demokrasi görüşü de gün ışığına çıkartılabilir. (shrink)

This fourteenth volume of Collected Papers is an eclectic tome of 87 papers in Neutrosophics and other fields, such as mathematics, fuzzy sets, intuitionistic fuzzy sets, picture fuzzy sets, information fusion, robotics, statistics, or extenics, comprising 936 pages, published between 2008-2022 in different scientific journals or currently in press, by the author alone or in collaboration with the following 99 co-authors (alphabetically ordered) from 26 countries: Ahmed B. Al-Nafee, Adesina Abdul Akeem Agboola, Akbar Rezaei, Shariful Alam, Marina Alonso, Fran Andujar, (...) Toshinori Asai, Assia Bakali, Azmat Hussain, Daniela Baran, Bijan Davvaz, Bilal Hadjadji, Carlos Díaz Bohorquez, Robert N. Boyd, M. Caldas, Cenap Özel, Pankaj Chauhan, Victor Christianto, Salvador Coll, Shyamal Dalapati, Irfan Deli, Balasubramanian Elavarasan, Fahad Alsharari, Yonfei Feng, Daniela Gîfu, Rafael Rojas Gualdrón, Haipeng Wang, Hemant Kumar Gianey, Noel Batista Hernández, Abdel-Nasser Hussein, Ibrahim M. Hezam, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Muthusamy Karthika, Nour Eldeen M. Khalifa, Madad Khan, Kifayat Ullah, Valeri Kroumov, Tapan Kumar Roy, Deepesh Kunwar, Le Thi Nhung, Pedro López, Mai Mohamed, Manh Van Vu, Miguel A. Quiroz-Martínez, Marcel Migdalovici, Kritika Mishra, Mohamed Abdel-Basset, Mohamed Talea, Mohammad Hamidi, Mohammed Alshumrani, Mohamed Loey, Muhammad Akram, Muhammad Shabir, Mumtaz Ali, Nassim Abbas, Munazza Naz, Ngan Thi Roan, Nguyen Xuan Thao, Rishwanth Mani Parimala, Ion Pătrașcu, Surapati Pramanik, Quek Shio Gai, Qiang Guo, Rajab Ali Borzooei, Nimitha Rajesh, Jesús Estupiñan Ricardo, Juan Miguel Martínez Rubio, Saeed Mirvakili, Arsham Borumand Saeid, Saeid Jafari, Said Broumi, Ahmed A. Salama, Nirmala Sawan, Gheorghe Săvoiu, Ganeshsree Selvachandran, Seok-Zun Song, Shahzaib Ashraf, Jayant Singh, Rajesh Singh, Son Hoang Le, Tahir Mahmood, Kenta Takaya, Mirela Teodorescu, Ramalingam Udhayakumar, Maikel Y. Leyva Vázquez, V. Venkateswara Rao, Luige Vlădăreanu, Victor Vlădăreanu, Gabriela Vlădeanu, Michael Voskoglou, Yaser Saber, Yong Deng, You He, Youcef Chibani, Young Bae Jun, Wadei F. Al-Omeri, Hongbo Wang, Zayen Azzouz Omar. (shrink)

This twelfth volume of Collected Papers includes 86 papers comprising 976 pages on Neutrosophics Theory and Applications, published between 2013-2021 in the international journal and book series “Neutrosophic Sets and Systems” by the author alone or in collaboration with the following 112 co-authors (alphabetically ordered) from 21 countries: Abdel Nasser H. Zaied, Muhammad Akram, Bobin Albert, S. A. Alblowi, S. Anitha, Guennoun Asmae, Assia Bakali, Ayman M. Manie, Abdul Sami Awan, Azeddine Elhassouny, Erick González-Caballero, D. Dafik, Mithun Datta, Arindam (...) Dey, Mamouni Dhar, Christopher Dyer, Nur Ain Ebas, Mohamed Eisa, Ahmed K. Essa, Faruk Karaaslan, João Alcione Sganderla Figueiredo, Jorge Fernando Goyes García, N. Ramila Gandhi, Sudipta Gayen, Gustavo Alvarez Gómez, Sharon Dinarza Álvarez Gómez, Haitham A. El-Ghareeb, Hamiden Abd El-Wahed Khalifa, Masooma Raza Hashmi, Ibrahim M. Hezam, German Acurio Hidalgo, Le Hoang Son, R. Jahir Hussain, S. Satham Hussain, Ali Hussein Mahmood Al-Obaidi, Hays Hatem Imran, Nabeela Ishfaq, Saeid Jafari, R. Jansi, V. Jeyanthi, M. Jeyaraman, Sripati Jha, Jun Ye, W.B. Vasantha Kandasamy, Abdullah Kargın, J. Kavikumar, Kawther Fawzi Hamza Alhasan, Huda E. Khalid, Neha Andalleb Khalid, Mohsin Khalid, Madad Khan, D. Koley, Valeri Kroumov, Manoranjan Kumar Singh, Pavan Kumar, Prem Kumar Singh, Ranjan Kumar, Malayalan Lathamaheswari, A.N. Mangayarkkarasi, Carlos Rosero Martínez, Marvelio Alfaro Matos, Mai Mohamed, Nivetha Martin, Mohamed Abdel-Basset, Mohamed Talea, K. Mohana, Muhammad Irfan Ahamad, Rana Muhammad Zulqarnain, Muhammad Riaz, Muhammad Saeed, Muhammad Saqlain, Muhammad Shabir, Muhammad Zeeshan, Anjan Mukherjee, Mumtaz Ali, Deivanayagampillai Nagarajan, Iqra Nawaz, Munazza Naz, Roan Thi Ngan, Necati Olgun, Rodolfo González Ortega, P. Pandiammal, I. Pradeepa, R. Princy, Marcos David Oviedo Rodríguez, Jesús Estupiñán Ricardo, A. Rohini, Sabu Sebastian, Abhijit Saha, Mehmet Șahin, Said Broumi, Saima Anis, A.A. Salama, Ganeshsree Selvachandran, Seyed Ahmad Edalatpanah, Sajana Shaik, Soufiane Idbrahim, S. Sowndrarajan, Mohamed Talea, Ruipu Tan, Chalapathi Tekuri, Selçuk Topal, S. P. Tiwari, Vakkas Uluçay, Maikel Leyva Vázquez, Chinnadurai Veerappan, M. Venkatachalam, Luige Vlădăreanu, Ştefan Vlăduţescu, Young Bae Jun, Wadei F. Al-Omeri, Xiao Long Xin.‬‬‬‬‬‬. (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)

In this paper, Taylor series is used to solve neutrosophic multi-objective programming problem (NMOPP). In the proposed approach, the truth membership, Indeterminacy membership, falsity membership functions associated with each objective of multi-objective programming problems are transformed into a single objective linear programming problem by using a first order Taylor polynomial series. Finally, to illustrate the efficiency of the proposed method, a numerical experiment for supplier selection is given as an application of Taylor series method for solving neutrosophic multi-objective programming problem (...) at end of this paper. (shrink)

Causal finitism, the view that the causal history of any event must be finite, has garnered much philosophical interest recently—especially because of its applicability to the Kalām cosmological argument. The most prominent argument for causal finitism is the Grim Reaper argument, which attempts to show that, if infinite causal histories are possible, then other paradoxical states of affairs must also be possible. However, this style of argument has been criticized on the grounds of (i) relying on controversial modal principles, and (...) (ii) providing a false diagnosis of the paradoxes involved. In this paper, I develop a new kind of Grim Reaper argument immune to these criticisms. I show that, by using insights from the literature on time travel, causal finitists should instead argue that infinite causal histories are problematically inexplicable, as they entail the possibility of unexplained foiling mechanisms. The fruits of this paper are that (i) a novel supporting argument for the Kalām is developed, and (ii) along the way of building this argument, it is shown that the literatures on time travel and causal finitism are deeply and intimately connected. (shrink)

The Successive Addition Argument (SAA) is one of the key arguments espoused by William Lane Craig for the thesis that the universe began to exist. Recently, Malpass, Mind, 131(523), 786–804 (2021) has developed a challenge to the SAA by way of constructing a counterexample that originates in the work of Fred Dretske. In this paper, I show that the Malpass-Dretske counterexample is in fact no counterexample to the argument. Utilizing a distinction between properties of members and properties of collections, I (...) argue that Malpass’ counterexample has no bearing on the soundness of the SAA. I also develop a novel parity argument against Malpass’ argument that I demonstrate can only be resolved by way of the aforementioned analysis. (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)

It is a common intuition in scientific practice that positive instances confirm. This confirmation, at least based purely on syntactic considerations, is what Nelson Goodman’s ‘Grue Problem’, and more generally the ‘New Riddle’ of Induction, attempt to defeat. One treatment of the Grue Problem has been made along Bayesian lines, wherein the riddle reduces to a question of probability assignments. In this paper, I consider this so-called Bayesian Grue Problem and evaluate how one might proffer a solution to this problem (...) utilizing what I call a phenomenological approach. I argue that this approach to the problem can be successful on the Bayesian framework. (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: 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)

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)

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)

The purpose of this article is to discuss Safavid period Islamic philosopher Sadraddin Shirazi’s philosophical methodology and the sources of the school founded by him. The article relies on research conducted on Shirazi philosophy. It shows that Shirazi through synthesizing the methods of the earlier schools that existed in Islam to acquire knowledge devised a new mechanism for acquiring knowledge. Before coming to Shirazi, intellectual movements formed during Islam’s classical period, such as peripateticism, illuminationism, theology and Gnosticism, were of different (...) views regarding the best way to reach the truth. For example, for peripatetics reason, for theologians revelation, for gnostics/mystics intuition or personal experience were important, but ishraqi school was somewhere between peripatetic and Gnostic schools. Sadraddin Shirazi, though, considered all these agents necessary, for they complement each other, and said they should be utilized altogether to obtain ultimate reality. Similarly, considering his philosophical sources, we see that he is influenced almost by all philosophical schools as well as religious sources. However, Ibn Sina and Ibn Arabi’s influence on him was more profound compared to other philosophers. (shrink)

This paper describes an Intelligent Tutoring System for helping users with ADO NET called ADO-Tutor. The IntellTutoring System was designed and developed using (ITSB) authoring tool for building intelligent educational systems. The user learns through the intelligent tutoring system ADO NET, the technology used by Microsoft NET to connect to databases. The material includes lessons, examples, and questions. Through the feedback provided by the intelligent tutoring system, the user's understanding of the material is assessed, and accordingly can be guided to (...) different difficulty level of exercises and/or the lessons. The Intelligent Tutoring System was evaluated by a group of users and the results were more than satisfactory in terms of the quality of the material and the design of the system. (shrink)

Abstract: A complex parasite such as human Plasmodium is likely to generate a variety of substances that injure the hosts directly or cause immunopathology. In malaria, a blood concentration of anti-inflammatory cytokines, such as interleukin (IL-10) is increased. The present study was performed to analyze IL-10 levels in patients with malaria falciparum and healthy controls individuals and correlate with malaria density infection as well as age groups. It is a cross sectional study was carried out in Khartoum state /Sudan, a (...) total of 70 febrile patients and 20 healthy volunteers as controls were included in this study. Blood samples were collected from both patients and control candidates in (EDTA) containers for parasitological and immunological tests. Immunochromatography test and microscopic examination for both thick and thin blood films were used to detect the positive samples for Plasmodium falciparum. Enzyme linked Immunosorbant Assay (ELISA), was used for the determination of IL-10 levels. The result showed that of IL-10 levels in malaria patients were 1598.50 and SD 4771.64 while the levels in healthy participants the level was 10.39 and SD was 124.33 the difference was found to be statistically significant (P= 0.007). The levels of cytokines mentioned above were observed to be raised in malaria individual compared to healthy control candidates. (shrink)

This paper describes an Intelligent Tutoring System for helping users with ADO.NET called ADO-Tutor. The Intelligent Tutoring System was designed and developed using (ITSB) authoring tool for building intelligent educational systems. The user learns through the intelligent tutoring system ADO.NET, the technology used by Microsoft.NET to connect to databases. The material includes lessons, examples, and questions. Through the feedback provided by the intelligent tutoring system, the user's understanding of the material is assessed, and accordingly can be guided to different difficulty (...) level of exercises and/or the lessons. The Intelligent Tutoring System was evaluated by a group of users and the results were more than satisfactory in terms of the quality of the material and the design of the system. (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)

This thesis analyses Deleuze & Guattari’s notion of becoming through certain performative encounters in contemporary political art, and re-conceptualizes them as “art(s) of becoming”. Art(s) of becoming are actualizations of a non-representational –minoritarian– mode of becoming and creation as well as the political actions of fleeing quanta. The theoretical aim of the study is, on the one hand, to explain how Platonic Idealism is overturned by Deleuze’s reading of Nietzsche and Leibniz, and on the other hand, how Cartesian dualism of (...) mind and body is surpassed by following a Spinozistic theory of affects. In this respect, the dissertation has both theoretical and practical dimensions. Since art(s) of becoming are bodies without organs which constitute their own lines of flight through a process of minoration, the concepts of body, affect, becoming, and intensity are central to this study. For the same reason, this is an attempt to show the intersections of philosophical, political and aesthetic domains in Deleuze’s theory of sensation which is part of his general practice of philosophy, that is, a quest for establishing an ontology of immanence as opposed to identitarian metaphysics. (shrink)

Abstract: This research aims at the design and implementation of an automatic high-performance voltage stabilizer which helps to detect inappropriate voltage levels and correct them to produce a reasonably stable output. The system consists of five dependent architectural designs which involve the power supply, delay switching circuit, monitoring circuit and load changeover circuit using 555 ic which produces the required signal when switched on by a relay to activate the alarm. The system uses a relay to charge the batteries and (...) automatically switches on this supply to supply the loads. It is composed of switching transistors and relay to control the power provided to ac loads, and it has LEDs for indicating the state of the system, 240-220vac/120-110vac switch, voltmeter displaying load voltage, heatsink, and fans for cooling the system. The system protects electrical equipment and machine against voltage surge. (shrink)

Abstract: The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, necessitating accurate and timely forecasting of cases for effective mitigation strategies. In this research paper, we present a novel approach to predict COVID-19 cases using Artificial Neural Networks (ANNs), harnessing the power of machine learning for epidemiological forecasting. Our ANNs-based forecasting model has demonstrated remarkable efficacy, achieving an impressive accuracy rate of 97.87%. This achievement underscores the potential of ANNs in providing precise and data-driven insights into the dynamics (...) of the pandemic. However, this paper underscores the critical importance of a comprehensive evaluation beyond accuracy, including metrics such as sensitivity, specificity, and the area under the ROC curve (AUC-ROC), to assess the model's performance robustness. The research paper offers detailed insights into the architecture of the ANN model, encompassing critical hyperparameters, data preprocessing techniques, and regularization strategies employed to optimize model accuracy. Ethical considerations surrounding data privacy and potential biases within the COVID-19 dataset are also addressed. While the achieved accuracy is a significant milestone, this study underscores the dynamic and evolving nature of the pandemic, necessitating continuous model refinement and validation. Furthermore, it emphasizes the importance of considering false positives and false negatives in the context of public health decision-making. In conclusion, this research contributes to the arsenal of tools available for pandemic management by showcasing the potential of ANNs in COVID-19 case forecasting. It encourages ongoing exploration and adaptation of predictive models to enhance their applicability in real-world public health scenarios, ultimately contributing to more effective pandemic control and response efforts. (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.

The given is the state of a mind in its primary engagement with the world. A satisfactory epistemology—one, it turns out, that is foundationalist and includes a naïve realist view of perception—requires a certain account of the given. Moreover, knowledge based on the given requires both a particular view of the world itself and a heterodox account of judgment. These admittedly controversial claims are supported by basic ontological considerations. I begin, then, with two contradictory views of the world per se (...) and the structure one experiences. I draw out the consequences of these two views for what intentionality is. The two views yield incompatible accounts of the given. The definitive spontaneity of the one account, and passivity of the other, can be understood in terms of the structure (or lack thereof) in the given. In defense of the claim that a structured given is not an apt epistemic basis, I examine an attempt to found an epistemology on such an account in light of the so-called myth of the given. I maintain that the given, if it is to provide some justification for taking the world to be a particular way, must be unstructured. To support this, I first discuss a significant problem with traditional foundationalism. I then argue that a satisfactory (foundationalist) epistemology requires the rejection of the orthodox propositional view of judgment in favor of a non-propositional, reistic view. (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)