Lung Cancer Detection Using Artificial Neural Network

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

Web Application for Generating a Standard Coordinated Documentation for CS Students’ Graduation Project in Gaza Universities

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

Energy Efficiency Prediction using Artificial Neural Network

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%

Handwritten Signature Verification using Deep Learning

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%

A Retrospective Appraisal of Teacher Induction

A Retrospective Appraisal of Teacher Induction Abstract Examination of an induction program for new teachers was undertaken from the viewpoint of induction graduates three years after participation. Their retrospective perspectives were investigated as to their satisfaction with assimilation in school in the induction year, their attitudes towards organizational aspects of the program, and the program\u27s contribution to their professional development. Comparisons were made to beginning teachers in the midst of their induction year. Data were collected from 98 induction graduates and 390 induction participants using questionnaires. Compared to induction participants, graduates retrospectively remembered the induction year at school less positively and more often recommended extending induction support. The graduates ascribed only moderate contribution to the induction program. In general retrospective appraisals of active teachers and non-teaching graduates were similar. Implications for the use of retrospective evaluations are discussed

Predictors of Teacher Educators\u27 Research Productivity

This study examined the relationship between teacher educators\u27 research productivity (RP) and their background and professional characteristics, attitudes, motives, obstacles and time devoted to research. The sample included 161 teacher educators from four teacher education colleges in Israel. The findings indicate the significance of five variables for predicting RP: academic degree, rank, administrative position, desire to develop new knowledge and learn from research findings and perceived insufficient research competence and self-confidence. These variables account for 37.2% of the variance in RP. The results from this study provide useful information for teacher education institutions and policy makers regarding variables significantly related to RP. These variables should be addressed when recruiting teacher educators, assigning administrative duties and designing professional development programs, particularly for new career faculty

Suggestions to Enhance the Scholarly Search Engine: Google Scholar

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

A Flood Risk Management Program of Wadi Baysh Dam on the Downstream Area: An Integration of Hydrologic and Hydraulic Models, Jizan Region, KSA

For public safety, especially for people who dwell in the valley that is located downstream of a dam site, as well as the protection of economic and environmental resources, risk management programs are urgently required all over the world. Despite the high safety standards of dams because of improved engineering and excellent construction in recent times, a zero-risk guarantee is not possible, and accidents can happen, triggered by natural hazards, human actions, or just because the dam is aging. In addition to that is the impact of potential climate change, which may not have been taken into account in the original design. A flood risk management program, which is essential for protecting downstream dam areas, is required. Part of this program is to prepare an inundation map to simulate the impact of dam failure on the downstream areas. The Baysh dam has crucial importance both to protect the downstream areas against flooding, to provide drinking water to cities in the surrounding areas, and to use the excess water for irrigation of the agricultural areas located downstream of the dam. Recently, the Kingdom of Saudi Arabia (KSA) was affected by extraordinary rainstorm events causing many problems in many different areas. One of these events happened along the basin of the Baysh dam, which raised the alarm to the decision makers and to the public to take suitable action before dam failure occurs. The current study deals with a flood risk analysis of Wadi Baysh using an integration of hydrologic and hydraulic models. A detailed field investigation of the dam site and the downstream areas down to the Red Sea coast has been undertaken. Three scenarios were applied to check the dam and the reservoir functionality; the first scenario at 100-and 200-year return period rainfall events, the second scenario according to the Probable Maximum Precipitation (PMP), and the third scenario if the dam fails. Our findings indicated that the Baysh dam and reservoir at 100-and 200-year rainfall events are adequate, however, at the PMP the water will spill out from the spillway at ~8900 m3/s causing flooding to the downstream areas; thus, a well-designed channel along the downstream wadi portion up to the Red Sea coast is required. However, at dam failure, the inundation model indicated that a vast area of the section downstream of the dam will be utterly devastated, causing a significant loss of lives and destruction of urban areas and agricultural lands. Eventually, an effective warning system and flood hazard management system are imperative

Fraudulent Financial Transactions Detection Using Machine Learning

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%

Implications and Applications of Artificial Intelligence in the Legal Domain

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