The Use of Artificial Intelligence and Robotics -Related Films as a Tool to Introduce the Middle School Students to Artificial Intelligence and Robotics.

تساهم الأفلام القائمة على الذكاء الاصطناعي والروبوتات بشكل كبير في صناعة الأفلام، ومن الواضح أن أعدادها متناسبة مع أعمال السينما التي زادت بسرعة خلال العقد الماضي. وقد لوحظت هذه النسب المتزايدة منها بسبب التطور غير المسبوق في مفاهيم ومشاريع الذكاء الاصطناعي وتكنولوجيا الروبوتات التي اجتذبت طيفًا واسعًا من فئة المجتمعات. لذلك، يهدف هذا البحث إلى التحقق من تأثير هذا النوع من الأفلام على مجموعة من طلاب المرحلة الإعدادية. تم إعداد المنهجية المقترحة لهذه الدراسة لقياس الجوانب المختلفة لطلاب المرحلة الإعدادية على النحو التالي: (1) إدخال مثل هذه الموضوعات التكنولوجية و (2) زيادة مهارات الإبداع والتخيل. تم إجراء استبيان لتقييم الأثر على طلاب المدارس الإعدادية قبل وبعد مشاهدة مقاطع فيلم محددة مسبقًا تتعلق بالذكاء الاصطناعي والروبوتات. أظهرت نتائج الاستبيان أن مثل هذا النوع من الأفلام له آثار إيجابية أولية على دوافع الطلاب وإلهامهم وإبداعهم.Artificial intelligence and robotics-based films contribute significantly to the films industry, it is evidenced that it is proportion as part of the film business was increased rapidly during last decade. This was observed due to the increases in the artificial intelligence and robotics technology which have been attracting wide spectrum of the communities’ category. Therefore, this paper aim to investigate the impact of this type of films on the middle school students’ group. The proposed methodology of this study is made to measure various aspects for the middle school students as follows: (1) Introducing such technological topics and (2) Increasing the creativity and imagination skills. A survey to evaluate the impact on middle school students was conducted that consists of a list questionnaire before and after watching a pre-selected film clips related to artificial intelligence and robotics. It was evidenced that such as films type have initial positive effects on the students’ motivations, inspiration and creativity

An evolutionary computation approach to electricity trade negotiation

This paper presents an evolutionary negotiation process for power generating and power consuming companies in a distributed electricity market environment. The process is implemented within an electricity agent to best select a negotiation strategy that meets the underlying company objectives and interests. The negotiating agent generates a sequence of improving strategy population as the outcome of a search method modeled by the selection, crossover, and mutation genetic operators. Agents use a content specification language based on an extended object model to specify the requirements, constraints, and negotiation strategic rules, which are used by the negotiation server to conduct a negotiation. A design architecture and a framework for negotiation is presented with detailed KQML communication primitives that make up the negotiation protocol. Various software technologies have been used for implementation and tested in a C++ environment

An Innovative Model for University – Industry Partnership

Knowledge-technology transfer is the core driver for national prosperity and enhancement of intellectual capitals. Jordan has taken major steps towards economic transformation from a traditional rhythm into a knowledge base motivated by the accelerative impact of Information and Communication Technology (ICT). In this context, Yarmouk University has developed dynamic programmes of ICT enrichment and adopted an innovative partnership model. This model represents an in-campus facility that works towards bridging the gap between academia and industry. It provides a rich internal collaborative environment that brings together business and technical faculties in the pursuit of projects with an objective to cultivate collaboration with industrial and business partners; update the knowledge base of the university with the latest industry developments; and align the skills and knowledge of students to real and immediate industry needs. This paper presents an innovative partnership model and describes its philosophy adopted to maximise the rate of success of knowledge-technology transfer within a bidirectional relationship body. Results of the application of this model reinforce common ideas about the ingredients for success as measured in knowledge-technology transfer metrics on utilised resources, available technology, level of effort, national competitiveness, innovative technology, macroeconomic and microeconomic levels, and internal performance

Performance Predictions of Sci-Fi Films via Machine Learning

The films teenagers watch have a significant influence on their behavior. After witnessing a film starring an actor with a particular social habit or personality trait, viewers, particularly youngsters, may attempt to adopt the actor’s behavior. This study proposes an algorithm-based technique for predicting the market potential of upcoming science fiction films. Numerous science fiction films are released annually, and working in the film industry is both profitable and delightful. Before the film’s release, it is necessary to conduct research and make informed predictions about its success. In this investigation, different machine learning methods written in MATLAB are examined to identify and forecast the future performance of movies. Using 14 methods for machine learning, it was feasible to predict how individuals would vote on science fiction films. Due to their superior performance, the fine, medium, and weighted KNN algorithms were given more consideration. In comparison to earlier studies, the KNN-adopted methods displayed greater precision (0.89–0.93), recall (0.88–0.92), and accuracy (90.1–93.0%), as well as a rapid execution rate, more robust estimations, and a shorter execution time. These tabulated statistics illustrate that the weighted KNN method is effective and trustworthy. If several KNN algorithms targeting specific viewer behavior are logically coupled, the film business and its global expansion can benefit from precise and consistent forecast outcomes. This study illustrates how prospective data analytics could improve the film industry. It is possible to develop a model that predicts a film’s success, effect, and social behavior by assessing features that contribute to its success based on historical data

A Smart Infrared Microcontroller-Based Blind Guidance System

Blindness is a state of lacking the visual perception due to physiological or neurological factors. The partial blindness represents the lack of integration in the growth of the optic nerve or visual centre of the eye, and total blindness is the full absence of the visual light perception. In this work, a simple, cheap, friendly user, smart blind guidance system is designed and implemented to improve the mobility of both blind and visually impaired people in a specific area. The proposed work includes a wearable equipment consists of head hat and mini hand stick to help the blind person to navigate alone safely and to avoid any obstacles that may be encountered, whether fixed or mobile, to prevent any possible accident. The main component of this system is the infrared sensor which is used to scan a predetermined area around blind by emitting-reflecting waves. The reflected signals received from the barrier objects are used as inputs to PIC microcontroller. The microcontroller is then used to determine the direction and distance of the objects around the blind. It also controls the peripheral components that alert the user about obstacle's shape, material, and direction. The implemented system is cheap, fast, and easy to use and an innovative affordable solution to blind and visually impaired people in third world countries

Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process

A low-cost, fast, dependable, repeatable, non-invasive, portable, and simple-to-use vascular screening tool for coronary artery diseases (CADs) is preferred. Photoplethysmography (PPG), a low-cost optical pulse wave technology, is one method with this potential. PPG signals come from changes in the amount of blood in the microvascular bed of tissue. Therefore, these signals can be used to figure out anomalies within the cardiovascular system. This work shows how to use PPG signals and feature selection-based classifiers to identify cardiorespiratory disorders based on the extraction of time-domain features. Data were collected from 360 healthy and cardiovascular disease patients. For analysis and identification, five types of cardiovascular disorders were considered. The categories of cardiovascular diseases were identified using a two-stage classification process. The first stage was utilized to differentiate between healthy and unhealthy subjects. Subjects who were found to be abnormal were then entered into the second stage classifier, which was used to determine the type of the disease. Seven different classifiers were employed to classify the dataset. Based on the subset of features found by the classifier, the Naïve Bayes classifier obtained the best test accuracy, with 94.44% for the first stage and 89.37% for the second stage. The results of this study show how vital the PPG signal is. Many time-domain parts of the PPG signal can be easily extracted and analyzed to find out if there are problems with the heart. The results were accurate and precise enough that they did not need to be looked at or analyzed further. The PPG classifier built on a simple microcontroller will work better than more expensive ones and will not make the patient nervous