ISSN:2320-9151 Impact Factor:3.5

Volume 10, Issue 2, February 2022 Edition - IEEE-SEM Journal Publication


Efosa-Ehioghiren, Augustina Izehiuwa PhD & Haruna Ezekiel E.O

Children have been subjected to innumerable shades and forms of abuse and maltreatment, the issue of child abuse and neglect has generated much debated and controversy as it concerns its harmful effects. It is based on this understanding that the writers ventured into this area with a view of bringing out its harmfulness, psychological consequences and psychosocial support needed. The writers started by examining the contextual meaning and epidemiology of child abuse, the neglect and the psychosocial support given to the victims. Highlighting the different forms of the phenomenon and its theories, the implication was that the counselors should help the children to understand the law that protect them and how they can benefit from it and most importantly to speak out in face of the abuse and neglect. the reviewer made a case for the total eradication of this forms of social malaise and recommended among others that the government, the family, the church as well as the advocacy groups should cooperate in child welfare matters in the form of exchange of information facilities, training and tracking of duly allocated resourced to children especially the specially needs children.


Shreepooja Yelugoti, Arya Sasidharan, Sayali Bijutkar, Juilee Gayachari

Within the present scenario, the majority of the population uses the ATM system to withdraw cash. At the same time, there are countless ATM thefts and robberies that have occurred in many localities of the city, although Closed-circuit Television(CCTV) cameras are installed within the ATM center, there is no improvement in the reduction of ATM thefts and robberies, therefore there is a need to enhance the present ATM system. A good way to abate these kinds of robberies is by means of the usage of smart and embellished technology. In the proposed system the face of the person seeking to withdraw the amount of cash is detected and is compared to the original cardholder with the help of face recognition algorithms like Local Binary Pattern Histogram(LBPH), Linear Discriminant Analysis(LDA), Speeded Up Robust Features(SURF), Principle Component Analysis(PCA). If the face is matched, the transaction is continued, or else a message or email along with the snap of the withdrawer is sent to the registered mobile number or email of the cardholder for confirmation and further procedures.

Challenges of Batteries For Electric VehiclesPDF

Udom Ifeanyi Emmanuel

Battery represents arguably the most important and most technically challenging component of the electric vehicle (EV). The electric vehicle (EV) technology addresses the issue of the reduction of carbon and greenhouse gas emissions. The concept of EVs focuses on the utilization of alternative energy resources. However, EV systems currently face challenges in energy storage systems (ESSs) with regard to their safety, size, cost, and overall management issues, hence the reason for the stated seminar topic on the challenges of batteries for EVs. In addition, the hybridization of ESSs with advanced power electronic technologies has a significant influence on optimal power utilization to lead advanced EV technologies. Batteries for electric vehicles have many real-life challenges. First, you need a sophisticated system to monitor the batteries to make sure that they are balanced charged and that they are protected from a lot of external conditions like overcharge, over-discharge, over current, short circuits. If any of these conditions occur at any time, it might trigger thermal runaway and cause disastrous consequences to humans and the environment. Finally, the paper also highlights a number of key factors and challenges and presents the possible recommendations for the development of next-generation of EVs and battery management systems for electric vehicles and battery energy storage systems.

Class Schedule and Performance: Does Time of the Day Affect Students’ LearningPDF

Ronnel A. dela Cruz

Learning can be attributed to several factors that may affect students’ performance. Several researches indicate that class scheduling has an impact on academic achievement in an educational setting. This action research focuses on the potential effect of morning and afternoon schedule in the course Systems Analysis and Design of the Third Year Bachelor of Science in Information Technology students in terms of their performance in class. Several researches cited indicate that learning occurs when a student is taught at their preferred time of the day. In addition, the researcher used Test of Significant Difference between the performance of students in the morning and afternoon Session. Based on the study conducted, one contributing factor that affects the learning of students is their schedule. Students’ schedule has an impact on their class engagement and performance in class. This research proves that students’ performance in the morning session performed well compared to those in the afternoon session.

Impact of F-A-T Strategies on Level of Problem-Solving Skills among STE and RBEC Students: A Comparative StudyPDF

Richard M. Oco, PhD

This study aims to determine the Level of Problem-Solving Skills among grade 9 STE and RBEC Students. Specifically, it seeks to provide data on level of problem-solving skills based on pretest and posttest results with the aid of FAT strategies which were implemented in a span of 3 months. The respondents of this study were the 94 students comprised of 47 from STE Class and 47 from RBEC Class. Statistical inferences like mean, percentage, t-test and Pearson product moment correlation coefficient were utilized to check the statistical significance of the data. Study revealed that the interventions in Mathematics on Word Problem Solving Skills has significant strong positive correlation to the students’ level of achievement in mathematics wherein from only 25% of the students who were able to reach the Outstanding and Very Satisfactory Level of problem-solving skills in mathematics as it was increased to 67%. Moreover, the 41% of students who were on the Fairly Satisfactory and Did not meet Expectations Level went down to 9% respectively. The percentage average score of 84.00% equivalent to Satisfactory Level increased to 88.00% equivalent to Very Satisfactory Level. The computed r-value of 0.786 indicating strong positive correlation is higher than the r-tabular value which was 0.205 and 0.05 level of significance while the computed t-value of 5.050 is higher than the t-tabular value which was at 1.984 at 0.05 level of significance. In the final analysis, the interventions have positive impact towards improving the students’ problem-solving skills which is also pivotal in developing their critical thinking skills.

Using Artificial Neural Networks to Forecast Egyptian economic indicatorPDF

Ahmed Nabil, Khaled M. Metwally, Omar Saker, El-Henawy I.M

Gross Domestic Product (GDP) refers to the market value of all final goods and services produced within a country in a given period. GDP can be thought as the size of an economy, this allowed many decision makers relied on GDP in their economic polices and decisions. Thus, GDP considered the foremost important macroeconomic performance measure of a country. Regarding the GDP prediction economic impact, GDP prediction has gained a great interest last years, this could help decision makers in their economic decisions and applied policies. Future GDP expectations becomes the primary determinant in many other sectors, e.g., investment sector, employment sector, economic sector, and also in stock market activities. This research work presents a GDP prediction case study on Egypt economy. The presented study implemented alternative prediction models that relied on Artificial Intelligence (AI) techniques, e.g., Long Short Term Memory (LSTM). An enhanced LSTM models were proposed for GDP prediction. The proposed enhanced LSTM models have implemented uni-variate (GDP) time-series and multivariate (GDP, Unemployment, Inflation rate) time-series schemes integrated with an ensemble approach for selecting the best prediction results. The obtained results showed that multivariate with two macroeconomic indicators (GDP and Inflation rate) outperformed uni-variate and multivariate with three indicators in Root Mean Square Error (RMSE) and Coefficient- of-Determination (R2).