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Age and Gender Influence on Suicidal Ideation Among Undergraduates in Ilorin Metropolis, Kwara State, Nigeria

Abstract: This study investigated age and gender influence on suicidal ideation among undergraduates in Ilorin Metropolis. A descriptive research design was used for this study. The participants of this study consisted of 250 randomly selected undergraduates from five faculties of Al-Hikmah University, Ilorin Nigeria. The suicidal Ideation Scale used by Roberts and Chen (1995) was adopted to collect the data. Two null hypotheses were tested at 0.05 critical region. The finding indicated that there were significant gender and age difference in the suicidal ideation of undergraduates in Ilorin Metropolis. The findings were discussed and necessary recommendations based on the findings of the study were highlighted. This includes the fact that intervention programmes for managing self-destructive ideation among undergraduates should take into cognizance the influence of age and gender. Author(s) Name: Dr. Adekola Kamil Lasisi Paper ID: MIJRDV1I10002 Pages: 19-24 Full Text: Download
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Deep Learning Applied in Computer Vision

Abstract: Computer vision is a multidisciplinary field in computational intelligence and artificial intelligence that guide intelligent systems and machines towards understanding the content of images or video. It has come under the spotlight in recent times due to the remarkable advancement and breakthroughs day-by-day such as autonomous driving, intelligent systems, pedestrian system, robotics, medical imaging, remote sensing, object detection, security, speech sequence, image registration, biometric technologies, image retrieval and video processing to mention just a few. The aim of this work is to provide a systematic review in the application of deep learning in various aspects of computer vision that is object detection, object classification, scene classification, image segmentation, image retrieval, image registration, object recognition, feature extraction, image fusion and anomaly detection by providing detail description in a systematic way of all the deep learning techniqu