25 results
Prof. Eunice E. Omonzejie, BA, Ed. (Ife), Grenoble-France), MA (Benin), PhD) (Benin)
AFRICAN MIGRANT MASCULINITIES, MODERNITIES AND CULTURAL IDENTITIES: DECODING CONTEMPORARY REALITIES IN FRANCOPHONE FICTION. IT IS AN INAUGURAL LECTURE DELIVERED BY PROF. ENICE OMONZEJIE. A PROFESOR OF METROPOLITAN FRENCH AND FRANCOPHONE LITERATURES AND CULTURES.
THE CONCEPT OF GEOPHYSICS AS A TOOL FOR SUSTAINABLE ENVIRONMENT STEWARDSHIP IN NIGERIA, WE WITNESS THE TANGIBLE CONSEQUENCES OF NEGLECTING THIS SUBTERRANEAN WORLD FROM DEVASTATING BUILDING COLLAPSE IN URBAN AREAS TO THE SILENT POISONING OF OUR WATER SOURCES.
ENGR. PROF. VINCENT OPE-OLUWA AFENYE AKPAIDA B. ENGR., ENG. (BENIN) PhD (EKPOMA)
THE SIGNIFICANCE OF PATH LOSS IN OUR LIVES AND ITS REFLECTION OF DIVINE PRESENCE
PROF. AZUKA ROMANU B.SC, PhD (EKPOMA), PGDE (NONUN) ND, PGD BIBLE THEOLOGY (NAST, EWU)
MICRO-OGRGANISMS ARE AMONG THE OLDEST FORMS OF LIFE ON EARTH. LONG BEFORE HUMANS, ANIMALS, OR PLANTS EXISTED, THESE TINY ORGANISMS WERE ALREADY SHAPING THE PLANET.
Prof. PROF. SUNDAY OLOWO SAMUEL
to the Almighty God who had seen today and set it apart for this purpose. I welcome you all who have come from far and near, and I want to immensely thank the Vice-Chancellor for giving me the opportunity to deliver the 98th Inaugural Lecture of this great University. This is the first Inaugural Lecture from the Department of Medical Microbiology and Parasitology, also the first in the Faculty of Basic Clinical Sciences. To the glory of God, today I arrive my academic destination!!!!
THIS LECTURE INTEGRATES TWO INTERRELATED NARRATIVES: MY PERSONAL JOURNEY IN BECOMING AN OBSTETRICS AND GYNAECOLOGIST, AND THE HISTORICAL EVOLUTION OF THE DEPARTMENT OF OBSTETRICS AND GYNAECOLOGY AT IRRUA SPECIALIST TEACHING HOSPITAL (ISTH), IN COLLABORATION WITH THE COLLEGE OF MEDICINE, AMBROSE ALLI UNIVERSITY, EKPOMA.
ABSTRACT Building intelligent software that can effectively detect accident scene with the help of Google map has suffered set back because of the poor ability of the currently used software to effectively detect, identify and classify accident scene images from non accident scene images. Hence there is need for a better technique of implementing this software. In this paper, Convolutional neural networks (CNN) which is a part of deep learning algorithm was used to provide a better classification technique that any software to be developed for the purpose of detecting accident scene image can adopt. The algorithm was tested on 4000 accident scene images with other kind of images (cats and dogs) by adopting models of other researchers. In this paper, classification accuracy and Mean Squared Error (MSE) were used to evaluate the algorithm in identifying accident scene images accurately. The result was further presented using a graph of MSE against a number of trained epochs. The result of the experiment shows accuracy in the image classification and identification
Marco Comi; Bhaskar DasGupta; Michael Schapira et al.
A traditionally desired goal when designing auction mechanisms is incentive compatibility, i.e., ensuring that bidders fare best by truthfully reporting their preferences. A complementary goal, which has, thus far, received significantly less attention, is to preserve privacy, i.e., to ensure that bidders reveal no more information than necessary. We further investigate and generalize the approximate privacy model for two-party communication recently introduced by Feigenbaum et al.[8]. We explore the privacy properties of a natural class of communication protocols that we refer to as "dissection protocols". Dissection protocols include, among others, the bisection auction in [9,10] and the bisection protocol for the millionaires problem in [8]. Informally, in a dissection protocol the communicating parties are restricted to answering simple questions of the form "Is your input between the values αand β(under a predefined order over the possible inputs)?". We prove that for a large class of functions, called tiling functions, which include the 2nd-price Vickrey auction, there always exists a dissection protocol that provides a constant average-case privacy approximation ratio for uniform or "almost uniform" probability distributions over inputs. To establish this result we present an interesting connection between the approximate privacy framework and basic concepts in computational geometry. We show that such a good privacy approximation ratio for tiling functions does not, in general, exist in the worst case. We also discuss extensions of the basic setup to more than two parties and to non-tiling functions, and provide calculations of privacy approximation ratios for two functions of interest.
Dr. M. O. ODIGHI of the department of computer science; Prof. M. I. OMOGBHEMHE of the department of computer science
Abstract Accurate crop recommendation systems are essential for optimizing agricultural productivity and sustainability, yet existing approaches often fail to integrate diverse environmental factors and adapt to location-specific conditions. This study proposes a hybrid machine learning model that leverages soil and climate parameters through a threestage pipeline: Random Forest for feature selection, Extreme Gradient Boosting for robust prediction, and a lightweight Feed forward Neural Network for final decision-making. The model was evaluated on real-world datasets, demonstrating superior performance with an overall accuracy of 95.3%, precision of 95.1%, recall of 95.0%, F1-score of 95.1%, and a root mean squared error (RMSE) of 0.12. Ablation experiments reveal that excluding Random Forest feature selection reduces accuracy to 91.9%, omitting geospatial adaptation lowers accuracy to 92.6%, and replacing the neural network with logistic regression drops accuracy further to 89.2%. These results confirm the effectiveness of the hybrid architecture and the critical role of feature selection and geospatial adaptation in enhancing crop recommendation accuracy. This work presents a scalable and locationsensitive framework with significant potential to advance precision agricultur
