Pedestrian detection and vehicle type recognition using CENTROG features for nighttime thermal images

This paper proposes a feature-based technique to detect pedestrians and recognize vehicles within thermal images that have been captured during nighttime. The proposed technique applies the support vector machine (SVM) classifier on CENsus Transformed histogRam Oriented Gradient (CENTROG) features in order to classify and detect humans and/or vehicles. Although thermal images suffer from low image resolution, lack of colour and poor texture information, they offer the advantage of being unaffected by high intensity light sources such as vehicle headlights which tend to render normal images unsuitable for nighttime image capturing and subsequent analysis. Since contour is the most distinctive feature within thermal images, CENTROG is used to capture this feature information and is used within the experiments. The experimental results so obtained were compared with those obtained by employing the CENsus TRansformed hISTogram (CENTRIST). Experimental results revealed that CENTROG offers better detection and classification accuracy for both pedestrian and detection and vehicle type recognition

Handcrafted and Transfer Learned Feature Techniques for Vehicle Make and Model Recognition on Nigerian Road

The vehicle makes and model recognition (VMMR) is a challenging task due to the wide range of vehicle categories and similarities between different classes. Studies have shown that works have recognized vehicles of different countries' make and models. Popular vehicles on Nigerian roads may include products like; Toyota, Honda, Peugeot, Benz, Innoson Vehicle Manufacturing (IVM), etc. The VMMR is important in the intelligent transport system hence, this paper presents a handcrafted and transfer learning model to detect stationary vehicles and classify them based on brand, make, and model. A new dataset was introduced consisting of selected images of popular brands of vehicles driven on Nigerian roads. Framework for a vehicle make and model recognition was developed by extracting features using EfficientNet and HOG models and evaluated on the locally gathered datasets. For classification, a linear Support Machine Vector (SVM) was used. Experimental results showed 94.5% on HOG, 97% with EfficientNet, and 98.1% accuracy when HOG and EfficientNet features were concatenation.  The proposed concatenated model outperformed HOG and EfficientNet extracted features by providing higher accuracy and confusion matrix with the highest number of classified images. The study shows the advantages of the proposed model in terms of its accuracy in terms of identifying the vehicle make and model

A Gender Recognition System Using Facial Images with High Dimensional Data

Gender recognition has been seen as an interesting research area that plays important roles in many fields of study. Studies from MIT and Microsoft clearly showed that the female gender was poorly recognized especially among dark-skinned nationals. The focus of this paper is to present a technique that categorise gender among dark-skinned people. The classification was done using SVM on sets of images gathered locally and publicly. Analysis includes; face detection using Viola-Jones algorithm, extraction of Histogram of Oriented Gradient and Rotation Invariant LBP (RILBP) features and trained with SVM classifier. PCA was performed on both the HOG and RILBP descriptors to extract high dimensional features. Various success rates were recorded, however, PCA on RILBP performed best with an accuracy of 99.6% and 99.8% respectively on the public and local datasets. This system will be of immense benefit in application areas like social interaction and targeted advertisement

Performance Evaluation of Machine Learning Predictive Analytical Model for Determining the Job Applicants Employment Status

Several higher institution of learning faces issue or difficulty of turning out more than 90% of their graduates who can competently satisfy and meet the requirements of the industry. However, the industry is also confronted with the difficulty of sourcing skilled tertiary institution graduates that match their needs. Failure or success of any organization depends mostly on how its workforce is recruited and retained. Therefore, the selection of an acceptable or satisfactory candidate for the job position is one of the major and vital problems of management decision-making. This work, therefore, proposes a modern, accurate and worthy machine learning classification model that can be deployed, implemented, and put to use when making predictions and assessments on job applicant's attributes from their academic performance datasets in other to meet the selection criteria for the industry. Both supervised and unsupervised machine learning classifiers were considered in this work. Naïve Bayes, Logistic Regression, support vector machine (SVM). Random Forest and Decision tree performed well, but Logistic Regression outperformed others with 93% accuracy

Perspective on Dark-Skinned Emotion Recognition using Deep-Learned and Handcrafted Feature Techniques

Image recognition has been widely used in various fields of applications such as human—computer interaction, where it can enhance fluency, accuracy, and naturalness in interaction. The need to automate the decision on human expression is high. This paper presents a technique for emotion recognition and classification based on a combination of deep-learned and handcrafted features. Residual Network (ResNet) and Rotation Invariant Local Binary Pattern (RILBP) features were combined and used as features for classification. The aim is to classify, identify, and make judgment on facial images from dark-skinned facial images. Facial Expression Recognition 2013 (FER2013) and self-captured dark-skinned datasets were used for the experiment and validated. The result showed 93.4% accuracy on FER dataset and 95.5% on self-captured dataset, which proved the efficiency of the proposed model

Adoption of Internet of Things (IoT) among organizations in Lagos State, Nigeria

Today, Lagos State, Nigeria is a major hub for the headquarters of national and global organizations. However, there has been little knowledge of the Internet of Things (IoT) adoption among organizations in the state despite the benefits its offer; such as, optimization in operations, reduction in costs, and improvement in efficiency. The study assessed the current use of the IoT among twenty-nine (29) organizations in Lagos State. The research study employed a non-probability purposive sampling approach while adopting the Monitor Deloitte for Ericsson Denmark maturity model which describe the levels of sophistication with which IoT can be deploy. The statistical package used for the study was IBM SPSS Statistics 23 while research hypotheses were tested using the one-way analysis of variance (One-way ANOVA) and the Chi-Square of Association. Results from the study showed that IoT remains non-existence, in research, planning and early stages of adoption. Thus, organizations still have potential to not only utilize IoT more, but also to sophisticate how IoT is used. These findings form the baseline for future studies on the adoption of IoT among organizations in the state, tracking organizations usage of it over time and giving an indication of how quickly organizations are progressing with the IoT.Keywords: Big Data, Internet of Things (IoT), Machine to Machine (M2M), Nigeria, Sensors, Network

Gender recognition based fingerprints using dynamic horizontal voting ensemble deep learning

Despite tremendous advancements in gender equality, there are still persistent gender disparities, especially in important human activities. Consequently, gender inequality and related concerns are serious problems in our global society. Major players in the global economy have identified the gender identity system as a crucial stepping stone for bridging the enormous gap in gender-based problems. Extensive research conducted by forensic scientists has uncovered a unique pattern in the fingerprint, and these distinguishing characteristics of fingerprints can be utilized to determine the gender of individuals. Numerous research has revealed various fingerprint-based approaches to gender recognition. This research aims to present a novel dynamic horizontal voting ensemble model with a hybrid Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) deep learning algorithm as the base learner to determine human gender attributes based on fingerprint patterns automatically. More than four thousand Live fingerprint images were acquired and subjected to training, testing, and classification using the proposed model. The results of this study indicated over 99% accuracy in predicting a person’s gender. The proposed model also performed better than other state-of-the-art models, such as ResNet-34, VGG-19, ResNet-50, and EfficientNet-B3, when implemented on the SOCOFing public dataset

Gender recognition based fingerprints using dynamic horizontal voting ensemble deep learning

Despite tremendous advancements in gender equality, there are still persistent gender disparities, especially in important human activities. Consequently, gender inequality and related concerns are serious problems in our global society. Major players in the global economy have identified the gender identity system as a crucial stepping stone for bridging the enormous gap in gender-based problems. Extensive research conducted by forensic scientists has uncovered a unique pattern in the fingerprint, and these distinguishing characteristics of fingerprints can be utilized to determine the gender of individuals. Numerous research has revealed various fingerprint-based approaches to gender recognition. This research aims to present a novel dynamic horizontal voting ensemble model with a hybrid Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) deep learning algorithm as the base learner to determine human gender attributes based on fingerprint patterns automatically. More than four thousand Live fingerprint images were acquired and subjected to training, testing, and classification using the proposed model. The results of this study indicated over 99% accuracy in predicting a person’s gender. The proposed model also performed better than other state-of-the-art models, such as ResNet-34, VGG-19, ResNet-50, and EfficientNet-B3, when implemented on the SOCOFing public dataset