AN AUTOMATED ENERGY BILL METERING SYSTEM BASED ON GSM TECHNOLOGY

The measurement of the energy consumed by residential and commercial buildings by utility provider is important in billing, control, and monitoring of the usage of energy. Traditional metering techniques used for the measurement of energy are not convenient and is prone to different forms of irregularities. These irregularities include meter failure, meter tampering, inaccuracies in billing due to human error, energy theft, and loss of revenue due to corruption, etc. This research study proposed the design and construction of a microcontroller-based electric energy metering system using the Global System for Mobile communication (GSM) network. This system provides a solution to the irregularities posed by the traditional metering technique by allowing the utility provider have access to remote monitoring capabilities, full control over consumer load, and remote power disconnection in the case of energy theft. Proteus simulation software was used to model the system hardware and the software was obtained by using embedded C programming and visual basic. It was observed that the system could remotely take accurate energy readings, provided full control over consumer loads and execute remote disconnection in case of energy theft. The system provides high performance and high accuracy in power monitoring and power management.   &nbsp

Facial Image Verification and Quality Assessment System -FaceIVQA

Although several techniques have been proposed for predicting biometric system performance using quality values, many of the research works were based on no-reference assessment technique using a single quality attribute measured directly from the data. These techniques have proved to be inappropriate for facial verification scenarios and inefficient because no single quality attribute can sufficient measure the quality of a facial image. In this research work, a facial image verification and quality assessment framework (FaceIVQA) was developed. Different algorithms and methods were implemented in FaceIVQA to extract the faceness, pose, illumination, contrast and similarity quality attributes using an objective full-reference image quality assessment approach. Structured image verification experiments were conducted on the surveillance camera (SCface) database to collect individual quality scores and algorithm matching scores from FaceIVQA using three recognition algorithms namely principal component analysis (PCA), linear discriminant analysis (LDA) and a commercial recognition SDK. FaceIVQA produced accurate and consistent facial image assessment data. The Result shows that it accurately assigns quality scores to probe image samples. The resulting quality score can be assigned to images captured for enrolment or recognition and can be used as an input to quality-driven biometric fusion systems.DOI:http://dx.doi.org/10.11591/ijece.v3i6.503

AN AUTOMATED ENERGY METER READING SYSTEM USING GSM TECHNOLOGY

The measurement of the energy consumed by residential and commercial buildings by utility provider is important in billing, control and monitoring of the usage of energy. Traditional metering techniques used for the measurement of energy are not convenient and is prone to different forms of irregularities. These irregularities include inaccuracies in billing due to human error, energy theft, loss of revenue due to corruption and so on. This research study proposed the design and construction of a microcontroller based electric energy metering system using the Global System for Mobile communication (GSM) network. This system provides solution to the irregularities posed by the traditional metering technique by allowing the utility provider have access to remote monitoring capabilities, full control over consumer load, and remote power disconnection in the case of energy theft. Proteus simulation software was used to model the system hardware and the software was obtained by using embedded C programming and visual basic. It was observed that the system could remotely take accurate energy readings, provided full control over consumer loads and execute remote disconnection in case of energy theft. The system provides high performance and high accuracy in power monitoring and power management. Keywords: GSM, Automati

PREDICTING SOCIAL NETWORK ADDICTION USING VARIANT SIGMOID TRANSFER FEED-FORWARD NEURAL NETWORKS (FNN-SNA)

Researchers have reflected on personal traits that may predict Social Networking Sites (SNS) addiction. However, most of the researchers involved in the findings of personality traits predictor for social networking addiction either postulate or based their conclusions on analytical tools. Moreso, a review of the literature reveals that the prediction of social networking addiction using classifiers have not been well researched. We examined the prediction of SNS addiction from a well-structured questionnaire consisting of sixteen (16) personality traits. The questionnaire was administered on the google form with a response rate of 95% out of the 102-sample size. Additionally, a three (3) variant sigmoid transfer feed- forward neural networks was developed for the prediction of SNS addiction. Result indicated that pertinence (β = 0.251, p  0.01) was the most powerful predictor of social networking addiction in general and less obscurity addiction (β = 0.244, p  0.01). Experimental results also showed that the developed classifier correctly predict SNS addiction with 98% accuracy compared to similar classifiers.     &nbsp

Predictive Analytics and Software Defect Severity: A Systematic Review and Future Directions

Software testing identifies defects in software products with varying multiplying effects based on their severity levels and sequel to instant rectifications, hence the rate of a research study in the software engineering domain. In this paper, a systematic literature review (SLR) on machine learning-based software defect severity prediction was conducted in the last decade. The SLR was aimed at detecting germane areas central to efficient predictive analytics, which are seldom captured in existing software defect severity prediction reviews. The germane areas include the analysis of techniques or approaches which have a significant influence on the threats to the validity of proposed models, and the bias-variance tradeoff considerations techniques in data science-based approaches. A population, intervention, and outcome model is adopted for better search terms during the literature selection process, and subsequent quality assurance scrutiny yielded fifty-two primary studies. A subsequent thoroughbred systematic review was conducted on the final selected studies to answer eleven main research questions, which uncovers approaches that speak to the aforementioned germane areas of interest. The results indicate that while the machine learning approach is ubiquitous for predicting software defect severity, germane techniques central to better predictive analytics are infrequent in literature. This study is concluded by summarizing prominent study trends in a mind map to stimulate future research in the software engineering industry.publishedVersio

BLACKFACE SURVEILLANCE CAMERA DATABASE FOR EVALUATING FACE RECOGNITION IN LOW QUALITY SCENARIOS

Many face recognition algorithms perform poorly in real life surveillance scenarios because they were tested with datasets that are already biased with high quality images and certain ethnic or racial types. In this paper a black face surveillance camera (BFSC) database was described, which was collected from four low quality cameras and a professional camera. There were fifty (50) random volunteers and 2,850 images were collected for the frontal mugshot, surveillance (visible light), surveillance (IR night vision), and pose variations datasets, respectively. Images were taken at distance 3.4, 2.4, and 1.4 metres from the camera, while the pose variation images were taken at nine distinct pose angles with an increment of 22.5 degrees to the left and right of the subject. Three Face Recognition Algorithms (FRA), a commercially available Luxand SDK, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were evaluated for performance comparison in low quality scenarios. Results obtained show that camera quality (resolution), face-to-camera distance, average recognition time, lighting conditions and pose variations all affect the performance of FRAs. Luxand SDK, PCA and LDA returned an overall accuracy of 97.5%, 93.8% and 92.9% after categorizing the BFSC images into excellent, good and acceptable quality scales.

GENDER AND PARENTS’ EDUCATIONAL QUALIFICATIONS ON ACHIEVEMENT MOTIVATION OF COVENANT UNIVERSITY

Achievement motivation level among university students varies. Some are highly motivated and achieve success while others are lowly motivated and experience little success. This study examined the influence of gender and parents’ educational qualifications on achievement motivation level of Covenant University students. The scope was limited to undergraduate students of the four Colleges in Covenant University, namely - College of Business and Social Sciences, Engineering, Science and Technology, and Leadership Development Studies. To achieve the objective of this study, two research questions and four hypotheses were raised and formulated respectively to guide the investigation of the study. The sample of the study consisted of three hundred (300) students comprising 206 males and 94 females randomly selected. Questionnaire forms were used for data collection. The study made use of the ex-post factor method which consists of survey and descriptive designs. Results show that female students’ achievement motivation level is stronger than that of the males at 1% level of significance (0.626). There is no significant correlation between father’s highest educational qualification and students’ achievement motivation level for both males and females at (0.064). However, there was a significant relationship between mother’s highest educational qualification and students’ achievement motivation level. (0.105). Based on the findings, it was recommended that male students should be given the same level of attention as the females by parents. In addition, the university should introduce sustainable mentorship programmes with faculty as role models to motivate the male students

Mel-Frequency Cepstral Coefficients and Convolutional Neural Network for Genre Classification of Indigenous Nigerian Music

Music genre classification is a field of study within the broader domain of Music Information Retrieval (MIR) that is still an open problem. This study aims at classifying music by Nigerian artists into respective genres using Convolutional Neural Networks (CNNs) and audio features extracted from the songs. To achieve this, a dataset of 524 Nigerian songs was collected from different genres. Each downloaded music file was converted from standard MP3 to WAV format and then trimmed to 30 seconds. The Librosa sc library was used for the analysis, visualization and further pre-processing of the music file which includes converting the audio signals to Mel-frequency cepstral coefficients (MFCCs). The MFCCs were obtained by taking performing a Discrete Cosine Transform on the logarithm of the Mel-scale filtered power spectrum of the audio signals. CNN architecture with multiple convolutional and pooling layers was used to learn the relevant features and classify the genres. Six models were trained using a categorical cross-entropy loss function with different learning rates and optimizers. Performance of the models was evaluated using accuracy, precision, recall, and F1-score. The models returned varying results from the classification experiments but model 3 which was trained with an Adagrad optimizer and learning rate of 0.01 had accuracy and recall of 75.1% and 84%, respectively. The results from the study demonstrated the effectiveness of MFCC and CNNs in music genre classification particularly with indigenous Nigerian artists

IMPLEMENTATION OF A BIMODAL BIOMETRIC ACCESS CONTROL SYSTEM FOR DATA CENTER

The use of biometrics has become one of the only sure ways to provide secure access control to rooms where vital asset are stored, such as data centers where valuable information are stored. This paper aim at designing and implementing a bimodal biometric access control system for data center using fingerprint and Iris trait of the same person, it is called bimodal biometric system. The system was implemented by integrating hardware components such as PIC18F452 microcontroller, fingerprint and iris sensors and so no with the software programs as such C language and MYSQL interface. On testing, it is found to improve the security and reliability in the access control systems management of the data center

Text messaging-based medical diagnosis using natural language processing and fuzzy logic

Research ArticleThe use of natural language processing (NLP) methods and their application to developing conversational systems for health diagnosis increases patients’ access to medical knowledge. In this study, a chatbot service was developed for the Covenant University Doctor (CUDoctor) telehealth system based on fuzzy logic rules and fuzzy inference. The service focuses on assessing the symptoms of tropical diseases in Nigeria. Telegram Bot Application Programming Interface (API) was used to create the interconnection between the chatbot and the system, while Twilio API was used for interconnectivity between the system and a short messaging service (SMS) subscriber. The service uses the knowledge base consisting of known facts on diseases and symptoms acquired from medical ontologies. A fuzzy support vector machine (SVM) is used to effectively predict the disease based on the symptoms inputted. The inputs of the users are recognized by NLP and are forwarded to the CUDoctor for decision support. Finally, a notification message displaying the end of the diagnosis process is sent to the user. The result is a medical diagnosis system which provides a personalized diagnosis utilizing self-input from users to effectively diagnose diseases. The usability of the developed system was evaluated using the system usability scale (SUS), yielding a mean SUS score of 80.4, which indicates the overall positive evaluationTaikomosios informatikos katedraVytauto Didžiojo universiteta