The phenomenon of product/business failure, as well as lack of environmental sustainability and learning limitations, is fast becoming a recurrent ‘disease’ for investors, designers, design sponsors and education policy makers in many developing countries with poor persuasiveness contributing a large quota to such failures. This has greatly hampered the education, poverty alleviation and developmental efforts of the governments of such societies. In a bid to curb this negative trend, children, who are major influencers in product purchase behaviours of adults, have been targeted specifically by persuasive designers, in an effort to both educate and adopt them as means of reaching the larger populace. However, most researches in current persuasive system designs are limited to the information communication/management technology or computerized environments. These systems are technology/internet-driven and many potential users, in reality, in the developing world, unlike the rest of the world is often made to believe, do not have open access to such systems. Unfortunately, the effectiveness of any persuasive system is dependent on its accessibility to its user(s). Technological backwardness (often concealed behind ostentatious self-deceptive facades) has led to the poor persuasiveness of local persuasive systems and products in the third worlds. Therefore, adopting a mixed method for establishing the factor(s) limiting the efficiency of the computer/electronic-human interaction persuasive systems (CHIPS) in South-West Nigeria (N=900), this study established the need to adopt more of the product/entity-human interaction persuasive system (PEHIPS) as an effective alternative for third world countries as, based on the study outcomes, the CHIPS proved less relatively effective in comparison to PEHIPS in rural regions. It however recommends the alternating adoption of a combination of both computerized and entity/product driven systems for the purpose of optimizing persuasive effectiveness in developing worlds.

Technology is developed and utilized as an honest computer in order that it can provide useful information. With the aim of developing and meeting business objectives, the utilization of sales transaction data in minimarket GP is processed into information or knowledge as a recommendation to ascertain the possible value of purchased simultaneously. This processing uses data mining.
Database buildup in computerized systems is justified by getting added value from this data set. Data mining can predicts trends and therefore the nature of business behavior which is extremely useful to support important deciding.
The algorithm wont to form the association rules during this study is CT-Pro. CT-Pro algorithm may be a development of FP-Growth. The difference is within the second step where FP-Growth creates the FP-Tree arrangement while CT-Pro makes the Compressed FP-Tree (CFP-Tree) arrangement. The CT-Pro algorithm process by analyzing employing a tree system where the things most frequently purchased become root and other items will follow the basis. The CFP-Tree process will provide levels for every transaction and facilitate mining results.
CT-Pro algorithm implementation with CFP-Tree arrangement applied to data mining systems is in a position to research sales data for 3 months, namely January 2020 – March 2020 with a complete data record of 1.303 and 320 sales transactions at minimarket GP become information or knowledge. The results of this study are the relationship between the tendency of products that are bought together based on categories in a kind of percentage to be used as a recommendation in structuring the position of items that are mutually frequent in certain categories.

Testing is considered as one of the expensive activities in software development process. Fixing the defects during testing process can increase the cost as well as the completion time of the project. Cost of testing process can be reduced by identifying the defective modules during the development (before testing) stage. This process is known as “Software Defect Prediction”, which has been widely focused by many researchers in the last two decades. This research proposes a classification framework for the prediction of defective modules using variant based ensemble learning and feature selection techniques. Variant selection activity identifies the best optimized versions of classification techniques so that their ensemble can achieve high performance whereas feature selection is performed to get rid of such features which do not participate in classification and become the cause of lower performance. The proposed framework is implemented on four cleaned NASA datasets from MDP repository and evaluated by using three performance measures, including: F-measure, Accuracy, and MCC. According to results, the proposed framework outperformed 10 widely used supervised classification techniques, including: “Naïve Bayes (NB), Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF)”.

Automatic Environmental Sound Recognition (AESR) is an essential topic in modern research in the field of pattern recognition. We can convert a short audio file of a sound event into a spectrogram image and feed that image to the Convolutional Neural Network (CNN) for processing. Features generated from that image are used for the classification of various environmental sound events such as sea waves, fire cracking, dog barking, lightning, raining, and many more. We have used the log-mel spectrogram auditory feature for training our six-layer stack CNN model. We evaluated the accuracy of our model for classifying the environmental sounds in three publicly available datasets and achieved an accuracy of 92.9% in the urbansound8k dataset, 91.7% accuracy in the ESC-10 dataset, and 65.8% accuracy in the ESC-50 dataset. These results show remarkable improvement in precise environmental sound recognition using only stack CNN compared to multiple previous works, and also show the efficiency of the log-mel spectrogram feature in sound recognition compared to Mel Frequency Cepstral Coefficients (MFCC), Wavelet Transformation, and raw waveform. We have also experimented with the newly published Rectified Adam (RAdam) as the optimizer. Our study also shows a comparative analysis between the Adaptive Learning Rate Optimizer (Adam) and RAdam optimizer used in training the model to correctly classifying the environmental sounds from image recognition architecture.

Intranets & Intranet-wares have become a central junction/platform for implementing the organization’s specific work culture like computing, communication and collaboration. In order to ensure smooth and effective communication, computing and collaboration among employees, intranets play an important role. For vertical and horizontal management, we use intranet-wares for directing, reporting, collaborating, socializing, communicating and meeting or discussing the professional and social issues. Today, cloud-based computing, communication, and collaboration have created new frontiers and emerging paradigms towards re-engineering of work cultures in the organizations. In order to enhance the performance with extended features, next-generation computing, communication, and collaboration the intranet needs redesign and migration strategy over alternative technology platforms. This research paper tries to answer the research questions that how an alternative technology strategy or pathway can be explored for enhancing the performance and extending the features of the exiting designs of educational Intranets. Further; how an on-premise intranet can be migrated over cloud platforms with enhanced performance and extended/add-on features. After analysis of collected facts, understanding the issues, challenges and limitations of the existing state of art intranets, a strong need for performance enhancement and add on features was observed for Intranets. The study deeply investigated and analyzed the issues, challenges and limitations i.e. features and performances of the current state of the art of the intranets in general and on-premise Intranet of AMU in specific. Finally, an Intranet Migration Strategy Model over Hybrid Cloud was designed and developed using SaaS (i.e. AMU CloudNet). In this study, the Interact Intranet was used for designing and demonstrating the functional prototype of intranet show that how computing, communication and collaboration services can be enhanced with anytime, anywhere and boundary-less access.