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Software requirements negotiation (SRN) is one of the most essential stages of software requirements engineering. SRN involves the stakeholder's interaction to reach a mutual understanding of the requirements for developing a software project. The increased research interest in requirements engineering has resulted in huge literature in the SRN domain. There is a need to investigate the broad of techniques, processes, and evaluation mechanisms used in the SRN research community. This study aims to examine and identify the existing methods, processes, evaluation mechanisms, quantity of publications, publication trends and demographics shaping SRN research domain. To accomplish our aim, we used an evidence-based systematic approach, and 67 relevant studies were ultimately chosen from the search process based on the formulated research questions. Our study result shows broad and promising SRN techniques that include agent-based negotiation, TAICOS, wikiwinwin and winbook. However, we found that the existing SRN techniques suffer from limitations that have stakeholders' communication gaps, interface issues to non-technical users, decision-making, and managing requirements changes. Moreover, our study found that 63% of the selected studies stated their SRN processes, with 22% adopting the basic SRN processes in winwin model. Five evaluation mechanisms were discovered, with case study and experiment most adopted by the selected studies with 44% and 30%, respectively. In conclusion, although research on SRN is recently gaining some traction, the works in the domain are insufficient. Concrete proposals are needed to improve SRN in software requirements engineering research domain.

A descriptive correlational study was used to determine the types and effects of domestic violence as well as the relationship between domestic violence and adolescents’ academic performance by gender in Borno State, Nigeria. The target population of the study comprised of 20,774 SS 11 students in public secondary schools, however, 300 subjects constituted the sample. A stratified random sampling technique was used in selecting the respondents based on gender, school location, and years of exposure to domestic violence. 155(51.67%) of the subjects were males while 145(48.33%) were females. Researchers’ self-authored 36 item instrument tagged “Domestic Violence Questionnaire (DVQ)” was used to elicit data for the study. The instrument had a validity index of .83 and a Cronbach alpha reliability coefficient of .91. Four objectives, three research questions, and one null hypothesis piloted the study. Data collected were analyzed using mean, standard deviation, and t-test. The findings of the study identified slapping and suicidal ideation as some of the types of domestic violence. Some of the effects of domestic violence include downheartedness, trauma, and melancholy in Borno State. Thus, arresting the menace of domestic violence requires a multi-dimensional approach blending the positive actions of the government at all levels and counsellors among other stakeholders. Some counselling implications and recommendations were proffered.

COVID‐19 pandemic has affected over 100 countries in a matter of weeks. People's response toward social distancing in the emerging pandemic is uncertain. In this study, we evaluated the influence of information (formal and informal) sources on situational awareness of the public for adopting health‐protective behaviors such as social distancing. For this purpose, a questionnaire‐based survey was conducted. The hypothesis proposed suggests that adoption of social distancing practices is an outcome of situational awareness which is achieved by the information sources. Results suggest that information sources, formal (P = .001) and informal (P = 0.007) were found to be significantly related to perceived understanding. Findings also indicate that social distancing is significantly influenced by situational awareness, P = .000. It can, therefore, be concluded that an increase in situational awareness in times of public health crisis using formal information sources can significantly increase the adoption of protective health behavior and in turn contain the spread of infectious diseases., Highlights Reducing mortality caused by COVID‐19 can be achieved by awareness.Situation awareness can be increased by formal information sources.Increased situational awareness lead to adoption of health protective behavior.

Software fault localization is one of the most tedious and costly activities in program debugging in the endeavor to identify faults locations in a software program. In this paper, the studies that used spectrum-based fault localization (SBFL) techniques that makes use of different multiple fault localization debugging methods such as one-bug-at-a-time (OBA) debugging, parallel debugging, and simultaneous debugging in localizing multiple faults are classified and critically analyzed in order to extensively discuss the current research trends, issues, and challenges in this field of study. The outcome strongly shows that there is a high utilization of OBA debugging method, poor fault isolation accuracy, and dominant use of artificial faults that limit the existing techniques applicability in the software industry.

Aspect-based sentiment analysis (ABSA) is currently among the most vigorous areas in natural language processing (NLP). Individuals, private and government institutions are increasingly using media sources for decision making. In the last decade, aspect extraction has been the most essential phase of sentiment analysis (SA) to conduct an abridged sentiment classification. However, previous studies on sentiment analysis mostly focused on explicit aspects extraction with limited work on implicit aspects. To the best of our knowledge, this is the first systematic review that covers implicit, explicit, and the combination of both implicit and explicit aspect extractions. Therefore, this systematic review has been conducted to, 1) identify techniques used for extracting implicit, explicit, or both implicit and explicit aspects; 2) analyze the various evaluation metrics, data domains, and languages involved in the implicit and explicit aspect extraction in sentiment analysis from years 2008 to 2019; 3) identify the key challenges associated with the techniques based on the result of a comprehensive comparative analysis; and finally, 4) highlight the feasible opportunities for future research directions. This review can be used to assist novice and prominent researchers to understand the concept of both implicit and explicit aspect extractions in aspect-based sentiment analysis domain.

Aspect-based sentiment analysis (ABSA) is described as one of the most vibrant research areas over the last decade. However, due to the exponential increase in aspect-based sentiment researches, there is a massive interest in advanced explicit aspect extraction (EAE) techniques. This interest brings about a huge amount of literature in the EAE domain. This study aims to investigate and identify the existing approaches, techniques, types of research, quantity of publications, publication trends and demographics shaping the EAE research domain in the last decade (2009 - 2019). Accordingly, an evidence-based systematic methodology was adopted to effectively capture all the relevant studies. The main findings revealed that, 1) there is considerable and continuous rise of EAE research activities around different parts of the globe in the last five years, particularly Asia, Middle-East, and European countries; 2) EAE research has been very limited among African countries which need to be addressed due its role on business intelligence as well as semantic values; 3) three research facets were highlighted based on this study, i.e. solution research, validation research, and evaluation research, in which solution research gets the highest attention; and finally 4) the EAE challenges, as well as feasible future recommendations, were highlighted in this study.

Even though information and communication technology (ICT) is essential for everyday life and has gained considerable attention in education and other sectors, it also carries individual differences in its use and relevant skills. This systematic review aims to examine the gender differences in ICT use and skills for learning through technology. A comprehensive search of eight journal databases and a specific selection criterion was carried out to exclude articles that match our stated exclusion criteria. We included 42 peer-reviewed empirical publications and conference proceedings published between 2006 and 2020. For a subsample of studies, we performed a small-scale meta-analysis to quantify possible gender differences in ICT use and skills. A random-effects model uncovered a small and positive, yet not significant, effect size in favor of boys (g = 0.17, 95% CI [-0.01, 0.36]). However, this finding needs to be further backed by large-scale meta-analyses, including more study samples and a broader set of ICT use and skills measures. We highlight several concerns that should be addressed and more thoroughly in collaboration with one another to better IT skills and inspire new policies to increase the quality of ICT use. The findings from this review further suggest implications and present existing research challenges and point to future research directions.

Aspect based sentiment analysis (ABSA) is regards as the most demanding area of research in the field of text mining and natural language processing. In the last decade, people tend to be more interested in consulting online media outlets to make decisions concerning products or events. However, the existing ABSA approaches alone are not sufficient in providing comprehensive information required to make an informed decision. Most of the early studies focused on the binary classification of “positive” and “negative” to predict an overall sentiment of a review, which reduced the effectiveness in decision making towards the entities. Even though earlier studies on SA are centered on aspect extraction, there are limited works that effectively combine aspect extraction with emotional affects such as surprise, sadness and happiness, which consequently leads to incomplete meaning of a content. In this paper, we investigate a problem involving aspects in relation to emotional affects. To propose a novel supervised approach for the aspect extraction with their associated emotion classes in sentiment analysis (SA), we conducted an analysis of some prominent ABSA approaches. This research work will lead to a more accurate results of SA and could assist both novice and prominent researchers in comprehending the relationship between aspects and emotions in SA considering its sematic role in business intelligence.

Chatbot is an AI based application that stimulates human conversations. In this research, we proposed a noble approach to the design of Chatbot that interacts with people in Hausa Language, which is the second most famous languages in Africa. Numerous chatbots have been created and continue to be created. To the best of our knowledge, there is very limited or no Chatbot that target Hausa Language, hence the catalyst of this research. The proposed Chatbot is capable of communicating with users in Hausa Language, tell some funny stories and above all has the ability of learning from users. It was designed as a web-based application that can feature in any platform with internet access and a web browser, where PHP was used as programming Language in conjunction with MySQL database. The interface was designed using HTML and CSS with JavaScript for interactivity. Developing this Chatbot system would bring the Hausa language into the Artificial intelligence stage and it would serve as a reference material for future research and development. The result shows that the system is capable of communicating in simple Hausa words and will be able to learn simple words in Hausa. Developing system whose ability is to learn, adapt and perform autonomously can be a major field of competition between technology inventors and Hausa-based language processing over the next few years.

A sentiment analysis of Arabic texts is an important task in many commercial applications such as Twitter. This study introduces a multi-criteria method to empirically assess and rank classifiers for Arabic sentiment analysis. Prominent machine learning algorithms were deployed to build classification models for Arabic sentiment analysis classifiers. Moreover, an assessment of the top five machine learning classifiers’ performances measures was discussed to rank the performance of the classifier. We integrated the top five ranking methods with evaluation metrics of machine learning classifiers such as accuracy, recall, precision, F-measure, CPU Time, classification error, and area under the curve (AUC). The method was tested using Saudi Arabic product reviews to compare five popular classifiers. Our results suggest that deep learning and support vector machine (SVM) classifiers perform best with accuracy 85.25%, 82.30%, precision 85.30, 83.87%, recall 88.41%, 83.89, F-measure 86.81, 83.87%, classification error 14.75, 17.70, and AUC 0.93, 0.90, respectively. They outperform decision trees, K-nearest neighbours (K-NN), and Naïve Bayes classifiers.

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amount of energy, especially during the training phase. The transmission of big data between service providers, users and data centres emits carbon dioxide as a result of high power consumption. This chapter proposes a theoretical framework for big data analytics using computational intelligent algorithms that has the potential to reduce energy consumption and enhance performance. We suggest that researchers should focus more attention on the issue of energy within big data analytics in relation to computational intelligent algorithms, before this becomes a widespread and urgent problem.

The Flower Pollination Algorithm (FPA) is a novel bio-inspired optimization algorithm that mimics the real life processes of the flower pollination. In this paper, we review the applications of the Single Flower Pollination Algorithm (SFPA), Multi-objective Flower Pollination Algorithm an extension of the SFPA and the Hybrid of FPA with other bio-inspired algorithms. The review has shown that there is still a room for the extension of the FPA to Binary FPA. The review presented in this paper can inspire researchers in the bio-inspired algorithms research community to further improve the effectiveness of the PFA as well as to apply the algorithm in other domains for solving real life, complex and nonlinear optimization problems in engineering and industry. Further research and open questions were highlighted in the paper.

The rapid increase in using non-standard words (NSWs) in communication through the social media is causing difficulties in understanding contents of the text messages. In addition, it affects the performance of several natural language processing (NLP) task such as machine translation, information retrievals, summarization and etc. In this study, we present an automatic text normalization system on Facebook chatting based on Hausa language. The proposed algorithm manually developed dictionary that employ normalization of each non-standard word with its equivalent standard word. This is accomplished through modification of the technique employed by [1] to fit Hausa NSWs' formation. It was found that our proposed algorithm was able to normalized Hausa NSWs with an accuracy of 100%The results of this research can facilitate comprehensive communication via Facebook using Hausa language.

This paper utilize modular neural network for prediction of possible emergencies locations during hajj pilgrimage. Available location, localization and positioning determination systems become increasingly important for use in day-to-day activities. These systems dwells on various scientific tools which ensure that the systems will provide accurate response to the needed service at the right time. Unfortunately, some tools were faced with drawbacks, either their use was not appropriate or they do not give reliable results, or the results obtained in certain scenario might not be apply to other scenarios. For this reasons, we utilize modular neural network tool to examine the analysis of determining possible emergencies locations within point of Interest of Hajj Pilgrimage in Meccah Saudi Arabia. The prediction results are generated by the use of longitude, latitude and distances as the dataset. Modular neural network takes longitude and latitude as inputs and predict distances within pilgrim’s possible point of interest. The learning systems were trained on the collected data. Experimental investigation demonstrated that modular network produce higher prediction accuracy compaired to other tools. This finding would contribute to the design of add-on applications which will deem to provide location based services for possible emergencies locations.