Knowledge-Based Systems

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

On the basis of their use, the DSS has received positive feedback from the University's decision makers.Making use of Intelligent Decision Support Systems (IDSS) technologies suited to provide decision support in the higher education environments, by generating and presenting relevant information and knowledge which are helpful in taking the decision regarding admission management in higher education colleges or universities. The university decision makers' needs and the DSS components are identified with the help of
survey done.In this paper the components of a decision support system (DSS) for developing student admission policies in higher education institute or in the university and the architecture about DSS based on ERP are proposed followed by how intelligent DSS in conjunction with ERP helps to overcome the drawbacks , if ERP is used alone in higher education institutes

This article draws on both transaction cost economics and sociological theory to examine the factors that explain the choice of governance structure in individual alliances, approached each transaction as independent and ignores the role of interfírm trust that emerges from repeated alliances between the same. Support emerged for the transaction cost claim that alliances that encompass shared R&D are likely to be equity based. There is also evidence that repeated alliances between two partners less likely than other alliances to be organized using equity.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

Knowledge based expert systems have been using in different fields of life like medicine, process controlling etc. Similarly,these systems are also being used in agriculture field in which different tasks from irrigation to harvesting are included. Expert system assists people in the making of environmentally sound and economically viable farm management decisions. Expert system can also be used as a training tool in agriculture sector because of its structured way of deriving knowledge and its explanation facility. There are some core areas of agriculture for which such type of systems have been developed and these areas include Pest management, Weed controlling, Fertilization, Farm planning, Agricultural engineering, Crop production, Natural resources conservation, Disorder management, Site evaluation, Variety selection, Marketing and finance management.

8 th International Conference on Software Engineering (SOENG 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Software Engineering and Applications. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on understanding Modern software engineering concepts and establishing new collaborations in these areas. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

Robot control software endows robots with advanced capabilities for autonomous operation, such as navigation, object recognition or manipulation, in unstructured and dynamic environments. However, there is a steady need for more robust operation, where robots should perform complex tasks by reliably exploiting these novel capabilities. Mission-level resilience is re- quired in the presence of component faults through failure recovery. To address this challenge, a novel self-adaptation framework based on functional knowledge for augmented autonomy is presented. A metacontroller is integrated on top of the robot control system, and it uses an explicit run-time model of the robot’s controller and its mission to adapt to operational changes. The model is grounded on a functional ontology that relates the robot’s mission with the robot’s architecture, and it is generated during the robot’s development from its engineering models. Advantages are discussed from both theoretical and practical viewpoints. An application example in a real autonomous mobile robot is provided. In this example, the generic metacontroller uses the robot’s functional model to adapt the control architecture to recover from a sensor failure.

Childlessness among married couples is a rising problem in India. One of the major factors of childlessness is due to being infertile of either one or both of wife or husband. Infertility refers to the failure of a couple to become pregnant after one year of regular unprotected sexual intercourse. Infertility is a life crisis with invisible losses, and its consequences are manifold. This paper is intended to propose a rule-based inferencing of infertility diagnosis of women using Java Expert System Shell (JESS). Such a system is essential because the percentage of childlessness due to infertility is rising very high these days in India. This framework is aimed to enhance the existing tools used to identify and diagnose infertility problems in Women in the state of Manipur. We have implemented here the user interface component using Java, the knowledge base of the system using the Java Expert System Shell (JESS) and the Java IDE of Netbeans 7.0 while the database component is using SQL. The proposed framework can be used as guidelines for infertility diagnosis for women to assists the physicians with their daily practices and women who had infertility problems.

With the advent and rising usage of Internet of Things (IoT) ecosystems , there is a consequent, parallel rise in opportunities where technology can find its place to improve a number of human conditions. However, this is nothing new-we have been perfecting the usage of tools to aid our daily living throughout history. The true evolution lies in the interaction between us and the tools we create. Tools are now smart devices, yielding an opportunity where human-device interaction is giving us the very knowledge on how to improve that particular synthesis. From improving our fitness to detecting bradycardia and response of traumatic brain injury patient, we have come to a point where we are able to gain actionable insight into a lot of aspects of our health and condition. This creates a certain autonomy in understanding the unique make-up of every single person, in addition to yielding information that can be used by health practitioners to help in diagnosis, determination of medical approach and right recovery and follow-up methods. All of this supported by two major factors: IoT platforms and Big Data Analysis (BDA). This paper takes a deep dive into exemplary setup of IoT platform and BDA framework necessary to support the improvement of human condition. Our SmartLeg prosthetic device integrates advanced prosthetic and robotic technology with the state-of-the-art machine learning algorithms capable of adapting the working of the prosthesis to the optimal gait and power consumption patterns, which provide means to customize the device to a particular user. KEYWORDS human-machine interface, active robotic prosthesis, machine learning, big data analysis

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

The paper suggests the development of a knowledge-based system (KBS) to support web interface design via organizing somehow dispersed HCI-related knowledge and providing it on a context basis. The frame-based ontology was created with Protege-Frames editor and CLIPS inference engine to serve as the core for the KBS, producing web design guidelines per business goals of a web-development (e-commerce) project and its target user group. The more theoretical knowledge, such as HCI laws and principles, was also embraced, and tag-based classification was used in the ontology to relate the guidelines to the design context, thus enhancing the explanatory component of the KBS. The results of the experimental testing suggest that the website developed with KBS's support compares favorably with existing analogues from e-commerce industry, attaining task success rate of 85.9% and receiving mildly positive user subjective impression ratings. The proposed KBS may be used not only as an expert system or in training inexperienced IT specialists, but also by skilled web and interface designers to improve the quality of their work by more wholly considering the attributes and requirements of a target user group.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

In this paper is analyzed the design of a software platform concerning a case study of process engineering involving the simultaneous adoption of data digitation, Data Mining-DM-processing, and Augmented Reality-AR-. Specifically is discussed the platform design able to upgrade the Knowledge Base-KB-enabling production process optimizations in working sites. The KB is gained by following 'Frascati' research guidelines addressing the possible ways to achieve the Knowledge Gain-KG-. The technologies such as AR and data entry mobile app are tailored in order to apply innovative data mining algorithms. In the first part of the paper is commented the preliminary project specifications, besides, in the second part, are shown the use cases, the unified modeling language-UML-models, and the mobile app mockups enabling KG. The proposed work discusses preliminary results of an industry project.

Gamification has become popular as a behavior change strategy to increase the motivation and engagement of users in a health and wellness applications. Motivational affordances or gamification elements can help to foster intrinsic or extrinsic motivation for an activity as mundane as achieving fitness and wellness goals. Research indicates that there are many motivations among older adults for playing digital games and exergames to encourage physical activity (PA). However, very few specific gamification elements (motivational af-fordances) have been defined for technology artifacts pertinent to older adults' physical activity (PA). We designed Spirit50, a gamified PA technology app and conducted an expert evaluation using long form questionnaires and the Heuristics Evaluation for Gameful Design instrument. Content analysis and comparisons of expert ratings of the heuristics provided specific insights into motivational af-fordances for older adults' PA technology.

Due to the rapidly increasing need of human-robot interaction (HRI), more intelligent robots are in demand. However, the vast majority of robots can only follow strict instructions, which seriously restricts their flexibility and versatility. A critical fact that strongly negates the experience of HRI is that robots cannot understand human intentions. This study aims at improving the robotic intelligence by training it to understand human intentions. Different from previous studies that recognizing human intentions from distinctive actions, this paper introduces a method to predict human intentions before a single action is completed. The experiment of throwing a ball towards designated targets are conducted to verify the effectiveness of the method. The proposed deep learning based method proves the feasibility of applying convolutional neural networks (CNN) under a novel circumstance. Experiment results show that the proposed CNN-vote method out competes three traditional machine learning techniques. In current context, the CNN-vote predictor achieves the highest testing accuracy with relatively less data needed.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

This research was funded by, and implemented within, a UK national research organisation, while the author was the KE and Impact Evaluation Manager responsible for assessing impact on a £15m UK government-funded research programme.

This work was also submitted as the authors MBA dissertation which was accepted and awarded in 2009.

ABSTRACT: A methodology and methods ‘toolbox’ is proposed for conducting impact research and assessment from within research programmes. The emphasis is upon how to clarify and assess the wider impacts from the early stages onwards.

The approach explicitly acknowledges, and attempts to address, the known issues and challenges of impact assessment while drawing upon the resources and opportunities available to research managers and researchers within such programmes.

The study has two triangulating foundations. Firstly, it examines the academic literature to help identify and design the pilot methodology and methods. Secondly it trials these in a real programme to both test and improve them.

The study was conducted over one year within one leading UK research organisation, where two research programmes (and around 60 research projects of £15m total value) were being assessed for their impacts. The study draws upon actual impact management experience and reflection, prospective (ex ante) and retrospective (ex post) assessments, participatory contributions from leading researchers undergoing assessment, and consultation with external users. The findings of this study outline the pilot and its first iteration. Recommendations are made for improvement to the methodology and methods.

A subsequent follow-on bid to UK government incorporating the impacts identified from implementing this methodology, successfully led to a further £15m of government funding.

It is expected the pilot methodology and methods will be applicable to other organisations, conducting and funding such research programmes, required to research and assess the wider research impacts.

Although primary focus of implemetation trials was upon research in physical sciences, technology, high-tech engineering, computing/IT, and environment, the wider literature study, the methodology design and justification, the methods proposed and used, and the resulting general recommendations and lessons are expected to apply more broadly to many other research areas, where the impact is to be found more widely in the society, economy, government or public sector or within organizations, policies, professions or the public.

Interest in this work is expected from research programme managers, impact managers and assessors, management consultants advising in this area, and government funding managers, all seeking to embed a developing approach to understand, capture, and enhance wider impact.

"With the development of computing technology, mechanistic models are often employed to simulate processes in coastal environments. However, these predictive tools are inevitably highly specialized, involving certain assumptions and/or limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying theories. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and practitioners of a model. The recent advancements in artificial intelligence (AI) technologies are making it possible to integrate machine learning capabilities into numerical modeling systems in order to bridge the gaps and lessen the demands on human experts. The objective of this paper is to review the state-of-the-art in the integration of different AI technologies into coastal modeling. The algorithms and methods studied include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. More focus is given to knowledge-based systems, which have apparent advantages over the others in allowing more transparent transfers of knowledge in the use of models and in furnishing the intelligent manipulation of calibration parameters. Of course, the other AI methods also have their individual contributions towards accurate and reliable predictions of coastal processes. The integrated model might be very powerful, since the advantages of each technique can be combined.

Keywords: artificial intelligence; coastal modeling; knowledge-based systems"

3rd International Conference on Machine Learning & Trends (MLT 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & its Trends. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

10th International Conference on Soft Computing, Artificial Intelligence and Applications (SAI 2021) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.

Twitter users get the latest tweets of their followees on their timeline. However, they are often overwhelmed by the large number of tweets, which makes it difficult for them to find interesting information among them. In this work, we present an efficient semantic recommendation method that helps users filter the Twitter stream for interesting content. The foundation of this method is a knowledge graph (KG) that can represent all user topics of interest as a variety of concepts, objects, events, persons, entities, locations and the relations between them. Our method uses the KG and graph theory algorithms not yet applied in social network analysis in order to construct user interest profiles by retrieving semantic information from tweets. Next, it produces ranked tweet recommendations. In addition, we use the KG to calculate interest similarity between users, and we present a followee recommender based on the same underlying principles. An important advantage of our method is that it reduces the effects of problems such as over-recommendation and over-specialization. As another advantage, our method is not impaired by the limitations posed by Twitter on the availability of the user graph data. We implemented from scratch the best-known state-of-the-art approaches in order to compare with them and assess our method. Moreover, we evaluate the efficiency and runtime scalability of our method.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

In the nowadays financial instability and sharp competition that best describes the worldwide business environment, every company regardless its size is continuosusly interested to analyse its financial and accounting performance, the forthcoming threats and the overall enterprise standing as a manner for developing future strategies. The current illustrates with examples the opportunities offered by the intelligent technologies for the financial and accounting software:1) economic and financial analysis using specific ratios according to the Romanian accounting system and 2) comparative analysis using Romanian and international bankcrupcy prediction models which analyse and give further direction to a small or a medium-sized enterprise.

Traditional methods for explaining programs provide explanations by converting the code of the program or traces of its execution to English. While such methods can sometimes adequately explain program behavior, they typically cannot provide justifications for that behavior. That is, such systems cannot tell why what the system is doing is a reasonable thing to be doing. The problem is that the knowledge required to provide these justifications was used to produce the program but is itself not recorded as part of the code, and hence is unavailable.The XPLAIN system uses an automatic programmer to generate a consulting program by refinement from abstract goals. The automatic programmer uses a domain model, consisting of descriptive facts about the application domain, and a set of domain principles which prescribe behavior and drive the refinement process forward. By examining the refinement structure created by the automatic programmer, XPLAIN provides justifications of the code. XPLAIN has been used to re-implement major portions of a Digitalis Therapy Advisor and provides superior explanations of its behavior.

In the last twelve years, the number of web user increases, so intensely leading to intense advancement in web services which leads to enlargement the usage data at higher rates. The purpose of a recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. Recommender systems differ in the way they analyze these data sources to develop notions of congeniality between users and items which can be used to identify well-matched pairs. The recommender system technology intentions to help users in finding items that match their personal interests. It has a successful usage in e-commerce applications to deal with problems related to information overload proficiently. In this paper, we will extensively present a survey of six existing recommendation system. The Collaborative Filtering systems analyze historical interactions alone, while Content-Based Filtering systems are based on profile attributes, Hybrid Techniques attempt to combine both of these designs, Demographic Based Recommender systems aim to categorize the user based on personal attributes and make recommendations based on demographic classes, while Knowledge-Based Recommendation attempts to suggest objects based on inferences about a user's needs and preferences, and Utility-Based Recommender systems make recommendations based on the computation of the utility of each item for the user. In this paper, we have recognized 60 research papers on recommender systems, which were published between 1971 and 2014. Finally, few research papers had an influence on research paper recommender systems in practice. We also recognized a lack of authority and long term research interest in the field, 78% of the authors published no more than one paper on research paper recommender systems, and there was miniature cooperation among different co-author groups.

One of the objectives of human resource management (HRM) is to promote the satisfaction of both managers and employees regarding HRM practices. Since promoting satisfaction begins by assessing its current level, this study aims to analyse the challenges faced by industrial companies' managers within developing countries-such as Morocco-in terms of implementing human resource (HR) function practices. This paper presents then a practice-oriented study conducted using a qualitative method based on the content analysis approach. Thus, data collection was based mainly on semi-structured interviews to which 30 managers had participated. Indeed, this study aims to measure managers' satisfaction regarding the HR function while highlighting, at the same time, the practices used in motivation, documentation process, etc., and in terms of locating weaknesses and obstacles to better HRM. The validity of inquiry was tested and proven by two validation strategies: member checking and external audits strategies. The results found, revealed genuinely remarkable weaknesses. Indeed, it turned out to be a lack consistent in terms of the proper structuring of the HR function. In addition to the poor mastery and application of basic HRM practices, namely the primitivity of documentation processes, and the job-person inadequacy, especially in the scarcity of adoption of developed management methods such as the implementation of provisional management of jobs and skills, the establishment of an occupational health system, and maturity in terms of carrying out the tasks of the control system, or even as regards the weaknesses linked to the lack of active communication, to the clarity of the objectives targeted for all the members, and to the sharing of private information, in addition to those arising from the irrelevance of the training received by the staff. As for proposing solutions, the integration of IT tools could improve the performance of HR management processes. Indeed, information systems promote active communication, different storage data that develop the quality of the decision. They also facilitate managing administrative issues, communicating and applying procedures, programming relevant training and motivating employees. The study has important implications for HR professionals and strategic leaders that are especially interested in developing countries.

The advancement of the digital technology and the rise of the knowledge economy have facilitated a growing practice of smart work-working anywhere and anytime. This study approaches smart work as a form of the sharing economy, with a central concern on its spatial disruption to inform planning implication, based on a case study of Canberra, Australia. The analysis combines spatial clustering of smart workers at small community level with the practice and perception of smart work. The results suggest an emerging spatial disruption of smart work on both land use and space use, which implies a need for some new planning thinking for urban-suburban relationship, infrastructure provision, localised economic development, and spatial reconfiguration for communities and spaces. This study also suggests a cautious and critical approach to sustainability aspirations, which have in part elevated the recent enthusiasm in smart work and the broader sharing economy.

The main aim of this research paper is to develop a rule based knowledge database for legal expert system
for consumer protection act, a domain within the Indian legal system which is often in demand. A
knowledge database developed here will further help the legal expert system to determine type of case with
respect to the Indian Judicial System. In this paper a rule based knowledge database will be developed to
determine the type of the case. The main aim of the study is to build a prototype which will rule based in
nature. The rule based knowledge database development is the first phase in the development of
comprehensive rule based legal expert system for consumer protection act which will be of great help in
process of solving consumer related cases.

Development of software is a very crucial and complex process. IT professionals have been facing difficulties and risks for the development of software projects. This research focuses on the effectiveness of two data mining algorithms that are Apriori algorithm and FP-Tree Algorithm in Software Engineering domain. These two algorithms have shown the capability of generating association rules in software engineering data. Associations between software risk factors and risk mitigation have been found using these algorithms which is a unique idea. Both the algorithms have different methodologies but with the same goals in the form of rules generation. This research paper targeted three things. Tracing of these two algorithms applied to the given dataset, Comparison in terms of pros and cons of both the algorithms and the combination of the two, novel adaptive architecture in a clear way. Embedding data mining techniques in software engineering have shown spark and generates fruitful results.

org/aiaa/index Scope & Topics 12 th International Conference on Artificial Intelligence, Soft Computing and Applications (AIAA 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

Twitter is a rapidly growing microblogging platform that allows its users to send and read short messages, called tweets. Because of the fact that a user's timeline consists of the latest tweets of their followees (users that they are following), followee recommendation is a problem of significant importance. In this work, we propose a followee recommendation approach, which takes advantage of the increasing amount of available social data and specifically the semantic relatedness of topics that interest users. In order to accomplish this, we use a Topic Graph, containing a wide variety of topics that will be used for the recommendation process. Today knowledge graphs provide a solid basis for us to construct a full and reliable Topic Graph. Our approach takes advantage of the semantic information retrieved from users' tweets, in order to build an interest profile for each user. Then we use graph theory algorithms in order to calculate user interest similarity using the Topic Graph.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications. Topics of interest include, but are not limited to, the following:

Data available on the web is growing at an exponential rate, creating Knowledge or extracting information is of paramount importance. Information Retrieval (IR) plays a crucial role in Knowledge management as it helps us to find the relevant information from the existing data. This paper compares the performance of keyword-based retrieval and other architectural styles in information retrieval system with ontology-based retrieval on documents in regional language

The human brain can store countless folds of knowledge. Still, we cannot fully utilize a
single human brain to solve a specific problem. Knowledge-based systems (KBSs) are computer
programs specifically developed to perform problem solving like human experts. These systems
effectively expand efficiency and flawlessly solve problems in various fields. The structure of a
KBS can be divided into five standard components, which are described in detail. The component
used for storage is called a knowledgebase, while an inference engine is a software module that
processes the knowledge stored in the knowledgebase. Different tools, shells and programming
languages can be used to develop and utilize KBSs. The most popular development languages are
List Programming (Lisp), Prolog, Java Expert Systems Shell (JESS) and C Language Integrated
Production System (CLIPS). This paper highlights various known issues in the phases of the
development, deployment and maintenance of KBSs. Developers, as well as end users, may face
various issues in the life cycle of these systems. The key personalities involved in the development
and maintenance of KBSs are knowledge engineers and domain knowledge experts. Most issues are
related to the experts’ availability, their behavior and long-term cooperation

The International Journal of Software Engineering & Applications (IJSEA) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Software Engineering & Applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern software engineering concepts & establishing new collaborations in these areas.

Predicting the time to build software is a very complex task for software engineering managers. There are complex factors that can directly interfere with the productivity of the development team. Factors directly related to the complexity of the system to be developed drastically change the time necessary for the completion of the works with the software factories. This work proposes the use of a hybrid system based on artificial neural networks and fuzzy systems to assist in the construction of an expert system based on rules to support in the prediction of hours destined to the development of software according to the complexity of the elements present in the same. The set of fuzzy rules obtained by the system helps the management and control of software development by providing a base of interpretable estimates based on fuzzy rules. The model was submitted to tests on a real database, and its results were promissory in the construction of an aid mechanism in the predictability of the software construction.

Aspect extraction is one of the key tasks in sentiment analysis. In recent years, statistical models have been used for the task. However, such models without any domain knowledge often produce aspects that are not interpretable in applications. To tackle the issue, some knowledge-based topic models have been proposed, which allow the user to input some prior domain knowledge to generate coherent aspects. However, existing knowledge-based topic models have several major shortcomings, e.g., little work has been done to incorporate the cannot-link type of knowledge or to automatically adjust the number of topics based on domain knowledge. This paper proposes a more advanced topic model, called MC-LDA (LDA with m-set and c-set), to address these problems, which is based on an Extended generalized Pólya urn (E-GPU) model (which is also proposed in this paper). Experiments on real-life product reviews from a variety of domains show that MC-LDA outperforms the existing state-of-the-art models markedly.