KBS for Desktop PC Troubleshooting

Abstract: Background: In spite of the fact that computers continue to improve in speed and functions operation, they remain complex to use. Problems frequently happen, and it is hard to resolve or find solutions for them. This paper outlines the significance and feasibility of building a desktop PC problems diagnosis system. The system gathers problem symptoms from users’ desktops, rather than the user describes his/her problems to primary search engines. It automatically searches global databases of problem symptoms and solutions, and also allows ordinary users to contribute exact problem reports in a structured manner. Objectives: The main goal of this Knowledge Based System is to get the suitable problem desktop PC symptoms and the correct way to solve the errors. Methods: In this paper the design of the proposed Knowledge Based System which was produced to help users of desktop PC in knowing many of the problems and error such as : Power supply problems, CPU errors, RAM dumping error, hard disk errors and bad sectors and suddenly restarting PC. The proposed Knowledge Based System presents an overview about desktop PC hardware errors are given, the cause of fault are outlined and the solution to the problems whenever possible is given out. CLIPS Knowledge Based System language was used for designing and implementing the proposed expert system. Results: The proposed PC desktop troubleshooting Knowledge Based System was evaluated by IT students and they were satisfied with its performance

Lung Cancer Detection Using Artificial Neural Network

In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is “survey lung cancer”. Model evaluation showed that the ANN model is able to detect the absence or presence of lung cancer with 96.67 % accuracy

Case-based reasoning combined with statistics for diagnostics and prognosis

Many approaches used for diagnostics today are based on a precise model. This excludes diagnostics of many complex types of machinery that cannot be modelled and simulated easily or without great effort. Our aim is to show that by including human experience it is possible to diagnose complex machinery when there is no or limited models or simulations available. This also enables diagnostics in a dynamic application where conditions change and new cases are often added. In fact every new solved case increases the diagnostic power of the system. We present a number of successful projects where we have used feature extraction together with case-based reasoning to diagnose faults in industrial robots, welding, cutting machinery and we also present our latest project for diagnosing transmissions by combining Case-Based Reasoning (CBR) with statistics. We view the fault diagnosis process as three consecutive steps. In the first step, sensor fault signals from machines and/or input from human operators are collected. Then, the second step consists of extracting relevant fault features. In the final diagnosis/prognosis step, status and faults are identified and classified. We view prognosis as a special case of diagnosis where the prognosis module predicts a stream of future features

KBS for Diagnosing Pineapple Diseases

Abstract: Background: The pineapple (A nanas comosus) is a tropical plant with an edible multiple fruit consisting of coalesced berries, also called pineapples, and the most economically significant plant in the Bromeliaceae family. Pineapples may be cultivated from a crown cutting of the fruit, possibly flowering in five to ten months and fruiting in the following six months.[5][6] Pineapples do not ripen significantly after harvest. In 2016, Costa Rica, Brazil, and the Philippines accounted for nearly one-third of the world's production of pineapples.[8] Pineapple damage is not taken quickly, it can lead to damage in the Pineapple. Objectives: The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment. Methods: In this paper the design of the proposed Expert System which was produced to help Fruits Agricultural Specialist in diagnosing many of the Pineapple diseases such as : Phytophthora heart (top) rot, Base (butt) rot or Fruit let core rot (green eye, Pineapple Sprain, Turf Toe, Pineapple disease , Plantar Fasciitis, Warts, Bunion, Rheumatoid Arthritis, Gout, Heel Spur, Athlete's Pineapple , The proposed expert system presents an overview about Pineapple diseases are given, the cause of diseases are outlined and the treatment of disease whenever possible is given out. CLIPS Expert System language was used for designing and implementing the proposed expert system. Results: The proposed Pineapple diseases diagnosis expert system was evaluated by Agricultural students and they were satisfied with its performance. Conclusions: The Proposed expert system is very useful for Fruits Agricultural Specialist, patients with Pineapple problem and newly graduated Agricultural Specialist

Assessment Of The Level Of Financial Security Of Machine-building Enterprises Of Zaporizhzhia Region With The Help Of Integral-rating Method

In the deepening of the financial crisis, enterprises are trying to implement an effective anti-crisis policy. The financial system as a source of accumulation of modern crisis phenomena, which are spreading further to all spheres of the economy, generally require the formation of new instruments to ensure their financial and economic security. The constant increase in the riskiness of economic activity may entail a significant decrease in the level of financial stability and solvency of the enterprise, and in the future lead to bankruptcy, and requires each business entity to create a perfect system of financial and economic security. To assess the financial security situation at specific enterprises, not all financial and economic indicators that are used can be applied, but only those that most closely meet the requirements and most reflect the results of financial and economic activity, and, if possible, do not contradict each other. The modern business environment highlights the issues of ensuring financial and economic security. Proper assessment of it, as well as diagnostics of the financial condition of enterprises is a priority task for ensuring sustainable development and operation of the enterprise. Market conditions require enterprises to improve production efficiency, competitiveness of products and services based on the implementation of scientific and technological progress, effective forms of management and enterprise management, enterprise activation and so on. So, the role of diagnostics of a financial condition is great enough. Diagnostics of the financial security of an enterprise is an analytical assessment of all the functional components of safety from the standpoint of achieving the highest possible level of protection against internal and external threats, aimed at making effective management decisions and developing a financial security strategy. With the help of diagnostics of the company's financial security, the article will assess the level of financial security of machine-building enterprises in the Zaporizhzia region using the integrated-rating method

Self-tuning routine alarm analysis of vibration signals in steam turbine generators

This paper presents a self-tuning framework for knowledge-based diagnosis of routine alarms in steam turbine generators. The techniques provide a novel basis for initialising and updating time series feature extraction parameters used in the automated decision support of vibration events due to operational transients. The data-driven nature of the algorithms allows for machine specific characteristics of individual turbines to be learned and reasoned about. The paper provides a case study illustrating the routine alarm paradigm and the applicability of systems using such techniques

Onion RBS for Disorders Diagnosis and Treatment

Abstract: This research included the design of an initial expert system which helps farmers and specialists to diagnose and provide appropriate advice on onion plant diseases; furthermore, the management of knowledge used in the expert system was discussed. One of the key elements of this research was to find the appropriate language to diagnose the onion disease and the current situation in the knowledge base. Expert systems to be able to effectively implement the consultation, production rules were used to capture knowledge. The expert system was developed using CLIPS with the Delphi language interface. The expert system has produced good results in the analysis of onion disease cases that have been tested and enable the system to determine the correct diagnosis in all cases

Aided diagnosis of structural pathologies with an expert system

Sustainability and safety are social demands for long-life buildings. Suitable inspection and maintenance tasks on structural elements are needed for keeping buildings safely in service. Any malfunction that causes structural damage could be called pathology by analogy between structural engineering and medicine. Even the easiest evaluation tasks require expensive training periods that may be shortened with a suitable tool. This work presents an expert system (called Doctor House or DH) for diagnosing pathologies of structural elements in buildings. DH differs from other expert systems when it deals with uncertainty in a far easier but still useful way and it is capable of aiding during the initial survey 'in situ', when damage should be detected at a glance. DH is a powerful tool that represents complex knowledge gathered from bibliography and experts. Knowledge codification and uncertainty treatment are the main achievements presented. Finally, DH was tested and validated during real surveys.Peer ReviewedPostprint (author's final draft

ANN for Parkinson’s Disease Prediction

Parkinson's Disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Doctors do not know what causes it and finds difficulty in early diagnosing the presence of Parkinson’s disease. An artificial neural network system with back propagation algorithm is presented in this paper for helping doctors in identifying PD. Previous research with regards to predict the presence of the PD has shown accuracy rates up to 93% [1]; however, accuracy of prediction for small classes is reduced. The proposed design of the neural network system causes a significant increase of robustness. It is also has shown that networks recognition rates reached 100%