Pest activity prognosis in rice fields using fuzzy expert system approach

Logik kabur merupakan satu bentuk perwakilan pengetahuan bagi konsep yang tak dapat ditakrifkan secara tepat tetapi bergantung kepada konteks penggunaannya. Sistem Pakar adalah program komputer yang menggunakan pengetahuan manusia untuk menyelesaikan masalah khusus yang memerlukan kepintaran manusia. Oleh kerana pengetahuan yang terlibat dalam pengurusan serangga adalah tidak lengkap dan kabur, maka logik kabur diintegrasikan ke dalam sistem pakar untuk mengendalikan penaksiran anggaran. Sistem Pakar dan Logik Kabur mempunyai kelebihannya tersendiri dan gabungan kedua-dua teknologi yang membentuk sistem pakar-kabur dapat meningkatkan keupayaan sistem (Herrmann, 1996). Berdasarkan keupayaan logik kabur dan sistem pakar, peramalan aktiviti serangga di sawah padi menggunakan pendekatan pakar-kabur telah dibangunkan untuk menyediakan maklumat kepada petani dan penyelidik melalui Internet. Oleh kerana beras merupakan makanan ruji rakyat Malaysia dan Kedah merupakan kawasan utama penanaman padi di Malaysia, kajian ini memfokuskan kepada aktiviti serangga di sawah padi. Dalam MyPEST, jenis serangga yang mengakibatkan kerosakan pada tanaman padi ditentukan oleh sistem pakar, manakala Logik Kabur digunakan untuk meramalkan tahap aktiviti serangga. Ixii penting supaya rawatan awal dapat dilakukan sebelum kerosakan bertambah buruk. Sistem MyPEST membantu pengguna dengan mengendalikan rundingan pakar yang dikawalselia oleh sistem pakar dan logik kabur untuk peramalan dan menguruskan ketidakpastian data menggunakan pembolehubah lingistik. Sistem berasaskan web ini juga membantu petani dan institusi pertanian untuk menguruskan ladang dengan cekap dan dapat meningkatkan kualiti serta kuantiti beras yang dihasilkan. Dalam kajian ini, proses peramalan menggunakan lebih daripada satu attribut telah dikaji. Dapatan kajian menunjukkan sekiranya lebih daripada satu atribut terlibat, graf keputusan 3-dimensi yang kurang tegar dihasilkan. Penentuan jenis serangga adalah dalam fasa pertama MyPEST dan diikuti oleh peramalan aktiviti serangga yang dikenalpasti. Sistem ini telah disemak oleh pakar serangga di MARDi dan disahkan membawa manfaat kepada penyelidik di MARDI, MADA dan DOA khususnya dan petani secara keseluruhan

Satisficing-based formulation of fuzzy random multi-criteria programming models in production applications

制度:新 ; 報告番号:甲3648号 ; 学位の種類:博士(工学) ; 授与年月日:2012/5/21 ; 早大学位記番号:新6011 学位請求の論文名: ファジィランダム多目的計画モデルのサティスファイシングによる定式化と生産問題への応用Waseda Universit

User-Oriented Preference Toward a Recommender System

            في الوقت الحاضر، من الملائم لنا استخدام محرك بحث للحصول على المعلومات المطلوبة. لكن في بعض الأحيان يسيء فهم المعلومات بسبب التقارير الإعلامية المختلفة. نظام التوصية (RS) شائع الاستخدام في كل الأعمال لأنه يمكن أن يوفر معلومات للمستخدمين التي ستجذب المزيد من الإيرادات للشركات. ولكن أيضًا ، في بعض الأحيان ، يوصي النظام المستخدمين بالمعلومات غير الضرورية. لهذا السبب ، قدم هذا البحث بنية لنظام التوصية التي يمكن أن تستند إلى التفضيل الموجه للمستخدم. هذا النظام يسمى UOP-RS. لجعل UOP-RS بشكل كبير، ركزهذا البحث على معلومات السينما وتجميع قاعدة بيانات الأفلام من موقع IMDb الذي يوفر معلومات متعلقة بالأفلام والبرامج التلفزيونية ومقاطع الفيديو المنزلية وألعاب الفيديو والمحتوى المتدفق الذي يجمع أيضًا العديد من التقييمات والمراجعات من المستخدمين. حلل البحث أيضًا بيانات المستخدم الفردي لاستخراج ميزات المستخدم. بناءً على خصائص المستخدم ، وتقييمات / درجات الفيلم ، ونتائج الأفلام ، تم بناء نموذج UOP-RS. في تجربتنا ، تم استخدام 5000 مجموعة بيانات أفلام IMDb و 5 أفلام موصى بها للمستخدمين. تظهر النتائج أن النظام يمكنه إرجاع النتائج في 3.86 ثانية ولديه خطأ 14٪ على السلع الموصى بها عند تدريب البيانات على أنها K = 50. في نهاية هذه الورقة خلص إلى أن النظام يمكن أن يوصي بسرعة مستخدمي السلع التي يحتاجون إليها. سوف يمتد النظام المقترح للاتصال بنظام Chatbot بحيث يمكن للمستخدمين جعل الاستعلامات أسرع وأسهل من هواتفهم في المستقبل.            Nowadays, it is convenient for us to use a search engine to get our needed information. But sometimes it will misunderstand the information because of the different media reports. The Recommender System (RS) is popular to use for every business since it can provide information for users that will attract more revenues for companies. But also, sometimes the system will recommend unneeded information for users. Because of this, this paper provided an architecture of a recommender system that could base on user-oriented preference. This system is called UOP-RS. To make the UOP-RS significantly, this paper focused on movie theatre information and collect the movie database from the IMDb website that provides information related to movies, television programs, home videos, video games, and streaming content that also collects many ratings and reviews from users. This paper also analyzed individual user data to extract the user’s features. Based on user characteristics, movie ratings/scores, and movie results, a UOP-RS model was built. In our experiment, 5000 IMDb movie datasets were used and 5 recommended movies for users. The results show that the system could return results on 3.86 s and has a 14% error on recommended goods when training data as . At the end of this paper concluded that the system could quickly recommend users of the goods which they needed.  The proposed system will extend to connect with the Chatbot system that users can make queries faster and easier from their phones in the future

Soil Classification based on Machine Learning for Crop Suggestion

A system for classifying and arranging information about soil is known as soil classification. This category of soil was formed in response to a need for a simple, consistent, and easy-to-understand way to classify lands, which is especially important for plantation and agricultural decision-making. However, the current method of assessing soil type is time consuming and heavily relied on agricultural experts. The implementation of machine learning is expected for better soil classification to suggest the crop. The three algorithms are tested, which is Random Forest, Naïve Bayes, and k-Nearest Neighbor (k-NN). Classification techniques are being chosen as a data mining task to produce a classify model. Random Forest has the best accuracy (97.23 percent), Naïve Bayes has the second highest accuracy (96.82 percent), and k-Nearest Neighbor (k-NN) has the lowest accuracy (92.92 percent)

An expert system for pneumococcal prognosis

Threats and viruses are particularly alarming for children with low immunization levels. Pneumococcal disease is the world's most important cause of child death and has claimed many lives. Since awareness of the dangers of Pneumococcal viruses among parents is low in Malaysia, preventive measures such as vaccine intake cannot be done comprehensively. Hence, in order to communicate information about Pneumococcal disease, a pneumococcal disease diagnosis system for children is developed. This system employs expert system method and apply forward chaining technique for its reasoning. Knowledge base of the system is stored in the database for data management. This alternative system allows access to information as well as early diagnosis of early symptoms can be detected. This system is expected to benefit users in terms of knowledge sharing, and self-checking on their body condition, especially parents, to prevent any possible diseases that may infect children's

Estimation of confidence-interval for yearly electricity load consumption based on fuzzy random auto-regression model

Many models have been implemented in the energy sectors, especially in the electricity load consumption ranging from the statistical to the artificial intelligence models. However, most of these models do not consider the factors of uncertainty, the randomness and the probability of the time series data into the forecasting model. These factors give impact to the estimated model’s coefficients and also the forecasting accuracy. In this paper, the fuzzy random auto-regression model is suggested to solve three conditions above. The best confidence interval estimation and the forecasting accuracy are improved through adjusting of the left-right spreads of triangular fuzzy numbers. The yearly electricity load consumption of North-Taiwan from 1981 to 2000 are examined in evaluating the performance of three different left-right spreads of fuzzy random auto-regression models and some existing models, respectively. The result indicates that the smaller left-right spread of triangular fuzzy number provides the better forecast values if compared with based line models. Keywords: Fuzzy random variable, auto-regression model, left-right spread, triangular fuzzy number, forecasting error, electricity

Workplace safety risk assessment model based on fuzzy regression

Regulating safety and health in a workplace is crucial for any industry. It makes measuring a level of risk to characterize hazards in a workplace is a necessary. A systematic risk assessment in a workplace is capable to evaluate the level of risk which might occur. The assessment of risk in workplace regularly is performed by several identified attributes. At present, quantitative risk assessment uses crisp value in its evaluation. However, risk assessment process is exposed to uncertain information, due to human evaluation which uses linguistic value and is difficult to translate into precise numerical value. It makes the risk assessment process in workplace is imprecise. Thus, a robust fuzzy regression is introduced in this paper to determine the fuzzy weights of assessment attribute and build a robust fuzzy assessment model. This is important to identify the relationship among attributes, and helps the examiners to conduct a proper assessment in uncertain environment. A triangular fuzzy number is used to present the fuzzy judgment. An explanatory example is included to show the working procedure. The result indicates that the proposed model is beneficial to facilitate the decision model in evaluating risk, and specify excellent choice under the presence of uncertainty

An enhanced possibilistic programming model with fuzzy random confidence-interval for multi-objective problem

Mathematical models are established to represent real-world problems. Since the real-world faces various types of uncertainties, it makes mathematical model suffers with insufficient uncertainties modeling. The existing models lack of explanation in dealing uncertainties. In this paper, construction of mathematical model for decision making scenario with uncertainties is presented. Primarily, fuzzy random regression is applied to formulate a corresponding mathematical model from real application of a multi-objective problem. Then, a technique in possibilistic theory, known as modality optimization is used to solve the developed model. Consequently, the result shows that a well-defined multi-objective mathematical model is possible to be formulated for decision making problems with the uncertainty. Indeed, such problems with uncertainties can be solved efficiently with the presence of modality optimization