SISTEM PAKAR DIAGNOSA PENYAKIT INFEKSI PADA SALURAN PERNAPASAN MENGGUNAKAN METODE BACKWARD CHAINING DAN PROBABILITAS KLASIK

Infeksi saluran pernapasan atau respiratory tract infections adalah infeksi yang menyerang saluran pernapasan manusia. Infeksi ini disebabkan oleh bakteri atau virus. Dalam upaya membantu masyarakat (pasien) mendiagnosa penyakit infeksi pada saluran pernapasan sejak dini diperlukan sebuah sistem pakar yang mampu mendiagnosa penyakit infeksi pada saluran pernapasan sebelum dilakukan diagnosa lebih lanjut di rumah sakit. Sistem pakar diagnosa  penyakit infeksi pada saluran pernapasan di bangun dengan menggunakan mesin inferensi Backward Chaining yang dikombinasikan dengan metode probabilitas klasik dengan harapan dapat disajikan persentase kemungkinan penyakit pasien berdasarkan hipotesa pasien. Hasil dari penelitian ini berupa aplikasi (perangkat lunak) sistem pakar, berdasarkan uji coba yang telah dilakukan penelusuran inferensi runut balik (Backward Chaining) lebih efektif dikombinasikan dengan metode probabilitas klasik, dengan demikian dapat disajikan persentase kemungkinan kebenaran penyakit sesuai hipotesa pasien berdasarkan gejala-gejala yang dirasakan

Performance Analysis of ANN on Dataset Allocations for Pattern Recognition of Bivariate Process

Several approaches to identifying the out-of-control variables after the detection of abnormal pattern has been most intensively studied and used in practice. One of the several approaches is the Artificial Neural Network (ANN) based model for diagnosis of out-of-control signal of multivariate process mean shift. In spite of the number of years of research in neural network, limited research (if any) have been done on the effect of dataset allocations in percentages for training and testing on the performance of ANN. In this paper, we investigate the use of different percentages of dataset allocation into training, validation and testing on the performance of ANN in pattern recognition of bivariate process using six selected training algorithms. The result of study showed that large allocation of dataset for training was found suitable, having higher recognition accuracy for ANN learning and perform better for pattern recognition of bivariate process. Keywords: Bivariate Process; Pattern Recognition; Recognition accuracy; Multivariate quality control charts, training algorith

Predicting Completion Time for Production Line in a Supply Chain System through Artificial Neural Networks

Completion time in manufacturing sector is the time needed to produce a product through production processes in sequence and it reflects the delivery performance of such company in supply chain system to meet customer demands on time. However, actual completion time always deviated from the standard completion time due to unavoidable factors and consequently affect delivery due date and ultimately lead to customer dissatisfaction. Therefore, this paper predicts completion time based on historical data of production line activities and discovers the most influential factor that contributes to the tardiness or a late jobs due date from its completion time. A well-known company in producing audio speaker is selected as a case company. Based on the review of previous works, it is found that Artificial Neural Networks (ANN) has superior capability in prediction of future occurrence by capturing the underlying relationship among variables through historical data. Besides, ANN is also capable to provide final weight for each of related variable. Variable with the highest value of final weight indicates the most influential variable and should be concerned more to solve completion time issue which has persisted among entities in supply chain system. The obtained result is expected to become an advantageous guidance for every entity in supply chain system to fulfil completion time requirement as requested by customer in order to survive in this turbulent market place

Decision problem structuring method for the specification and selection of active fire protection systems

The UK along with the EU has witnessed a recent proliferation of designs for potential active fire suppression systems for the mitigation of fire risks in buildings and equipment; from five in 1986 (BSI, 1986) to eleven in 2011 (BSI, 2011a). However, each technology remains limited to the protection of certain types of application only, rather than offering a solution to guard against all possible hazards. This trend occurs at the same time as a transition from prescriptive to performance based standards and against the backdrop of the current nonprescriptive regulatory frameworks including the Building Regulations (HMSO, 2010), The Regulatory (fire) Reform Order (HMSO, 2005) and associated guidance (Approved Documents, standards, codes of practice and guides). Hazards can be difficult to assess and describe and the inequality or absence of satisfactory methods is notable in many recently published guidance documents. Active fire protection systems are installed to meet legislative requirements (to protect life), and / or when identified as appropriate by a cost-benefit analysis (e.g. to achieve risk reduction for business resilience purposes or to historic assets). There are many guidance documents available to assist users and designers in choosing and specifying appropriate active fire protection. These documents vary in age, relevance, scope, quality, impartiality and suitability. The Fire Protection Association (FPA) and several leading insurers who participate in its risk management work, have identified the requirement for assistance with the decision making process of analysing fire hazards and matching them to appropriate candidate systems, in order to make informed and impartial recommendations. This has led to the undertaking of a four year research project aimed at developing a decision problem structuring method and a software tool (Expert System), for the specification and selection of Active Fire Protection Systems. The research aim is to develop a tool that will assist users in making an informed selection of a system that is likely to best suit their needs and thereby contribute to overall improvements in fire safety and outcomes. This paper presents a summary of the work to date, focusing on the demand for the work, development of the methodology and practical application of the emerging Expert System

ELEKTRONİK CİHAZLARDA ARIZA TEŞHİSİ İÇİN BİR UZMAN SİSTEM UYGULAMASI

Bir cihaz, bir yapı veya bir sistem, kendisinden beklenen fonksiyonları yerine getiremediği durumlarda birtakım arızalara yol açmaktadır. Bu arızaların bakım onarım faaliyetleri de; ilgili kurum, kuruluş veya şirketlerde bazı kayıplara (zaman, finansal vb.) sebep olmaktadır. Geliştirilen bir uzman sistem aracılığıyla etkili bir arıza teşhis sistemi kullanılarak bir makinenin herhangi bir yerindeki arızası kolayca belirlenebilir, belirlenen arıza ile ilgili hızlı ve doğru birtakım değerlendirmeler yapılabilir. Bu çalışmada, girdi ekranından yapılan tercihleri yorumlayarak arızalı cihazın tamiri konusunda önerilerde bulunan bir uzman sistem geliştirilmiştir. Geliştirilen sistem, yorumlama işleminde bilgi tabanını kullanmaktadır. Geliştirilen sistem tarafından kullanılan kural tabanı arızalı cihazların katalog bilgilerinden, servis notlarından ve “uzman” yetkili servislerin tecrübelerinden toplanarak oluşturulmuştur. Geliştirilen sistem, NetBeans 6.9.1 ortamında JAVA programlama dili kullanılarak hazırlanmıştır

Development of a fixed firefighting system selection tool for improved outcomes

The UK along with the European Union has experienced a recent proliferation in design approaches for potential fixed firefighting systems. Such systems are installed to mitigate fire hazards in buildings and equipment. In the UK, for example there were five general design approaches to fixed firefighting systems protection in 1986. This had increased to eleven in 2011. This is against the backdrop of the current non-prescriptive regulatory frameworks including the Building Regulations, the repeal of so-called ‘local acts’, the Regulatory (fire) Reform Order and associated guidance (Approved Documents, standards, codes of practice and guides). In response to this trend, as was intended, the market place is becoming increasingly competitive. However, the capability of each technology remains limited to protection against certain hazards, rather than offering a solution to guard against all possible scenarios. When selecting a fixed firefighting system, fire hazards and interactions can be difficult to assess and describe and the inequality or absence of satisfactory methods is notable in many recently published guidance documents. The absence of good quality guidance for non-expert practitioners (specifiers) and regulatory changes means a good quality source of impartial and expert knowledge is increasingly desirable. The challenge is to amass this knowledge and render it in an accessible format to the non-expert user. This paper reports on progress to-date; understanding the problem, amassing and structuring the knowledge base and developing a suitable knowledge management tool

Predicting Completion Time for Production Line in a Supply Chain System through Artificial Neural Networks

Completion time in manufacturing sector is the time needed to produce a product through production processes in sequence and it reflects the delivery performance of such company in supply chain system to meet customer demands on time. However, actual completion time always deviated from the standard completion time due to unavoidable factors and consequently affect delivery due date and ultimately lead to customer dissatisfaction. Besides, it is found that little attention has been given in analysing completion time at production line from previous literatures. Therefore, this paper fill the knowledge gap by predicting completion time based on historical data of production line activities and discovers the most influential factor that contributes to the tardiness or a late job’s due date from its completion time. A wellknown company in producing audio speaker is selected as a case company. Based on the review of previous works, it is found that Artificial Neural Networks (ANN) has superior capability in prediction of future occurrence by capturing the underlying relationship among variables through historical data. Besides, ANN is also capable to provide final weight for each of related variable. Variable with the highest value of final weight indicates the most influential variable and should be concerned more to solve completion time issue which has persisted among entities in supply chain system. The obtained result is expected to become an advantageous guidance for every entity in supply chain system to fulfil completion time requirement as requested by customer in order to survive in this turbulent market plac

Η τεχνολογία της ανάλυσης δεδομένων στη λογιστική και ελεγκτική

Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2018.Η παρούσα εργασία έχει ως σκοπό την παρουσίαση των νέων τεχνολογιών στην Λογιστική και Ελεγκτική. Αρχικά, πραγματοποιείται μια ιστορική ανασκόπηση του επαγγέλματος του Ορκωτού Ελεγκτή Λογιστή στην παγκόσμια οικονομία από τα αρχαία χρόνια μέχρι και σήμερα. Επίσης, γίνεται αναφορά στο επάγγελμα Ορκωτού Ελεγκτή Λογιστή στην Ελλάδα και πραγματοποιείται μια προσπάθεια καταγραφής των αλλαγών από την παραδοσιακή μορφή του ελέγχου μέχρι τις μελλοντικές τεχνολογικές εξελίξεις στον έλεγχο των χρηματοοικονομικών καταστάσεων. Στη συνέχεια, παρουσιάζεται λεπτομερώς η τεχνολογία της ανάλυσης των δεδομένων (Data Analytics) στον έλεγχο των χρηματοοικονομικών καταστάσεων, οι εφαρμογές της και τις μελλοντικές ωφέλειες από την πλήρη υιοθέτησή της στα προγράμματα ελέγχου. Τέλος, γίνεται προσπάθεια περιεκτικής ανάλυση των κυριότερων τεχνολογιών που, σύμφωνα με τους αναλυτές, θα απασχολήσουν το επάγγελμα του Ορκωτού Ελεγκτή στο άμεσο μέλλον. Οι τεχνολογίες αυτές είναι η τεχνολογία Blockchain, η Τεχνητή Νοημοσύνη, η Αυτοματοποίηση Ρομποτικών Διαδικασιών και η τεχνολογία Βοηθού Τεχνητού Νευρωνικού Δικτύου