Depicting Decision-Making: A Type-2 Fuzzy Logic Based Explainable Artificial Intelligence System for Goal-Driven Simulation in the Workforce Allocation Domain

With the recent development of artificial intelligence along with information and communications infrastructure, a new paradigm of online services is being developed. Whereas in the past a service system could only exchange information of the service provider at the request of the user, information can now be provided by automatically analyzing a particular need, even without a direct user request. This also holds for online platforms of used-vehicle sales. In the past, consumers needed to inconveniently determine and classify the quality of information through static data provided by service and information providers. As a result, this service field has been harmful to consumers owing to such problems as false sales, fraud, and exaggerated advertising. Despite significant efforts of platform providers, there are limited human resources for censoring the vast amounts of data uploaded by sellers. Therefore, in this study, an algorithm called YOLOv3+MSSIM Type 2 for automatically censoring the data of used-vehicle sales on an online platform was developed. To this end, an artificial intelligence system that can automatically analyze an object in a vehicle video uploaded by a seller, and an artificial intelligence system that can filter the vehicle-specific terms and profanity from the seller’s video presentation, were also developed. As a result of evaluating the developed system, the average execution speed of the proposed YOLOv3+MSSIM Type 2 algorithm was 78.6 ms faster than that of the pure YOLOv3 algorithm, and the average frame rate per second was improved by 40.22 fps. In addition, the average GPU utilization rate was improved by 23.05%, proving the efficiency.

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An artificial intelligence system for a complex electromagnetic field problem. I. Finite element calculations and fuzzy logic development

IEEE Transactions on Magnetics ◽

10.1109/20.737475 ◽

1999 ◽

Vol 35 (1) ◽

pp. 516-522 ◽

Cited By ~ 8

Author(s):

K.J. Satsios ◽

D.P. Labridis ◽

P.S. Dokopoulos

Keyword(s):

Artificial Intelligence ◽

Finite Element ◽

Fuzzy Logic ◽

Electromagnetic Field ◽

Artificial Intelligence System ◽

Field Problem ◽

Intelligence System ◽

Finite Element Calculations

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Metode Fuzzy Logic pada Sistem Pemantauan dan Pemberian Pakan Kucing Berbasis Smartphone

MIND Journal ◽

10.26760/mindjournal.v5i1.24-38 ◽

2021 ◽

Vol 5 (1) ◽

pp. 24-38

Author(s):

RACHMA APRILIYANI ◽

LISA KRISTIANA ◽

MIRA MUSRINI BARMAWI

Keyword(s):

Artificial Intelligence ◽

Fuzzy Logic ◽

Health Condition ◽

Health Conditions ◽

Raspberry Pi ◽

Artificial Intelligence System ◽

Motion Data ◽

Intelligence System ◽

Fuzzy Logic Method ◽

Weight Sensor

AbstrakKucing merupakan hewan peliharan yang perlu diberi pakan secara rutin oleh pemiliknya karena ada sebagian kucing yang mudah terserang penyakit dikarenakan pola makan yang tidak teratur. Untuk mengatasinya, penelitian ini mengusulkan membangun sistem kecerdasan buatan untuk memberi pakan kucing secara terjadwal serta mengetahui kondisi kesehatan kucing dengan memanfaatkan Raspberry Pi, sensor berat dan sensor gerak dengan masukan dari sensor yang telah terpasang di sekitar tempat pakan kucing. Data yang didapat berupa data pakan yang dihabiskan kucing dan data gerak kucing kemudian diterapkan metode fuzzy logic Sugeno untuk menentukan kondisi kesehatan kucing. Dari hasil penelitian didapatkan nilai status sebesar 71%, 76% dan 80% untuk masing-masing kucing. Nilai tersebut termasuk kedalam kategori kondisi kucing sehat sehingga tindakan yang dilakukan pemilik yaitu menjaga pola makan kucing. Pada smartphone ditampilkan informasi mengenai data pakan kucing, status, dan kondisi kesehatan kucing.Kata Kunci: kesehatan kucing, Raspberry Pi, kecerdasan buatan, fuzzy logic Sugeno, smartphone.AbstractCats is one kind of pets that need to be fed regularly by their owners because some cats are susceptible to disease due to their irregular diet. This research proposes to build an artificial intelligence system to provide cat food regularly and can determine the health condition of cats, this system was built using the Raspberry Pi, weight sensor and motion sensor with input from the sensors that has been installed around the cat feed area. The data obtained in the form of feed data consumed by cats and cat motion data then Sugeno fuzzy logic method is applied to determine the health condition of the cat. From the results, each cat has a status value of 71%, 76% and 80%. This status value is included in the category of a healthy cat condition so the action taken by owners is to maintain the cat's diet. The smartphone displays information about cat feed data, status and health conditions of cats.Keywords: cat health, Raspberry Pi, fuzzy logic Sugeno, artificial intelligence, smartphone.

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