Fuzzy Logic Approach for Routing in Internet of Things Network

A performance of network is evaluated by considering different parameters. The network lifetime depends on many factors Residual energy, Link lifetime and Delay. The Major Challenge in IoT is to the increased lifetime of low power and lossy network (RPL).The process considering input and output to evaluate Network performance by considering the above factors. The proposed system makes use of FIS (Fuzzy Inference System) for selecting the best path to maximize network lifetime. The outcome obtained by using MATLAB and Network performance is increased. The excellent route is selected if Residual Energy is 194, Link quality is 51.2 and Delay is 1.05 then excellent route quality is 73.4%

SISTEM PENDUKUNG KEPUTUSAN KELOMPOK MENGGUNAKAN KOMBINASI METODE MOORA DAN COPELAND SCORE UNTUK PEMILIHAN VENDOR

Proses pengambilan keputusan pemilihan vendor memiliki cakupan luas dan tingkat kompleksitas tinggi, hal tersebut disebabkan keterlibatan berbagai pihak pengambil keputusan yang memiliki preferensi masing-masing. Konsekuesinya proses pemilihan vendor harus dilakukan secara objektif dan transparan, untuk mendapatkan efektivitas proses dan meminimalisir terjadinya kolusi. Dalam penelitian ini, diusulkan penggunaan konsep Group Decision Support System (GDSS) untuk menentukan vendor terbaik berdasarkan agregasi preferensi masing-masing pihak pengambil keputusan. Konsep GDSS yang diusulkan adalah mengkombinasikan metode Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) dengan metode Copeland Score. Metode MOORA digunakan sebagai metode perangkingan berdasarkan kriteria dan rasio bobot masing-masing pihak pengambil keputusan. Hasil perangkingan menggunakan metode MOORA pada masing-masing pihak pengambil keputusan selanjutnya diagreasikan menggunakan metode Copeland Score, untuk mendapatkan rangking final vendor. Hasil analisis user acceptance test mengindikasikan konsep GDSS yang diusulkan dapat menjembatani permasalahan objektivitas pemilihan vendor, yaitu seluruh responden memberikan respons setuju terhadap pernyataan objektivitas yang membandingkan dengan sistem terdahulu. Analisis sensitivitas pada kombinasi metode MOORA dan Copeland Score juga memberikan hasil yang searah yaitu memiliki nilai sensitivitas yang rendah (9,09%). Kata kunci : GDSS, MOORA, Copeland Score, Pemilihan Vendor The vendor selection decision making process has a wide scope and a high level of complexity, this is due to the involvement of various decision makers who have their respective preferences. Consequently the vendor selection process must be carried out objectively and transparently, to get the effectiveness of the process and minimize collusion. In this research, it is proposed to use the concept of Group Decision Support System (GDSS) to determine the best vendor based on the aggregation of preferences of each decision maker. The proposed GDSS concept is to combine the Multi-Objective Optimization method on the basis of Ratio Analysis (MOORA) with the Copeland Score method. The MOORA method is used as a ranking method based on the criteria and weight ratio of each decision maker. The results of ranking using the MOORA method on each decision-making party are then aggregated using the Copeland Score method, to get the final vendor ranking. The results of the user acceptance test analysis indicate the proposed GDSS concept can bridge the objectivity problem of vendor selection, that is all respondents respond agree with the objectivity statement which comparing with the previous system. Sensitivity analysis on the combination of the MOORA method and the Copeland Score also gives unidirectional results which have a low sensitivity value (9.09%). Keywords : GDSS, MOORA, Copeland Score, Vendor Selectio

Discrete optimizations’ problems of deliveries of heterogeneous products

The article touches upon issues related to the tasks of the national project «Smart City». The paper analyzes the problem of supplying a conditional consumer with heterogeneous products in accordance with his demand for deliveries from several suppliers in a situation with fixed surcharges, in addition to the cost of purchasing each unit of production. For the situation under study, a model of a reduced transport type with a discontinuous piecewise linear objective function of minimized total costs and with a system of linear constraints is constructed. A method for finding the optimal solution one of the many such solutions, based on the ideas of the Hungarian algorithm is proposed, the justification of which is given on the basis of the corresponding lemma. A refinement of the method is presented in the presence of some restrictions related to suppliers. The polynomial complexity of the method, i.e. the problem is quickly solvable, and the significant limitation of the applicability of the method within the framework of the model are noted. Further possible research directions of a stochastic or fuzzy nature are indicated

Melanoma classification using deep transfer learning

Melanoma is the most lethal type of skin cancer, despite the fact that individuals who are discovered early have a decent chance of recovering. A few creators have looked at various strategies to deal with programmed location and conclusion using design recognition and AI technology. Anticipating an infection so that it does not spread It is often helpful when doctors can diagnose an illness early on and spread throughout the body. Early disease detection is quite difficult due to the small number of screening populations. Whatever the case, it will take time to determine if it is harmless or hazardous. Assume the afflicted person sees a critical specialist for analysis, unaware that the critical specialist's knowledge has resulted in a cancerous development. This is where AI and deep learning technologies become a vital component of an effective mechanised determination framework, which might help doctors forecast infections much more swiftly and even ordinary people analyse a sickness. Our study endeavour addresses the issues of increased clinical expenditures associated with discovery, lower Precision in recognition and the manual discovery framework's mobility. System for Detecting Malignant Growths in Melanoma is a deep learning-based predictive model that leverages thermoscope pictures

GIS based Traffic Accident Analysis System

In Malaysia, every year over thousands human beings die and tens of thousands are injured in road accidents. This paper focused on the goal of developing tools and methodologies to reduce accidents, and to make roadway safer, through the ability to better interpret accident records and to provide more information for individuals to evaluate accidents. It founds that the customization of GIS application for Traffic accidents analysis could be performed using Map Object and visual basic 6.0. This integration produced expert system provides wide range functions in low cost programming

Referencial para a caracterização de websites de hotéis de acordo com as necessidades dos consumidores

Online presence is essential for tourism organisations, and the quality of websites can influence customers. In the case of hotels, there are many studies to evaluate website performance based on functionality, usability and other factors, much less on the amount of different information available to the consumer. In the near future by using Big Data it is expected that hotel websites will be dynamic, they will adapt themselves on-the-fly, showing personalized information to each consumer. Different consumers will have different websites (information? available) from the same hotel. This paper presents a framework for the characterisation of hotel websites, focusing on the amount of information available to the consumer in each website, which was applied in a case study during the last months of 2013 to the websites of five-star hotels that operate in the tourist region of the Algarve, Portugal. The framework allowed to identify a set of exhaustive indicators for hotel website characterisation, which were then grouped into ten fundamental information dimensions. These dimensions further fell into four dimension groups. Finally, it is presented and discussed quantitative and qualitative evaluations, that illustrates which indicators and dimensions are more often considered on hotel websites to satisfy the consumer?s information needs