KAROMA: Karonese Morphologycal Analyzer Based on Graph Theory

Karonese is a local language of Karo ethnics from north Sumatra, Indonesia. Karonese terms have unique phonology, which exhibits variations in spellings and pronunciations while retaining the same meaning and in time. A morphological analyzer is a very critical issue for the enhancement of Natural language Processing (NLP) research on local languages, as well as in Karonese. This work proposed a morphology analyzer of Karonese based on graph theory (KAROMA). With its unique phonology, the formation of the Karonese morphology analyzer uses a word-based morphology approach.  Karonese terms that exhibit variations in spellings and pronunciations while retaining the same meaning and in time are expressed in a completed graph. Thus, the set of completed graphs form the Karonese WordNet. Furthermore, the stemming and lemmatization mechanism for Karonese is checked in the WordNet. This study also provides two KAROMA evaluators; member checking-based and text similarity-based by modified cosine similarity. The KAROMA evaluation process involves synthetic sentences of Karonese to calculate its text similarity. As a result, KAROMA detects the uniqueness of Karonese terms and normalizes them. The performance of KAROMA is 99% based on member checking and 97.16% of text similarity-based. Of course, this success is part of the development of NLP research for Karonese, such as sentiment analysis, text summarization, et

Building Dynamic Fuzzy Regression Model based on Convex Hull Algorithm and Its Industrial Applications

The conventional regression model was widely used in various real applications for 50 years. Precise and accurate models for prediction are important, especially for decision making purposes. Fuzzy logic is an approach to computing based on the "degrees of truth" rather than the common "true or false" Boolean logic, on which a modern computer is based. In other words, it deals with reasoning that is approximate rather than fixed or exact. Nowadays, a huge number of transactions are produced with an enormous amount of raw data. Comparing with the statistic regression method, fuzzy regression model however, requires large computation time, especially to dynamic or "on-line" data processing, because it takes all fuzzy samples as data points. The main aim of this research is to propose an innovative and efficient approach by combining the convex hull approach with the fuzzy regression technique to deal with dynamic data processing. This approach enables the computation of huge data within a realistic time frame. Three new dynamic models of the convex hull-based fuzzy regression are proposed and applied in industrial applications including granular data and switching regression problem. Specifically, well-known techniques such as conventional regression model are thoroughly extended and generalized. Statistical analysis techniques have been widely studied to improve its capability and produce effective prediction or forecasting models. However, these conventional techniques are not suitable for possibilistic or vague data processing. Then, since early 2000, some previous research studies highlighted the combination (or hybrid) approach for enhancing the computational performance and increasing the quality of produced regression models. However, most of them focus on batch data processing, which is not applicable for dynamic data analysis processes. Recently, an incipient practice called Soft Computing (SC) technology helps models and classifiers exploit tolerance for imprecision and uncertainty. The main objective of this research is to specifically create and design an algorithm for producing fuzzy regression models for dynamic data processing. A mathematical geometry facility such as Beneath-Beyond algorithm has been chosen to be combined together as one of convex hull incremental algorithm. Hence, the computational complexity and overall processing time were evaluated and successfully shown their simultaneously decrease. Therefore, decision making processes will be more efficient and well-timed

凸包に基づく動的ファジイ回帰モデルの構築アルゴリズムとその産業応用

早大学位記番号:新7267早稲田大

Power spectrum: A detailed dataset on electric demand and environmental interplays

This dataset provides detailed electricity demand forecasting metrics for the Sharjah Electricity and Water Authority (SEWA) over 2020 and 2021. Data encompasses both hourly and daily demand patterns, enriched with specific environmental parameters such as temperature, humidity, and solar irradiance. Additionally, SEWA's unique load metrics and lagged demand values, representing previous hour demand, are included.Data was procured using advanced electrical load meters and standardized weather data acquisition systems. Preliminary and advanced data processing was conducted via Excel tool. This comprehensive dataset is invaluable for stakeholders in electricity provisioning and policy-making. Its granular detail makes it a pivotal resource for modelling and forecasting electricity demand, aiding in infrastructure planning, renewable energy considerations, and demand-side management. The potential applications span across academic, policy, and industry domains, rendering it a versatile tool for future electricity demand research

An Improved Flower Pollination Algorithm for Global and Local Optimization

Meta-heuristic algorithms have emerged as a powerful optimization tool for handling non-smooth complex optimization problems and also to address engineering and medical issues. However, the traditional methods face difficulty in tackling the multimodal non-linear optimization problems within the vast search space. In this paper, the Flower Pollination Algorithm has been improved using Dynamic switch probability to enhance the balance between exploitation and exploration for increasing its search ability, and the swap operator is used to diversify the population, which will increase the exploitation in getting the optimum solution. The performance of the improved algorithm has investigated on benchmark mathematical functions, and the results have been compared with the Standard Flower pollination Algorithm (SFPA), Genetic Algorithm, Bat Algorithm, Simulated annealing, Firefly Algorithm and Modified flower pollination algorithm. The ranking of the algorithms proves that our proposed algorithm IFPDSO has outperformed the above-discussed nature-inspired heuristic algorithms