Michal Koren

• Doctor of Philosophy
• Shenkar College of Engineering and Design

Quantum computing is revolutionizing various fields, including operations research and queueing theory. This study presents a quantum method for simulating single-server Markovian (M/M/1) queues, making quantum computing more accessible to researchers in operations research. We introduce a dynamic amplification approach that adapts to queue traffic...

The fashion industry is a significant contributor to environmental degradation, with its rapid turnover of trends leading to substantial waste. This underscores the necessity for precise forecasting methods to optimize production, procurement, lead time management, and inventory control. Traditionally, fashion forecasting relied on statistical anal...

Clustering techniques are convenient tools for preparing and organizing unstructured and unclassified data. Depending on the data, they can be used to prepare for an analysis or to gain insight. However, choosing a clustering technique can be challenging when dealing with high-dimensional datasets. Most often, application requirements and data dist...

Data is essential for an organization to develop and make decisions efficiently and effectively. Machine learning classification algorithms are used to categorize observations into classes. The Naive Bayes (NB) classifier is a classification algorithm based on the Bayes theorem and the assumption that all predictors are independent of one another....

Quantum computing is a new and exciting field with the potential to solve some of the world’s most challenging problems. Currently, with the rise of quantum computers, the main challenge is the creation of quantum algorithms (under the limitations of quantum physics) and making them accessible to scientists who are not physicists. This study presen...

A substantial portion of global quantum computing research has been conducted using quantum mechanics, which recently has been applied to quantum computers. However, the design of a quantum algorithm requires a comprehensive understanding of quantum mechanics and physical procedures. This work presents a quantum procedure for estimating information...

Machine learning algorithms may have difficulty processing datasets with missing values. Identifying and replacing missing values is necessary before modeling the prediction for missing data. However, studies have shown that uniformly compensating for missing values in a dataset is impossible, and no imputation technique fits all datasets. This stu...

In today's world, data is essential for enhancing an organization's development and decision‐making processes. Implementing artificial intelligence is necessary to analyse data and make meaningful recommendations. Machine learning distance classification methods are used to classify observations in various algorithms, such as K‐nearest neighbours (...

Academic institutions have existed for centuries. In most places in the world, classes begin at 8:00 a.m. and continue into the evening hours, usually ending no later than 10:00 p.m. Although online teaching and learning have existed for over a decade, during the COVID-19 pandemic, it was developed and expanded drastically. As a result, new opportu...

A significant part of global quantum computing research has been conducted based on quantum mechanics, which can now be used with quantum computers. However, designing a quantum algorithm requires a deep understanding of quantum mechanics and physics procedures. This work presents a generic quantum “black box” for entropy calculation. It does not d...

Inventory planning in fashion markets is highly challenging, owing to uncertain demand; yet, in making inventory decisions, retailers may be able to capitalise on high substitutability between products. This research develops single-period inventory-management models describing a market with two substitutable products, under stockout-based substitu...

Fashion is replaced every season and collections change rapidly, depending on certain events. There are only a few weeks between the fashion shows and the collections reaching their sale points. As the pattern of demand is seasonal, new items must be produced every season. Additionally, colours and patterns change rapidly, creating a need for produ...

An organization needs data to develop, maintain, and build intelligence systems. Creating recommendations based on AI procedures is critical when the data contains multiple features. However, reducing the number of features is a significant challenge to improve a model's accuracy and reduce multicollinearity when developing a descriptive or predict...

In the apparel market's dynamic environment, fashion firms aim to successfully forecast both the desirability of new collections and the volume of each item to be produced and released in the market under conditions of uncertainty. This research study proposes an inventory-management model developed for substitutable products (sold in several versi...

Anomaly detection is often used to identify and remove outliers in datasets. However, detecting and analyzing the pattern of outliers can contribute to future business decisions or increase the accuracy of a learning algorithm. Selecting the applicable outlier detection method for a dataset requires human intervention and analysis due to the challe...

Recommendation systems are one of the main applications of machine learning (ML) used across different industries. This paper presents a new automated machine learning (AutoML) method of providing recommendations by processing data sets using ML algorithms, targeting, and offering cluster recommendations for new observations and as a new decision s...

Handling big data is a challenging task, which, in some cases, may raise performance issues. How data are collected and stored has a high impact on performance, when, for example, some data analysis is needed, or when an organization needs to run some queries on the data. In this paper, we study the impact of the block size in a big data environmen...

The research proposes an inventory management model for clothing sold in various colors. The model aims at maximizing the profits of firms, while reducing the costs that result from excess capacity of production or, alternatively, from loss of potential revenues due to low demand while considering the demand dependencies across colors. If the inven...

Fashion is primarily based on adoption of trends by consumers in textiles, clothing, footwear, jewelry and art, inter alia. As fashion is based on human preferences, it is characterized by dynamic changes throughout seasons and years, short lifecycles, low predictability, high volatility of demand and impulse purchases. In the dynamic environment o...