Technological advances have allowed to collect and store large volumes of data over the years. Besides, it is significant that today's applications have high performance and can analyze these large datasets effectively. Today, it remains a challenge for data mining to make its algorithms and applications equally efficient in the need of increasing data size and dimensionality [1]. To achieve this goal, many applications rely on parallelism, because it is an area that allows the reduction of cost depending on the execution time of the algorithms because it takes advantage of the characteristics of current computer architectures to run several processes concurrently [2]. This paper proposes a parallel version of the FuzzyPred algorithm based on the amount of data that can be processed within each of the processing threads, synchronously and independently.

本論文は，5章より構成されている。第1章では，機械学習の現状，応用及び構成を述べた上，本研究で扱った三つの課題を挙げた。第2章では，小サンプルデータの特徴選択方法を提案した。第3章では，クラスの不均衡性と学習データのサイズが分類器精度への影響を検討した。第4章では，ノイズが分類器の学習を妨げる問題点に対して，多要素ベースの学習に基づいた高速クラスノイズの検出方法を提案した。第5章では，分析の主な結果をまとめ，今後の課題と展望を述べた。 / This thesis is organized under five chapters. Chapter 1 gives a brief explanation of what machine learning is and why it matters. Chapter 2 makes a proposal to improve the performance of feature selection methods with low-sample-size data. Chapter 3 studies the effects of class imbalance and training data size on classifier learning empirically. Chapter 4 proposes a fast noise detector referring to the problems of noise detection algorithms, which are over-cleansing, large computational complexity and long response time. Chapter 5 draws a summary and the closing. / 博士(文化情報学) / Doctor of Culture and Information Science / 同志社大学 / Doshisha University

This thesis examines the energy efficiency of Advanced Encryption Standard (AES) encryption across various modes of operation (ECB, CBC, CFB, OFB, CTR, GCM, and CCM) on ARM Cortex-A53, Cortex-A72, and Cortex-A76 processors, using Raspberry Pi models 3, 4, and 5 as the experimental platforms. The study primarily investigates the impact of key lengths (128, 192, and 256 bits) and data sizes on energy consumption during encryption tasks. Using an experimental setup with the Raspberry Pi single-board computers, energy consumption was measured and analyzed through repeated encryption operations and data collection via a power meter interfaced with a database. The results reveal only modest increases in energy consumption with larger key lengths across all tested modes and data sizes, suggesting that while key length incrementally affects energy usage, the impact remains relatively minor, thus not significantly compromising energy efficiency for enhanced security. The analysis further shows that ECB mode consistently exhibits the lowest energy consumption, with CTR and CBC not far behind, followed by OFB and then CFB being the least effective among the traditional modes, with AEAD modes like GCM and CCM demanding substantially higher energy, reflecting their more complex processing requirements. Additionally, the study highlights the influence of data size on energy efficiency, showing a decrease in energy consumption per kilobyte with increasing file size up to a certain point, beyond which the benefits diminish. This thesis contributes to a deeper understanding of the trade-offs between security features and energy efficiency in AES encryption on ARM processors, offering insights into scenarios where energy consumption is a critical concern. The findings underscore the importance of selecting appropriate encryption modes and configurations based on the specific requirements and constraints of hardware environments aimed at optimizing energy efficiency in cryptographic operations. Future research could expand on a broader array of ARM-based devices to improve the biases from the Raspberry Pi boards and enhance the reliability of the conclusions drawn from the data.