Volume 1, Issue 1 – June 2025
Open Access
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Utilizing Machine Learning Algorithms and SMOTE for Analyzing and Predicting Homicides
This study analyzes homicide data in the United States from 1980 to 2014 using machine learning techniques to predict crime resolution and classify victim gender. The dataset, obtained from the FBI Supplementary Homicide Report, contains 638,454 records. Data preprocessing involved cleaning, converting categorical features to numerical values, and addressing class imbalance using Synthetic Minority Oversampling Technique (SMOTE). Various classification algorithms were applied, including Decision Tree and Naïve Bayes. The results showed that the Decision Tree model achieved 95% accuracy in predicting crime resolution and 85% accuracy in classifying victim gender, while Naïve Bayes reached 92% accuracy in crime resolution prediction. The findings highlight the effectiveness of machine learning in crime pattern analysis and prediction, aiding law enforcement in making more informed investigative decisions.
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Role of Chat Gpt-4 As an Assistant for Teachers in School Education and Universities
Artificial intelligence (AI) and ChatGPT-4 have versatile applications in both school children’s education and university settings. Chat GPT-4 can be a valuable assistant for teachers in various ways. The model can utilize its comprehensive knowledge to provide additional information and concepts in diverse fields to help explain difficult topics. It can also provide extra exercises and questions to assist students in practicing and reinforcing their skills. With its predictive and linguistic generation capabilities, Chat GPT-4 can also offer review and editing for students’ essays and research papers, aiming to improve the quality of their writing and expression. It can also guide students in the research process and the collection of reliable sources. Furthermore, the model can provide individual support by answering students’ questions and guiding them through the learning process. It can also be used to create simulations and educational scenarios to enhance students’ understanding and application of theoretical concepts in realistic contexts. It is worth mentioning that the model relies on the inputs it receives, so the teacher needs to play an active role in guiding and clarifying the ideas and information provided by the model, as well as in evaluating and monitoring students’ progress.
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Mining Dynamic Profit Databases: Efficient Solutions for High Utility Periodic and Closed Patterns
High utility pattern mining (HUPM) is one of the key areas in data mining, which is concerned with identifying patterns with high utility from transactional databases. The temporal factors such as periodicity and recency along with dynamic variations in profits have recently been added to pattern mining. However, no methods so far unify these dimensions in a common framework. To this end, in this paper we propose the DTU-Miner algorithm that integrates temporal constraints and dynamic profit updates to overcome such limitations. Through the use of advanced data structures such as UPR-List and P-set and the introduction of some novel pruning strategies, DTU-Miner surpasses state of the art in terms of Runtime, Memory and pattern quality. Results on benchmark datasets show that DTU-Miner outperforms state-of-the-art algorithms, CPR-Miner and iEFIM-Closed, which suggests the effectiveness of DTU-Miner over dense and sparse datasets including dynamic attributes.
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Towards Smart Manufacturing: Implementing PI Control on PLCs in IIoT-Driven Industrial Automation
The rapid development of the Internet of Things (IoT) has drawn significant attention from both industry and academia, driven by the integration of cloud computing, big data analytics, machine learning, and cyber-physical systems in manufacturing. Programmable Logic Controllers (PLCs), long central to industrial control systems, have evolved from basic feedback control devices to advanced components capable of networking and data exchange through IoT technologies. The Industrial Internet of Things (IIoT) refers to intelligent automation systems that continuously monitor critical parameters and respond to changes in real time. The integration of IoT with PLCs is transforming industrial automation by enabling remote real-time monitoring, data-driven decision-making, and predictive maintenance through advanced analytics. IIoT technologies enhance manufacturing performance and offer strategic value across sectors. Understanding their impact involves examining current research, including technology assessments and application-based case studies. This study provides an overview of PLC systems evolving into IIoT frameworks, with a focus on implementing proportional-integral (PI) control using the Siemens S7-300. Designed for precise and consistent temperature regulation, this approach enhances process efficiency and product quality, making it highly suitable for industrial and manufacturing environments.
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