Crime Prediction and Mapping Using Machine Learning Algorithms

One of the primary functions of government is to reduce crime. Despite the government's efforts to curb criminal activities, the security situation in many urban areas has deteriorated. This study aimed to create and test a machine learning model to predict crime categories and visualize the location where they occur using contextual features from the datasets. This was achieved by combining time, location, and contextual data with machine learning to improve crime prediction. The crime datasets were collected from various sources, including Nairobi County law enforcement agencies. They were s

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Design of a new text data similarity determination technique

Text similarity determination is one of the core technologies in the field of natural language processing. Its accuracy directly affects the accuracy of semantic similarity analysis and image, graphic and audio similarity. It is of great significance to conduct in-depth research on text similarity and mine efficient algorithms. The research goal of this paper is to improve the accuracy and efficiency of text similarity determination. This study summarize five traditional text similarity determination methods, namely Euclidean distance, cosine similarity, Manhattan distance, Jaccard similarity

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CMOS LC Voltage-Controlled Oscillator Sizing through Particle Swarm Optimization (PSO)

This paper introduces an advanced design methodology of a 2.6 GHz CMOS LC Voltage-Controlled Oscillator (VCO) within the AMS CMOS 0.35 μm technology process. The primary objectives of this design approach are to achieve minimal power consumption, a high figure-of-merit (FOM), and low phase noise. In addressing the inherent design challenges, a metaheuristic Particle Swarm Optimization (PSO) is implemented to identify the most effective dimensions for the components of the LC-VCO. In particular, the PSO algorithm is applied to optimize the channel length and width of the MOS transistors, aimin

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Optimizing Failure Prediction in Cloud Computing Using LSTM and MLP Integration

Accurate failure prediction in cloud computing is vital for maintaining system reliability and minimizing downtime. This paper presents an optimized approach for cloud failure prediction by integrating Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) models, leveraging the Google Cloud Trace dataset. To enhance the predictive accuracy and efficiency of the models, Grey Wolf Optimization (GWO) algorithm is employed for feature selection, ensuring that the most relevant features are utilized in the prediction process. The integration of LSTM's temporal sequence learning capabilitie

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The exploration and practice for the cultivation of the thinking ability of "data-based decision making" in teaching reform of chemical thermodynamics

The exploration and practice for the cultivation of the thinking ability of "data-based decision making" in teaching reform of chemical thermodynamicsThe exploration and practice for the cultivation of the thinking ability of "data-based decision making" in teaching reform of chemical thermodynamicsThe exploration and practice for the cultivation of the thinking ability of "data-based decision making" in teaching reform of chemical thermodynamicsThe exploration and practice for the cultivation of the thinking ability of "data-based decision making" in

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