As crime rates continue to rise, law enforcement agencies face the challenge of dealing with increased demand for their services. However, with the use of intelligence, there is an opportunity to enhance prediction and prevention efforts. A study has introduced an approach that optimizes neural networks for predicting crime trends over time and space. The methodology involves utilizing Recurrent Neural Networks to analyse behaviour patterns, Deep Convolutional Neural Networks for understanding traits, and Cross Deep Learning for integrating facial recognition capabilities. The model is trained on crime data from a specific location, along with various spatial, temporal, and demographic factors. Moreover, extracting insights from these datasets through data mining is critical. Thorough testing is conducted to improve the accuracy of identifying behaviours, enabling targeted crime prevention strategies. The advanced models have demonstrated success in forecasting crime hotspots with accuracy, which can help in allocating resources effectively. By implementing surveillance and analysis in cities, not only are security measures enhanced, but the burden on law enforcement is also reduced. This allows them to focus on critical tasks. This approach underscores the potential of optimized deep learning algorithms and neural networks in predicting activities based on data analysis. These AI-driven tools offer intelligence to support resource allocation as police departments adopt cutting-edge practices. The proposed framework can be expanded to regions to address emerging security challenges. In conclusion, this groundbreaking approach highlights the importance of using advanced technologies in crime prevention, enabling law enforcement agencies to serve communities more effectively.

The problem of determining a suitable radar signal while reducing the autocorrelation sidelobes and analyzing such radar signal under Doppler Effect and background noise environments with non-uniform PRI is addressed. The high peak sidelobe is the fundamental drawback for a Linear Frequency Modulated signal (LFM), despite its advantage in the simplicity and Doppler tolerance properties. To address this issue, an Improved Polynomial-I & Polynomial-II Non-Linear Frequency Modulation (NLFM) signal was designed. Prior research demonstrated that constructed Polynomial NLFM (PNLFM) with Non-Uniform Pulse Repetition Interval (PRI) signal provides reduced Peak Side Lobe level (PSL) compared to LFM. To examine the overall performance, research is done on the produced PNLFM signal in the condition of Doppler Effect and background noise. The background noise considered is Additive white Gaussian noise (AWGN) with SNR values ranging from -20dB to 20dB. Simulations are performed using various doppler frequencies and background noises. The simulation results show that the PNLFM is Doppler-tolerant in the Doppler shift case, i.e. there is no effect on PSL levels with the doppler frequency change in either the uniform or non-uniform PRI case. However, with high background noise the PSL levels get affected, which increases the PSL values. From the results, it is observed that the PNLFM with Non-Uniform PRI has a good detectability since the peak side lobe level is low in certain cases of heavy background noise.

Because people's intentions, feelings, and emotions are reflected in their facial expressions, researchers have been drawn to create self-acting autonomous facial expression detection systems. The facial emotion expression system is difficult due to the model's complexity, the small number of training data, and the minute micro facial muscle movements, despite the advancements in deep learning frameworks for automatic facial expression detection. This study suggests a deep learning framework consisting of Fully convolutional network called FCN-Long Short-Term Memory (LSTM), to detect behavior, mood, and facial activity using fine-grained facial action unit recognition with an ERS model. Based on these distinct patterns, the framework may be used to infer an individual's emotional state. The FCN helps in extracting the useful features, which then helps to increase the accuracy of the FCN-LSTM model. The LSTM is used to recognize the facial expression and present the state of the individual based on the expression by using the features that are extracted by FCN. This combination is helpful in generating accurate results for facial emotions recognition (FER). The model is trained and tested on Emotions (Emo-DB) dataset. The accuracy is observed as 94.67%, which is higher when compared to existing deep learning models.

This paper introduces a generalized approach structured for the systematic supervising and the performance assessment of drought environments. A significant part of the Time Series Analysis module is taking the history of drought-related parameters measures and giving more detail on the progress and the intensity of the drought esvents. Moreover, Deep Learning algorithms are used to train data sets and to make predictions about future values. The research was done using the multimodal technique (which encompasses machine learning approaches and data-driven analysis based on the satellite images of many sources) for the dispelling of the drought problem in Maharashtra, India in 2018 to 2021 in a bid to disseminate information. With respect to this issue, thorough and prompt drought estimation is a top concern to the successful management of these threats through water resource management. The study adopted the use of new models such as ConvLSTM2D, LSTM models, ARIMA and Random Forest regression to improve drought monitoring. Experimental results were scrutinized and illustrated with performance measures like R-squared, mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). LSTM algorithm demonstrated a very strong potential for learning extended connections and sequences. ARIMA method was more likely to catch the seasonal patterns. The ConvLSTM2D algorithm was enforced to enhance the drought monitoring process. It is as a result of this improvement that both drought evaluation and prediction matured, ultimately enabling more informed decision-making in a bid to manage the crisis as early as possible with mitigation measures being immediately put in place.

To deliver a quality project on time during software maintenance, selecting the most suitable developer to assign a newly reported bug is a complex task. The proposed Data-Driven Developer Recommendation System (DDRS) examines developer performance metrics based on bug reports. It recommends using machine learning techniques such as Random Forest, Decision Tree, and Support Vector Machine (SVM). The dataset from four open-source projects (SWT, Eclipse UI, BIRT, and JDT) is initially preprocessed. The algorithm computes severity and priority scores, average bug resolution time, and developer effort before merging these parameters into a Developer Weighted Score for ranking. Machine learning models are trained on the complete dataset with textual features converted to numerical representations via TF-IDF. These algorithms predict developer appropriateness for new bugs by combining predictions and precomputed scores to produce a top ten list. When tested using 10-fold cross-validation, the model displayed better accuracy, scoring up to 98.85% on BIRT, 97.7% on JDT, 91.5% on Eclipse UI, and 96.8% on SWT. The study emphasizes the importance of bug priority, resolution time, workload, and severity in triage. The suggested methodology successfully automates developer assignment, resulting in more efficient and accurate defect resolution in software development projects.

The ever-changing and temporary nature of Random Access Memory (RAM) presents distinct challenges and possibilities for digital forensic investigations. This research paper offers a comprehensive analysis of RAM data retrieval techniques within the realm of forensic studies. Starting with a comprehensive explanation of the importance of RAM in forensic investigations, this paper explores the different techniques used to extract volatile data from RAM. These methods involve conducting live system analysis, memory imaging, and memory dumping techniques, each with their own set of advantages and limitations. In addition, the paper delves into the complexities of data acquisition, preservation, and analysis, taking into account factors such as system state volatility, encryption, and anti-forensic measures. In addition, this paper explores the practicality and effectiveness of RAM data retrieval methods on various operating systems and hardware setups. It assesses the strengths and weaknesses of commonly used forensic tools and frameworks for RAM analysis, showcasing their performance in practical situations. In addition, the research explores the latest developments and trends in RAM forensics. This includes the use of machine learning algorithms to automate memory analysis and the creation of specialized forensic techniques for cloud environments. Finally, this paper brings together the findings to provide valuable insights into the best practices for RAM data retrieval in forensic investigations. It highlights the significance of meticulous documentation, maintaining a clear chain of custody, and adhering to legal standards. Through the synthesis of established knowledge and the exploration of emerging trends, this research makes a significant contribution to the field of RAM forensics. It offers valuable guidance for digital forensic practitioners, law enforcement agencies, and researchers, aiding in their understanding and advancement of the subject.