The dynamic force of artificial intelligence (AI) is reshaping our world, not in the distant future, but today. Its transformative potential, adaptability, and capacity to liberate human potential are becoming evident in a multitude of domains. AI's ability to process vast datasets, offer data-driven recommendations, and enhance decision-making processes underscores its pivotal role in addressing complex challenges. This article explores AI's current impact and its potential for further growth. It reviews 77 articles across diverse domains, highlighting AI's role in emergency services. Through an in-depth analysis of these studies, the paper provides a broad overview of the current state of AI in emergency services, identifying key trends, challenges, and future opportunities. By examining the methodologies, datasets, AI and deep learning techniques, feature selection processes, evaluation metrics, and prediction models used in each study, the paper aims to offer a thorough understanding of AI's role in this critical sector. This extensive body of knowledge is intended to be a valuable resource for researchers, practitioners, and policymakers. It supports the ongoing advancement of AI-driven emergency services, with the goal of saving lives, optimizing resource allocation, and enhancing response times in critical situations. Ultimately, this collaborative effort seeks to foster the development of more resilient and responsive emergency systems that can effectively mitigate risks and deliver timely aid to those in need. By advancing the capabilities of emergency response systems, AI enhances the precision and efficiency of critical interventions, ultimately leading to better outcomes and improved resilience in crisis situations.

This research work demonstrates cipher-type identification methods using machine learning algorithms. Cipher-type identification is a recent research interest to do better cryptanalysis of an encryption algorithm in a minimal time. Along with the increased security issues, obfuscation is being used with encryption algorithms to keep them hidden. This is when the ciphertext identification challenge came into play. The ciphertext classification challenge was performed using both image processing and natural language processing methods. For image processing purposes, CNN was utilized; whereas text-CNN, transformers and BERT models were used as natural language processing tools. In order to train the proposed machine learning based classification models, two types of datasets were generated: image data and text data. This study compares the experimental outcomes derived from various architectural CNN, Transformer, and BERT models. We also present a comparative study of our research work with another research works which are done in the recent past. The proposed BERT model is found to be the most efficient model for the correct classification of ciphertext over other transformer and CNN-based classification models. This work will surely help the cryptanalyst to perform cryptanalysis of an encryption algorithm in a minimal time.

The goal of designing and implementing an intelligent information system for the recognition and classification of sound signals is to create an effective solution at the software level, which would allow analysis, recognition, classification and forecasting of sound signals in megacities and smart cities using machine learning methods. This system can help people in various fields to simplify their lives, for example, it can help farmers protect their crops from animals, in the military it can help with the identification of weapons and the search for flying objects, such as drones or missiles, in the future there is a possibility for recognizing the distance to sound, also, in cities can help with security, so a preventive response system can be built, which can check if everything is in order based on sounds. Also, it can make life easier for people with impaired hearing to detect danger in everyday life. In the part of the comparison of analogues of the developed product, 4 analogues were found: Shazam, sound recognition from Apple, Vocapia, and SoundHound. A table of comparisons was made for these analogues and the product under development. Also, after comparing analogues, a table for evaluating the effects of the development was built. During the system analysis section, a variety of audio research materials were developed to indicate the characteristics that can be used for this design: period, amplitude, and frequency, and, as an example, an article on real-world audio applications is shown. A precedent scenario is described using the RUP methodology and UML diagrams are constructed: Diagram of use cases; Class diagram; Activity chart; Sequence diagram; Diagram of components; and Deployment diagram. Also, sound data analysis was performed, sound data was visualized as spectrograms and sound waves, which clearly show that the data are different, so it is possible to classify them using machine learning methods. An experimental selection of the machine learning method as staandart clasificers for building a sound recognition model was made. The best method turned out to be SVC, the accuracy of which reflects more than 30 per cent. A neural network was also implemented to improve the obtained results. The result of training a model based on a neural network during 100 epochs achieved a result of 97.7% accuracy for training data and 47.8% accuracy when checking performance on test data. This result should be higher, so it is necessary to consider improving recognition algorithms, increasing the amount of data, and changing the recognition method. Testing of the project was carried out, showing its operation and pointing out shortcomings that need to be corrected in the future.

In recent Artificial Intelligence developments, large datasets as knowledge are a prime requirement for analysis and prediction. To manage the knowledge of the network, the Data Center Network (DCN) has been considered a global data storage facility on edge servers and cloud servers. In recent research trends, knowledge-defined networking (KDN) architecture is considered, where the management plane works as the knowledge plane. The major network management task in the DCN is to control traffic congestion. To improve network management, i.e., optimized resource management, enhanced Quality of Service (QoS), we propose a path prediction technique by combining the convolution layer with the RNN deep learning model, i.e., Convolution-Long short-term memory network as Convolution-LSTM and the bi-directional long short-term memory (BiLSTM) network as Convolution-BiLSTM. The experimental results demonstrate that, in terms of many metrics, i.e., network latency, packet loss ratio, network throughput, and overhead, our proposed methodologies perform better than the existing works, i.e., OSPF, FlowDCN, modified discrete PSO, ANN, CNN, and LSTM-based routing approaches. The proposed approach improves the network throughput by approximately 30% and 12% as compared to existing CNN and LSTM-based routing approaches, respectively.

The complex process by which humans use their senses to clarify and understand the world around them is referred to as human perception. Analyzing human perception is important for comprehension of how humans think, feel, and act, which is helpful in a variety of contexts and ultimately promotes improved understanding, communication, and engagement. This study examines the field of text mining-based human perception analysis using a precisely chosen dataset of Twitter customer service discussions. Decision Trees, KNN, Naive Bayes, and GLM are four different algorithms that are methodically examined to determine which is the most effective method for understanding and predicting human perception from textual data. After an exhaustive analysis, the Decision Tree algorithm is shown to be the best performer, closely followed by Naive Bayes. The human perception analysis of text mining, including the methodology, findings, and implications, is described in depth. 

MANET is an infrastructure-less network, which is comprised of a greater number of nodes that are moving in a random manner. Establishing efficient and reliable routes is the crucial design issue in MANET. In addition, providing energy-efficient routes for performing communication operations in MANET is also a challenging goal. Increasing network lifetime and decreasing overall traffic load are the significant considerations while designing the MANET. An energy optimization routing mechanism with a fuzzy basis that is state-based and influenced by COVID-19 is used to prevent and control epidemics. Based on their lifespan and varying mobility speeds, it is dependent on energy optimization yield under various scenarios. Fuzzy-based bi-objective criteria in energy-efficient routing protocol for the mobile ad-hoc network to select the different constraints like link stability, bandwidth, and battery life. The main goal of this paper is to obtain the optimal solution to choose the best route, limit battery life, and change the movement of mobile nodes for large-scale mobile ad-hoc networks. It is demonstrated that the possible solution relates to the domain of selecting the variables by outlining the connection between the energy function’s variables. Researchers optimize a path's link lifespan, resulting in the choice of the shortest path being particularly efficient in COVID-19 pandemic prevention and treatment.