Artificial Intelligence (AI) and Machine Learning (ML) are envisaged to play key roles in 5G networks. Efficient radio resource management is of paramount importance for network operators. With the advent of newer technologies, infrastructure, and plans, spending significant radio resources on estimating channel conditions in mobile networks poses a challenge. Automating the process of predicting channel conditions can efficiently utilize resources. To this point, we propose an ML-based technique, i.e., an Artificial Neural Network (ANN) for predicting SINR (Signal-to-Interference-and-Noise-Ratio) in order to mitigate the radio resource usage in mobile networks. Radio resource scheduling is generally achieved on the basis of estimated channel conditions, i.e., SINR with the help of Sounding Reference Signals (SRS). The proposed Non-Linear Auto Regressive External/Exogenous (NARX)-based ANN aims to minimize the rate of sending SRS and achieves an accuracy of R = 0.87. This can lead to vacating up to 4% of the spectrum, improving bandwidth efficiency and decreasing uplink power consumption.

This paper presents an intelligent approach for the detection of Melanoma—a deadly skin cancer. The first step in this direction includes the extraction of the textural features of the skin lesion along with the color features. The extracted features are used to train the Multilayer Feed-Forward Artificial Neural Networks. We evaluate the trained networks for the classification of test samples. This work entails three sets of experiments including $50\%$ , $70\%$ and $90\%$ of the data used for training, while the remaining $50\%,30\%$ , and $10\%$ constitute the test sets. Haralick’s statistical parameters are computed for the extraction of textural features from the lesion. Such parameters are based on the Gray Level Co-occurrence Matrices (GLCM) with an offset of $2,4,8,12,16,20,24$ and 28, each with an angle of $0,45,90$ and 135 degrees, respectively. In order to distill color features, we have calculated the mean, median and standard deviation of the three color planes of the region of interest. These features are fed to an Artificial Neural Network (ANN) for the detection of skin cancer. The combination of Haralick’s parameters and color features have proven better than considering the features alone. Experimentation based on another set of features such as Asymmetry, Border irregularity, Color and Diameter (ABCD) features usually observed by dermatologists has also been demonstrated. The ‘D’ feature is however modified and named Oblongness. This feature captures the ratio between the length and the width. Furthermore, the use of modified standard deviation coupled with ABCD features improves the detection of Melanoma by an accuracy of $93.7\%$

The aim of this paper is to explore the application of Neuro-Evolutionary Techniques to the diagnosis of various diseases. We applied the evolutionary technique of Cartesian Genetic programming Evolved Artificial Neural Network (CG-PANN) for the detection of three important diseases. Some cases showed excellent results while others are in the process of experimentation. In the first case we worked on diagnosing the extent of Parkinson's disease using a computer based test. Experiments in this case are in progress. In the second case, we applied the Fine Needle Aspirate (FNA) data for Breast Cancer from the WDBC website to our network to classify the samples as either benign (non-cancerous) or malignant (cancerous). The results from these experiments were highly satisfactory. In the third case, we developed a modified form of Pan-Tompkins's algorithm to detect the fiducial points from ECG signals and extracted key features from them. The features shall be applied to our network to classify the signals for the different types of Arrhythmias. Experimentation is still in progress.

Forecasting the El Nino-Southern Oscillation (ENSO) is a challenging task in climatology. It is one of the main factors responsible for the Earth’s interannual climatic fluctuation and can result in many climatic anomalies. The impacts include natural disasters (floods, droughts), low & high agriculture yields, price fluctuation, energy demand, availability of water resources, animal movement, and many more. This study presents a comprehensive ENSO dataset containing standard indicators and other relevant data to facilitate ENSO analysis and forecasting. To ensure the dataset's validity and reliability, we performed extensive data analysis and trained four basic deep models for time series forecasting (i.e. CNN, RNN, LSTM, and hybrids). The data analysis confirmed the accuracy and suitability of the dataset for ENSO forecasting. The LSTM model achieved the best fit to the data, leading to superior performance in forecasting ENSO events.

The introduction of 5G with excessively high speeds and ever-advancing cellular device capabilities has increased the demand for high data rate wireless multimedia communication. Data compression, transmission robustness and error resilience are introduced to meet the increased demands of high data rates of today. An innovative approach is to come up with a unique setup of source bit codes (SBCs) that ensure the convergence and joint source-channel coding (JSCC) correspondingly results in lower bit error ratio (BER). The soft-bit assisted source and channel codes are optimized jointly for optimum convergence. Source bit codes assisted by iterative detection are used with a rate-1 precoder for performance evaluation of the above mentioned scheme of transmitting sata-partitioned (DP) H.264/AVC frames from source through a narrowband correlated Rayleigh fading channel. A novel approach of using sphere packing (SP) modulation aided differential space time spreading (DSTS) in combination with SBC is designed for the video transmission to cope with channel fading. Furthermore, the effects of SBC with different hamming distances $d_{(H,min)}$ but similar coding rates is explored on objective video quality such as peak signal to noise ratio (PSNR) and also the overall bit error ratio (BER). EXtrinsic Information Transfer Charts (EXIT) are used for analysis of the convergence behavior of SBC and its iterative scheme. Specifically, the experiments exhibit that the proposed scheme of error protection of SBC $d_{(H,min)}$ = 6 outperforms the SBCs having same code rate, but with $d_{(H,min)}$ = 3 by 3 dB with PSNR degradation of 1 dB. Furthermore, simulation results show that a gain of 27 dB $E_b/N_0$ is achieved with SBC having code rate 1/3 compared to the benchmark Rate-1 SBC codes.

A black box system in vehicles acts as an important tool that records important information to make vehicles safer, investigate accidents, and even improve the overall performance of the vehicles. This study will present the Black Box System that has been developed using ESP32 microcontrollers for cars to enhance data collection and analysis in automotive fields using technology. The Black Box System or Event Data Recorder (EDR) is an important tool in the enhancement of road safety, investigation of accidents, and evaluation of the performance of a vehicle. The system utilizes ESP32 as the main microcontroller since it is cost-effective, efficient, and can be programmed in multiple ways. It comprises several sensors and data acquisition modules to collect key parameters including speed, acceleration, geographical location, engine, and various diagnostic information about the vehicle. This paper also presents a detailed overview and integration of the system into Hardware and software parts of the automobile. A user-friendly interface facilitates data retrieval and analysis, supporting applications in fleet management, driver behavior monitoring, and accident investigations. The study focuses on the responsibility and protection of personal data, as well as ways of protecting personal data from misuse and violation of the law. Therefore, using ESP32 technology in the vehicle’s Black Box System is a great improvement towards road safety and vehicle monitoring. By ensuring data security and privacy, this system provides the users with a complete data set to support a decision-making process for self-employed drivers and other organizations.