Parkinson’s disease (PD) is a chronic and progressive neurological disease that mostly shakes and compromises the motor system of the human brain. Patients with PD can face resting tremors, loss of balance, bradykinesia, and rigidity problems. Complex patterns of PD, i.e., with relevance to other neurological diseases and minor changes in brain structure, make the diagnosis of this disease a challenge and cause inaccuracy of about 25% in the diagnostics. The research community utilizes different machine learning techniques for diagnosis using handcrafted features. This paper proposes a computer-aided diagnostic system using a convolutional neural network (CNN) to diagnose PD. CNN is one of the most suitable models to extract and learn the essential features of a problem. The dataset is obtained from Parkinson’s Progression Markers Initiative (PPMI), which provides different datasets (benchmarks), such as T2-weighted MRI for PD and other healthy controls (HC). The mid slices are collected from each MRI. Further, these slices are registered for alignment. Since the PD can be found in substantia nigra (i.e., the midbrain), the midbrain region of the registered T2-weighted MRI slice is selected using the freehand region of interest technique with a 33 × 33 sized window. Several experiments have been carried out to ensure the validity of the CNN. The standard measures, such as accuracy, sensitivity, specificity, and area under the curve, are used to evaluate the proposed system. The evaluation results show that CNN provides better accuracy than machine learning techniques, such as naive Bayes, decision tree, support vector machine, and artificial neural network.

Wind is one of the most important resources in the renewable energy basket. However, there are questions regarding wind as a sustainable solution, especially concerning its upfront costs, visual impact, noise pollution, and bird collisions. These challenges arise in commercial windmills, whereas for domestic small-scale windmills, these challenges are limited. On the other hand, accurate wind speed prediction (WSP) is crucial for optimizing power management in renewable energy systems. Existing research focuses on proposing model architectures and optimizing hyperparameters to improve model performance. This approach often results in larger models, which are hosted on cloud servers. Such models face challenges, including bandwidth utilization leading to data delays, increased costs, security risks, concerns about data privacy, and the necessity of continuous internet connectivity. Such resources are not available for domestic windmills. To overcome these obstacles, this work proposes a transformer model integrated with Long Short-Term Memory (LSTM) units, optimized for memory-constrained devices (MCDs). A contribution of this research is the development of a novel cost function that balances the reduction of mean squared error with the constraints of model size. This approach enables model deployment on low-power devices, avoiding the challenges of cloud-based deployment. The model, with its tuned hyperparameters, outperforms recent methodologies in terms of mean squared error, mean absolute error, model size, and R-squared scores across three different datasets. This advancement paves the way for more dynamic and secure on-device wind speed prediction (WSP) applications, representing a step forward in renewable energy management.

The meteorological parameters review the impact of refractivity on received signal strength for the one-fourth kilometre square area is confer for frequency 1800 to 1900 MHz. It is necessary to know the refractivity profile of radio frequencies in the surface layer of the atmosphere to predict the execution of a radio system for the consistent gradient of refractivity. The data were accumulated from the Atoll software and then an experimental setup was used on the same investigated area to compare both figures and find out the effect of refractivity on received signal strength. It has been analyzed that environmental parameter such as temperature, humidity, and height above the ground level play a major role in varying refractivity. The information recorded from the experimental arrangement shows that when refractivity is high (more particularly around the afternoon time and in the morning time, the humidity is high) the quality of the signal strength is degraded, while at that point when the refractivity is low (more particularly during the daytime when humidity is low due to high temperature) the quality of the signal strength is better. Refractivity and signal strength are inversely proportional to each other. It is essential to apply an accurate path loss prediction technique to demonstrate the need for a precise accounting of the refraction and ducting when planning future radio networks.