Skin problems are among the most common ailments on Earth. Despite its popularity, assessing it is not easy because of the complexities in skin tones, hair colors, and hairstyles. Skin disorders provide a significant public health risk across the globe. They become dangerous when they enter the invasive phase. Dermatological illnesses are a significant concern for the medical community. Because of increased pollution and poor diet, the number of individuals with skin disorders is on the rise at an alarming rate. People often overlook the early signs of skin illness. The current approach for diagnosing and treating skin conditions relies on a biopsy process examined and administered by physicians. Human assessment can be avoided with a hybrid technique, thus providing hopeful findings on time. Approaches to a thorough investigation indicate that deep learning methods might be used to construct frameworks capable of identifying diverse skin conditions. Skin and non-skin tissue must be distinguished to detect skin diseases. This research developed a skin disease classification system using MobileNetV2 and LSTM. For this system, accuracy in skin disease forecasting is the primary aim while ensuring excellent efficiency in storing complete state information for exact forecasts.

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Deep Learning Approaches for Prognosis of Automated Skin Disease

Wireless sensor networks applications have been used in many areas that are difficult to access by humans. However, Routing remains the key challenges in sensor networks as it is important for the timely delivery of sensed datato the base station. In recent years, Multipath Routing has been used to ensure reliable and scalable data transmission in WSN. Although many multipath routing algorithms have been proposed, very few protocols have been focused on the Quality of Service (QoS) based routing. This paper proposes a QoS-aware, multipath routing protocol in which sensor nodes are clustered using the hybrid Particle Swarm Optimization-Cuckoo Search Optimization algorithm. The proposed protocol then chooses multiple stable paths (optimized network routing) using the Cluster Heads to transmit data based on multi-hop communication. Unlike the existing protocols, it relies on paths that do not affect QoS for rapid data transmission. It also extends the network lifetime by changing the Cluster Heads periodically based on the residual energy and uses the optimal number of paths to data transmission unlike the existing QoS Centric protocols. The performance of the proposed protocol is evaluated using NS-2 Simulator in different scenarios. The proposed protocol outperforms current protocols in terms of QoS parameters such as throughput, packet delivery ratio, end-to-end delay, and network lifetime, according to simulation results.

A Qos-Aware, Hybrid Particle Swarm Optimization-Cuckoo Search Clustering Based Multipath Routing in Wireless Sensor Networks

An artificial intelligence-based solar radiation prophesy model for green energy utilization in energy management system

Waste management is one of the world's biggest challenges, either in the developed or the emerging economies. The biggest problem with pollution is that the compost heap is well flowed in public areas before the next sanitation period starts. Demographic expansion has caused the hygienic condition with regard to the waste management system to deteriorate considerably. Disposal of waste is a fundamental element in waste disposal. Gradually, the technologies of artificial intelligence (AI) gained popularity in offering different computer ways to solving intelligent waste problem. The management of misdefined issues, experiences and uncertainties and partial data were efficient for AI. Even though this work did conduct much study, very few evaluations demonstrated the influence of AI to resolve many difficulties of intelligent management of waste. Accurate evaluation of garbage amount and quality is critical to Smart waste management system development and design. However, it is a challenging task to anticipate the quantity of trash created, given the several characteristics and its variability. The framework utilized in this document is the convolution neural network, a suitable approach for estimating the waste mass.

An Artificial Intelligence Based Predictive Approach for Smart Waste Management

Due to air pollution, pollutants that harm humans and other species, as well as the environment and natural resources, can be detected in the atmosphere. In real-world applications, the following impurities that are caused due to smog, nicotine, bacteria, yeast, biogas, and carbon dioxide occur uninterruptedly and give rise to unavoidable pollutants. Weather, transportation, and the combustion of fossil fuels are all factors that contribute to air pollution. Uncontrolled fire in parts of grasslands and unmanaged construction projects are two factors that contribute to air pollution. The challenge of assessing contaminated air is critical. Machine learning algorithms are used to forecast the surroundings if any pollution level exceeds the corresponding limit. As a result, in the proposed method air pollution levels are predicted using a machine learning technique where a computer-aided procedure is employed in the process of developing technological aspects to estimate harmful element levels with 99.99% accuracy. Some of the models used to enhance forecasts are Mean Square Error (MSE), Coefficient of Determination Error (CDE), and R Square Error (RSE).

/pdf/a-radical-safety-measure-for-identifying-environmental-1r0ypwlo.pdf

A Radical Safety Measure for Identifying Environmental Changes Using Machine Learning Algorithms

Along with the reason of optimization of the Spread factor of a generalized regression neural network (Grnn) which is a variant of radial basis function network (Rbfn), the projected paper presents...

Modelling of optimised neural network for classification and prediction of benchmark datasets

Column reactor models of volume size 3,000  and 1,500 cm3 are made using organic materials such as sawdust and immature (drumstick) Moringa oleifera and other natural materials such as gravels (6 mm size) and ball clay available locally at Jodhpur, India. Water is passed through these porous reactors under gravity at once. The experiments were aimed at finding low-cost solutions for wastewater or sewage disposal at point of use. The change in pH during water filtration experiments is measured and modelled as functions of X1 (column height), X2 (flow rate), X3 (cumulative percolation time) and X4 (change in electrical conductivity). The parameters X1, X2, X3 and X4 are found to be highly correlated to each other irrespective of materials used for making the bioreactors. There is a hyperbolic relationship between temperature gradient within the porous material column bed through which water is percolating and time taken during that process. The temperature distribution in the gravel or sawdust media...

Arpit Deepak Yadav

Papers

Modelling of optimised neural network for classification and prediction of benchmark datasets

Empirical models for change in pH and temperature within gravity-based reactor columns