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In India, the agriculture is main profession for more than sixty percent of the population. The stakeholders of agriculture in India, facing plenty of problems that leads the people of the country to shift their profession and lets them migrate towards urban area. So, need of implementing technology in agriculture is must in future days because as population is increasing in exponential form as the result huge requirement of food and agricultural product. The data analytics will play a significant role in agricultural dataset for implementing prediction and recommendation system in the sector. Yield is one of the factors to be considered in the agriculture that determines the wellness and prosperity of the farmer. In this paper deals with prediction system to predict yield of areca nut product in Puttur taluk, Dakshin Kannda District, Karnataka state in India. The time series data analytics model known as Auto Regressive Integrated Moving Average (ARIMA) model is used for yield prediction system. The research is mainly focused on forecasting of areca nut production for next four or five years in Puttur taluk. It compares various ARIMA models with performance criteria and selects best model for prediction purpose. The diagnostic check is carried out to test the system performance After the prediction, then the actual values and predicted values are compared and presented in the form graphical representation. [ABSTRACT FROM AUTHOR]

In India, agriculture represents the primary occupation of more than 60% of the population. In terms of GDP, economic growth, traditional aspects, and social aspects, agriculture is essential for the country's development. The Indian farmers experienced numerous issues that have an impact on their way of life because the expansion in the agronomy business has not been as expected during the past two decades. Price fluctuation is one of the major issues faced by farmers, and as a result, they cannot get a reasonable price for their commodity. Also, it is very problematic to decide today without knowing the future price. So, this paper focused on finding a solution to the uncertainty problem in price faced by farmers that helps them take appropriate decisions during the farming process. The paper mainly concerns predictive data analytics using the ARIMA model, which predicts the price of areca nut products for the next 4 years using the past ten-year price dataset. The ARIMA model is a time series approach and a very appropriate framework for predicting future prices compared to other models. This paper includes a step-by-step procedure for the ARIMA techniques for forecasting price of agriculture commodity, and the outcomes are represented in the form of tables and graphical representations. [ABSTRACT FROM AUTHOR]

The expansion of Internet of Things (IoT) networks has presented new challenges in securing these interconnected devices against a variety of cyber threats. In this paper, we propose an innovative technique that integrates reinforcement learning (RL) and blockchain technology to improve the detection of attacks in IoT networks. Our design takes advantage of blockchain decentralized nature to establish a secure and transparent framework for network communication and consensus. We develop an RL-based attack detection model that identifies anomalies and potential hazards using IoT device data, network traffic, and historical attack patterns. Integrating the RL model with the blockchain network enables it to make decisions based on learned policies while maintaining the immutability and integrity of the decision-making process. We describe the main components of our design, such as the blockchain infrastructure, IoT device interaction, attack detection model, integration of reinforcement learning and blockchain, network consensus, continuous learning, and monitoring mechanisms. We demonstrate the efficacy of our proposed system in detecting attacks, mitigating risks, and adapting to changing threat landscapes through simulations and experiments. [ABSTRACT FROM AUTHOR]

The term adhoc network refers to a network that is created spontaneously, self-powered and infrastructure less networks. The energy in adhoc network does a crucial role in lengthening the network life span. The energy saving techniques in adhoc networks help to overcome the problem of link breakage. adhoc networks are generally used for specific scenarios, such as Earth Quakes, Military, Wild Life Monitoring, under water surveillance monitoring and disaster management. In this type of scenario, adhoc network nodes are located normally far away from each other and each node is independent in network and individually powered by batteries. So, Battery is the major constraint of this type of adhoc network. In an adhoc network, communication between nodes is possible by one another through forwarding packets and finding the paths to the destination by itself. Path selection in an adhoc network keeps in consideration energy usage, to increase the duration of the network. The selection of the optimal path can increase the duration of the network, if the highest leftover energy parameter is used to choose a path and a path with less hop count, the possibility of increase in network lifetime is more. The path selection is performed using the adhoc routing protocols and the AODV protocol has already been proven by researchers that it is appropriate for use in an adhoc network to increase battery efficiency and to lengthen the network life span. But, the AODV protocol has many disadvantages while determining the optimal path due to the reason, such as AODV protocol always tends to choose the shortest path without calculating the intermediary node residual energy, and then this leads to link breakage in the path. To overcome this drawback of the AODV protocol, we have designed a newly modified protocol called the Optimized Residual Energy Selection Adhoc On Demand Distance Vector (ORES-AODV) protocol, which selects the optimal path by calculating the average of maximum residual energy and by finding the average energy overhead of the intermediate nodes in the path and the mobility of the nodes are compared using mobility models. This protocol is 7% efficient than the other protocols proposed by taking AODV as the base protocol. Another Adhoc Network, the Wireless Sensor Network, has seen tremendous research in several years to solve the energy dissipation problem. To address the issue of energy waste in Wireless Sensor Networks, a new protocol called Modified Mobile-sinks-based Energy-efficient Clustering Algorithm (M-MECA) is introduced, which is based on mobile sink and clustering. The proposed protocol is compared with other protocols and the protocol developed is 6% efficient as evaluated with other protocols. [ABSTRACT FROM AUTHOR]

The emergence of Blockchain is seen as a viable technology for enabling the improved Internet services. To authenticate information via transactions without the involvement of a third party, this decentralised, secure, and auditable approach is used. Blockchain technology is now being used in conjunction with Wireless Sensor networks (WSNs) to help bring about the fourth industrial revolution. In this paper, we analyse the difficulties in transaction throughput enhancement and block time reduction that arise in blockchainenabled WSN networks. The study uses Dense Neural Networks to reduce the transmission delays. The simulations are conducted to test the viability of transactions and optimal distribution of transaction in WSN. Thus, Dense Nets enables optimal transactions of data from source to destination node via blocks. [ABSTRACT FROM AUTHOR]

Malignant rhabdoid tumour of kidney is a rare highly aggressive neoplasm of childhood. We present the case of a 18-months old girl presenting with decreased appetite, abdominal distention of 20 days duration and 3 episodes of haematuria. The patient underwent left radical nephrectomy and histopathological examination of the excised specimen confirmed the diagnosis of malignant rhabdoid tumour of the kidney. This case highlights the need to consider malignant rhabdoid tumour of the kidney of possibility young children in presenting with a renal mass. [ABSTRACT FROM AUTHOR]