Since the open nature of Internet of Things (IoT), it is more vulnerable for various attacks that degrades the network topologies and functionalities. Rather than other attacks, Rank Attack (RA) becomes the most dangerous attack mainly affecting the rank values of routing process. Thus, it reaches more attention in “Routing Protocol for Low Power and Lossy Networks (RPL)” as well. Hence, the trust-based secured IoT network is a quite challenging process. The former methods are limited with overhead problem, more energy consumption and RA, which is a basic security attack on routing in IoT networks. In order to tackle and to detect the attack, the secured IoT routing against rank attacks framework is developed. Initially, the data is aggregated from benchmark datasets. Further, the aggregated data is given to the Multiscale Depth wise Separable One-Dimensional Convolutional Neural Network (MDS-1DCNN) model for rank attack detection. In addition to that, the nodes are mitigated while the routing process takes place. To validate the efficiency of the given network model during routing, the Golden Eagle Optimizer (GEO) is utilized to derive the objective function using the constraints such as shortest distance, energy, path loss and delay. Finally, the performance is validated using diverse parameters and compared against classical methodologies. Thus, the results illustrate that the proposed model has effectively detected the RA that facilitates the secured IoT routing against other models.

Optical character recognition (OCR) technology is indispensable for converting and analyzing text from various sources into a format that is editable and searchable. Telugu handwriting presents notable challenges due to the resemblance of characters, the extensive character set, and the need to segment overlapping characters. To segment the overlapping characters, we assess the width of small characters within a word and segment the overlapping characters accordingly. This method is well suited for the segmentation of overlapping compound characters. To address the recognition of similar characters with less training periods we have used ResNet 18 and SqueezeNet models which have achieved character recognition rates of 95% and 94% respectively.

Organizations with field employees face the challenge of maintaining high-quality standards while optimizing costs. Analyzing, Classifying, Predicting, and Monitoring the Field Employee's Regular Activities, Work Productivity and Performance is the challenging objective for Management in any Organization. Research study suggests using Android Application and machine learning predictive analytic model by using decision tree algorithm to analyze and classify field employee work productivity and performance. This aims to achieve a higher accuracy rate and better insights into employee performance. To track field employees, a GPS tracking app is used on mobile devices. As part of Mobile Computing implementation, Android Mobile device transfers the data in the form of text, voice and images to the web server and vice-versa. It generates location data with secured communication through cryptography techniques and stores captured data in a database server for further analysis. Furthermore, a web-based application is proposed for monitoring and generating reports for individuals and groups of field employees, including route maps and time sheets. This proposed solution is going to be a complete one-point automated software solution for any business organization for managing their field employees.

Nowadays, heart diseases have become a leading cause of mortality worldwide and it affects a huge number of individuals. The early and accurate prediction of heart disease risk factors plays a crucial role in preventing opposing results. Additionally, it is necessary to recognize heart disease quickly and accurately by analyzing patient’s data. This paper proposed a novel approach for predicting heart disease through machine learning techniques. The proposed Bat Algorithm (BA) and Particle Swarm Optimization (PSO) based Random Forest (RF), named BAPSO-RF is utilized for selecting optimum features that can enhance the heart-disease prediction accuracy. The proposed BAPSO-RF is evaluated on UCI heart disease dataset which contains 14 attributes and 270 records. The proposed BAPSO-RF model attains better results by utilizing metrics like accuracy, precision, recall, and f1-score values of about 98.71%, 98.67%, 98.23%, and 98.45% correspondingly which ensures early and accurate prediction of heart disease compared to existing techniques like hybrid of Genetic Algorithm and Particle Swarm Optimization (PSO) with Random Forest (GAPSO-RF), stacked Genetic Algorithm (GA) and Genetic Algorithm with Radial Basis Function (GA-RBF).

Optical character recognition (OCR) is a process that recognizes and converts data from scanned images, including both handwritten and printed documents, into an accessible format. The challenges in Telugu OCR arise from compound characters, an extensive character set, limited datasets, character similarities, and difficulties in segmenting overlapping characters. To tackle these segmentation complexities, an algorithm has been developed, prioritizing the preservation of essential features during character segmentation. For distinguishing between structurally similar characters, we used convolutional neural networks (CNN) due to their feature-extracting properties. We have employed the CNN model, the SqueezeNet for feature extraction, resulting in an impressive character recognition rate of 94% and a word recognition rate of 80%.

A secured IoT routing model against different attacks has been implemented to detect attacks like replay attacks, version attacks, and rank attacks. These attacks cause certain issues like energy depletion, minimized packet delivery, and loop creation. By mitigating these issues, an advanced attack detection approach for secured IoT routing techniques with a deep structured scheme is promoted to attain an efficient attack detection rate over the routing network. In the starting stage, the aggregation of data is done with the help of IoT networks. Then, the selected weighted features are subjected to the Multiscale Depthwise Separable 1-Dimensional Convolutional Neural Networks (MDS-1DCNN) approach for attack detection, in which the parameters in the 1-DCNN are tuned with the aid of Fused Grasshopper-aided Lemur Optimization Algorithm (FG-LOA). The parameter optimization of the FG-LOA algorithm is used to enlarge the efficacy of the approach. Especially, the MDS-1DCNN model is used to detect different attacks in the detection phase. The attack nodes are mitigated during the routing process using the developed FG-LOA by formulating the fitness function based on certain variables such as shortest distance, energy, path loss and delay, and so on in the routing process. Finally, the performances are examined through the comparison with different traditional methods. From the validation of outcomes, the accuracy value of the developed attack detection model is 96.87%, which seems to be better than other comparative techniques. Also, the delay analysis of the routing model based on FG-LOA is 17.3%, 12.24%, 10.41%, and 15.68% more efficient than the classical techniques like DHOA, HBA, GOA, and LOA, respectively. Hence, the effectualness of the offered approach is more enriched than the baseline approaches and also it has mitigated diverse attacks using secured IoT routing and different attack models.