Testimonials : 04

The opinions of the users are analyzed by the sentiment analysis process. Sometimes, the users reviewed their opinions using emojis and it is necessary to analyze them to find the classes (positive, negative, or neutral). However, the existing works lack the ability of emoji analysis in large databases, which induces computational complexity and reduces performance. To tackle those issues, we propose a novel Recurrent Neural network (RNN) with an Emperor Penguin-based Salp Swarm algorithm (EPS2) approach. The optimization algorithm can be used to choose the parameters of the RNN and provide better results. The experiments are conducted by taking the data from four social media platforms known as Reddit, Twitter, IMDB movie review, and Yelp dataset. The performance is analyzed by different metrics and compared the outcomes with other state-of-art works. From the results, it is found that our proposed approach effectively analyses the emojis from the social media platform and provides better results.

Testimonials : 11

This study introduces a novel approach to error detection and correction within Very Large-Scale Integration (VLSI) systems, specifically tailored for space applications. The core of this research is the development and implementation of a sophisticated 2-dimensional error correction code designed to significantly enhance memory reliability in the harsh conditions of outer space. Traditional error correction methods, while effective to a certain extent, fall short in addressing the complex phenomenon of burst errors—errors that occur in multiple bits simultaneously as a result of a single disruptive event, such as cosmic radiation. The proposed error correction scheme innovatively employs extended XOR operations, covering larger blocks of data, thus offering a more comprehensive solution for detecting and correcting burst errors. Moreover, the integration of Cyclic Redundancy Check (CRC) techniques further bolsters the error detection and correction capabilities of the system. Through a detailed comparison with existing methods, our study demonstrates that the proposed 2-dimensional code not only addresses the limitations of current error correction techniques but also contributes to the advancement of memory system reliability in space engineering. The implementation of this method is poised to provide better performance in environments where burst errors are prevalent, marking a significant step forward in the domain of space system design and reliability. 

Testimonials : 01

Human Activity Recognition (HAR) plays a significant role in several fields by automatically identifying and monitoring human activities using advanced techniques. It enhances safety, improves healthcare services, optimizes fitness routines, and enables context-aware applications in various fields. HAR contributes to a more efficient and intelligent interaction between humans and technology. It has emerged as an essential research domain with applications in healthcare, smart environments, and human-computer interaction. This study aims to provide a comprehensive survey of the evolving landscape of HAR, including key methodologies, techniques, and trends in existing research. The study discusses various applications of HAR and their significance in modern smart environments. The survey also highlights different types of HAR and data collection techniques. Additionally, it explores various methods for analyzing the collected data and provides a comprehensive analysis of existing human activity classification datasets. It offers valuable insights into understanding the strengths and limitations of various HAR techniques. The study also discusses various challenges and future directions for HAR.   

Testimonials : 02

Medical image fusion technology and its collective diagnosis are becoming crucial day by day.This task confers the latest algorithm for image fusion of medical images to many diagnostic complications. Firstly, transform is employed on input source images. The result of the application of transform is the decomposition of source images into various subbands. Eminent features are extracted from these subbands by using resnet50. These features are fused by phase congruency and guided filtering fusion rules. Finally, inverse transform gives the original image. The experiment results of this algorithm are compared with different methods by taking some pairs of medical images. Subjective and objective outcomes prove that the proposed algorithm exceeds the current methods by giving optimal performance measures in the area of medical diagnosis. Thus, it is revealed that the suggested multimodal image fusion model provides elevated performance over existing models via diverse diseases using MRI-SPECT and MRI-PET.

Testimonials : 03

Unmanned Aerial Vehicles (UAVs) have grown into a more powerful type of data transmission due to this rapid progress of evolution of wireless communication technology. In addition, UAVs have been proven to be effective in a variety of applications, including intelligent transport, disaster risk management, surveillance, and environmental monitoring. When UAVs are deployed randomly, however, they can effectively accomplish challenging tasks because of the UAVs’ has low battery capacity, quick mobility, and dynamic in nature orientation. Due to this reason, a new technique must be designed for an optimal energy efficient UAV clustering as well as data routing protocols. In this work proposes a new hybrid model of Emperor penguin-based Generalized Approximate Reasoning Based Intelligent Control (EP-GARIC) cluster-based network topology. Furthermore, the optimal routing function is achieved by the proposed Artificial Jellyfish Optimization (AJO). The implementation of this research is carried out using Network Simulator (NS2). The simulation results displays the effective performance of the suggested approach in terms of reduced energy consumption, improved packet delivery ratio, reduced loss, and so on over compared to the conventional approaches. 

Testimonials : 05

Unmanned Aerial Vehicles (UAVs) have evolved into a potent form of data transmission, benefiting from the rapid advancements in wireless communication technology. Furthermore, UAVs have demonstrated their effectiveness across diverse applications, such as intelligent transportation, disaster risk management, surveillance, and environmental monitoring. When UAVs are deployed randomly, however, they can effectively accomplish challenging tasks because of the UAVs’ has low battery capacity, quick mobility, and dynamic in nature orientation. Due to this reason, a new technique must be designed for an optimal energy efficient UAV clustering as well as data routing protocols. In this work proposes a new hybrid model of Emperor penguin-based Generalized Approximate Reasoning Based Intelligent Control (EP-GARIC) cluster-based network topology. Moreover, the proposed model achieves the most efficient routing function through the utilization of the novel Artificial Jellyfish Optimization (AJO) technique. The execution of this study is conducted within the Network Simulator (NS2) environment. The outcomes of the simulations distinctly demonstrate the notable effectiveness of the suggested methodology. This is evidenced by a marked decrease in energy consumption, a substantial improvement in packet delivery ratio, a noteworthy reduction in losses, and other comparable metrics when contrasted with established conventional methods. Keywords—Clustering, Neural Network, Fuzzy method, Energy Efficiency, Parameter Tuning.

Testimonials : 06

Tropical cyclones (TC) are among the worst natural disasters, that cause massive damage to property and lives. The meteorologists track these natural phenomena using Satellite imagery. The spiral rain bands appear in a cyclic pattern with an eye as a center in the satellite image. Automatic identification of the cyclic pattern is a challenging task due to the clouds present around the structure. Conventional approaches use only image data to detect the cyclic structure using deep learning algorithms. The training and testing data consist of positive and negative samples of TC. But the cyclic structure's texture pattern makes it difficult for the deep learning algorithms to extract useful features. This paper presents an automatic TC detection algorithm using optical flow estimation and deep learning algorithms to overcome this draw-back. The optical flow vectors are estimated using the Horn-Schunck estimator, the Liu-Shen estimator, and the Lagrange multiplier. The deep learning algorithms take the optical flow vectors as input during the training stage and extract the features to identify the cyclone's circular pattern. The software used for experimental analysis is MATLAB 2021a. The proposed method increases the accuracy of detecting the cyclone pattern through optical flow vectors compared to using the pixel intensity values. By using proposed method 98% of accuracy will be achieved when compared with the existing methods.

Testimonials : 07

significant subfields in “Synthetic Aperture Radar (SAR)” research is considered to be target detection. Numerous studies have been conducted on target identification, with the majority of them favoring filter-oriented methods. The fundamental goal of radar systems is to “detect moving targets on the ground.” Decomposing a complex matrix into a structured sparse matrix and a low-rank matrix is a fundamental mathematics issue. Surveillance and reconnaissance rely heavily on “Ground Moving Target Indication (GMTI),” but it's not a simple task. The SAR ATI was first developed for calculating the radial velocity of ground-moving objects. Yet, overlapping stationary clutter can corrupt the recorded differential phase, resulting in mistakes in position and velocity calculations. The main concept of this paper is to propose a novel “Adaptive Simplified Fractional Fourier Transform (A-SFrFT)” using the intelligent meta-heuristic improvement. This adaptive SFrFT efficiently estimates the “Doppler parameters of the moving targets.” The improved “Harris Hawks Optimization (HHO)” termed Trio Updating HHO (TU-HHO) is used as the meta-heuristic algorithm that enhances the performance of the SFrFT-based target estimation. The mathematical analysis and simulation findings show that the suggested methods recommended strategy is successful.

Testimonials : 08

Data analytics involves examining raw data to extract insights and make informed decisions. It includes various techniques and processes to clean, transform, and model data to discover patterns, trends, correlations, and other meaningful information. It's used across industries for purposes like optimizing business operations, understanding customer behavior, improving healthcare outcomes, and more. Tools like statistical analysis, machine learning, data mining, and visualization aid in this process, allowing analysts to derive actionable insights from large and complex datasets.

Testimonials : 09

The Internet of Things (IoT) is a revolutionary technological concept that has transformed the way we interact with the world around us. It refers to a network of interconnected devices, objects, and systems that have the ability to collect, exchange, and process data over the internet without human intervention. These devices, often embedded with sensors and connectivity features, can range from everyday objects like smartphones and wearable devices to specialized equipment in industries, such as sensors in manufacturing plants or smart meters in energy grids. The power of IoT lies in its ability to enable seamless communication and collaboration between these devices, leading to unprecedented levels of automation, efficiency, and convenience in various aspects of our lives. Through IoT, devices can communicate with each other, share information, and perform tasks autonomously, creating a more connected and intelligent environment. This interconnectedness has given rise to a wide array of applications across numerous industries. In healthcare, IoT enables remote patient monitoring and personalized healthcare solutions. In agriculture, it facilitates precision farming techniques. Smart homes and cities utilize IoT to enhance energy efficiency, security, and overall quality of life. Industries like manufacturing and transportation benefit from IoT through predictive maintenance and optimized

Testimonial : 10

In this time of rapid invention and utilisation of battery-operated products, battery life is a significant problem. Because the traditional Full Adder(FA) uses more energy, we used low power FA circuitry in this study and examined how it functions in lieu of the traditional Full Adder circuitry. Contrary to the latest State of Art, only ten transistors make up the suggested design,which runs on a 0.8 supply voltage. This study compares and contrasts the present design with the FA's suggested work in respect of power and delay. This design consumes low power of only 674.38 nWand is area efficient as it consists of only ten transistors. The presented design of FA consumes less power and offers very less delay of only 2.3 ps than existing designs. TANNER EDA is used to simulate proposed FA and using a 65nm CMOS technology.

Testimonials : 12

Medical image fusion technology and its collective diagnosis are becoming crucial day by day. This task confers the latest algorithm for image fusion of medical images to many diagnostic complications. Firstly, transform is employed on input source images. The result of the application of transform is the decomposition of source images into various subbands. Eminent features are extracted from these subbands by using resnet50. These features are fused by phase congruency and guided filtering fusion rules. Finally, inverse transform gives the original image. The experiment results of this algorithm are compared with different methods by taking some pairs of medical images. Subjective and objective outcomes prove that the proposed algorithm exceeds the current methods by giving optimal performance measures in the area of medical diagnosis. Thus, it is revealed that the suggested multimodal image fusion model provides elevated performance over existing models via diverse diseases using MRI-SPECT and MRI-PET.

Testimonials : 13

Since CMOS technology consumes less power it is a key technology for VLSI circuit design. With technologies reaching the scale of 10 nm, static and dynamic power dissipation in CMOS VLSI circuits are major issues. Dynamic power dissipation is increased due to requirement of high speed and static power dissipation is at much higher side now a days even compared to dynamic power dissipation due to very high gate leakage current and subthreshold leakage. Low power consumption is equally important as speed in many applications since it leads to a reduction in the package cost and extended battery life. This paper surveys contemporary optimization techniques that aims low power dissipation in VLSI circuits.