Vol.33, No.1, 2022-Table of Contents
  • Deep Reinforcement Learning-Based Long Short-Term Memory for Satellite IoT Channel Allocation
  • Abstract In recent years, the demand for smart wireless communication technology has increased tremendously, and it urges to extend internet services globally with high reliability, less cost and minimal delay. In this connection, low earth orbit (LEO) satellites have played prominent role by reducing the terrestrial infrastructure facilities and providing global coverage all over the earth with the help of satellite internet of things (SIoT). LEO satellites provide wide coverage area to dynamically accessing network with limited resources. Presently, most resource allocation schemes are designed only for geostationary earth orbit (GEO) satellites. For LEO satellites, resource allocation is challenging due to… More
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  • Adaptive XGBOOST Hyper Tuned Meta Classifier for Prediction of Churn Customers
  • Abstract In India, the banks have a formidable edge in maintaining their customer retention ratio for past few decades. Downfall makes the private banks to reduce their operations and the nationalised banks merge with other banks. The researchers have used the traditional and ensemble algorithms with relevant feature engineering techniques to better classify the customers. The proposed algorithm uses a Meta classifier instead of an ensemble algorithm with an adaptive genetic algorithm for feature selection. Churn prediction is the number of customers who wants to terminate their services in the banking sector. The model considers twelve attributes like credit score, geography,… More
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  • Light-Weight Present Block Cipher Model for IoT Security on FPGA
  • Abstract The Internet of Things (IoT) plays an essential role in connecting a small number of billion devices with people for diverse applications. The security and privacy with authentication are challenging work for IoT devices. A light-weight block cipher is designed and modeled with IoT security for real-time scenarios to overcome the above challenges. The light-weight PRESENT module with the integration of encryption (E)-decryption (D) is modeled and implemented on FPGA. The PRESENT module has 64-bit data input with 80/128/256-bit symmetric keys for IoT security. The PRESENT module performs16/32/64 round operations for state register and key updation. The design mainly uses… More
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  • A Wireless ECG Monitoring and Analysis System Using the IoT Cloud
  • Abstract A portable electrocardiogram (ECG) monitoring system is essential for elderly and remote patients who are not able to visit the hospital regularly. The system connects a patient to his/her doctor through an Internet of Things (IoT) cloud server that provides all the information needed to diagnose heart diseases. Patients use an ECG monitoring device to collect and upload information regarding their current medical situation via the Message Queue Telemetry Transport (MQTT) protocol to the server. The IoT cloud server performs further analysis that can be useful for both the doctor and the patient. Moreover, the proposed system has an alert… More
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  • Error Rate Analysis of Intelligent Reflecting Surfaces Aided Non-Orthogonal Multiple Access System
  • Abstract A good wireless device in a system needs high spectral efficiency. Non-Orthogonal Multiple Access (NOMA) is a technique used to enhance spectral efficiency, thereby allowing users to share information at the same time and same frequency. The information of the user is super-positioned either in the power or code domain. However, interference cancellation in NOMA aided system is challenging as it determines the reliability of the system in terms of Bit Error Rate (BER). BER is an essential performance parameter for any wireless network. Intelligent Reflecting Surfaces (IRS) enhances the BER of the users by controlling the electromagnetic wave propagation… More
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  • An Advanced Integrated Approach in Mobile Forensic Investigation
  • Abstract Rapid advancement of digital technology has encouraged its use in all aspects of life, including the workplace, education, and leisure. As technology advances, so does the number of users, which leads to an increase in criminal activity and demand for a cyber-crime investigation. Mobile phones have been the epicenter of illegal activity in recent years. Sensitive information is transferred due to numerous technical applications available at one’s fingertips, which play an essential part in cyber-crime attacks in the mobile environment. Mobile forensic is a technique of recovering or retrieving digital evidence from mobile devices so that it may be submitted… More
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  • Bidirectional Long Short-Term Memory Network for Taxonomic Classification
  • Abstract Identifying and classifying Deoxyribonucleic Acid (DNA) sequences and their functions have been considered as the main challenges in bioinformatics. Advances in machine learning and Deep Learning (DL) techniques are expected to improve DNA sequence classification. Since the DNA sequence classification depends on analyzing textual data, Bidirectional Long Short-Term Memory (BLSTM) algorithms are suitable for tackling this task. Generally, classifiers depend on the patterns to be processed and the pre-processing method. This paper is concerned with a new proposed classification framework based on Frequency Chaos Game Representation (FCGR) followed by Discrete Wavelet Transform (DWT) and BLSTM. Firstly, DNA strings are transformed… More
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  • A Cloud-Based Secure Emergency Message Dissemination Scheme in Vehicular Adhoc Networks
  • Abstract The Internet of vehicles and vehicular ad-hoc networks (VANET) offers numerous opportunities for managing the transportation problems effectively. The high mobility and wireless communication in VANET lead to adequate network topology modifications, resulting in network instability and insecure data communication. With an unsteady flow of traffic, vehicles are unevenly distributed in the geographical areas in practice. A new type 2 fuzzy logic-based secure clustering (T2FLSC) with cloud-based data dissemination scheme called the T2FLSC-CDD model for the VANET has been introduced for resolving this issue. The vehicles are dynamically clustered by the use of the T2FLSC technique, which elects the CHs… More
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  • Software Information Hiding Algorithm Based on Palette Icon of PE File
  • Abstract PE (Portable executable) file is a standard format for executable file and is applied extensively. PE file has diversity, uncertainty of file size, complexity of file structure and singleness of file format, which make PE file easy to be a carrier of information hiding, especially for that of large hiding capacity. A novel software information hiding algorithm is proposed, which makes full use of display characteristics of palette icon of portable executable file. In this algorithm, the information is embedded into the transparent area of the icon by taking advantage of the redundant color items in the palette. The experimental… More
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  • Robust Node Localization with Intrusion Detection for Wireless Sensor Networks
  • Abstract Wireless sensor networks comprise a set of autonomous sensor nodes, commonly used for data gathering and tracking applications. Node localization and intrusion detection are considered as the major design issue in WSN. Therefore, this paper presents a new multi-objective manta ray foraging optimization (MRFO) based node localization with intrusion detection (MOMRFO-NLID) technique for WSN. The goal of the MOMRFO-NLID technique is to optimally localize the unknown nodes and determine the existence of intrusions in the network. The MOMRFO-NLID technique encompasses two major stages namely MRFO based localization of nodes and optimal Siamese Neural Network (OSNN) based intrusion detection. The OSNN… More
  •   Views:550       Downloads:308        Download PDF
  • Federated Learning for Privacy-Preserved Medical Internet of Things
  • Abstract Healthcare is one of the notable areas where the integration of the Internet of Things (IoT) is highly adopted, also known as the Medical IoT (MIoT). So far, MIoT is revolutionizing healthcare because it provides many advantages for the benefit of patients and healthcare personnel. The use of MIoT is becoming a booming trend, generating a large amount of IoT data, which requires proper analysis to infer meaningful information. This has led to the rise of deploying artificial intelligence (AI) technologies, such as machine learning (ML) and deep learning (DL) algorithms, to learn the meaning of this underlying medical data,… More
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  • Intelligent Computing and Control Framework for Smart Automated System
  • Abstract This paper presents development and analysis of different control strategies for smart automated system. The dynamic role of an electrical motor and sensor interfacing with wireless module becomes an essential element in a smart agriculture system to monitor various environmental parameters. The various key parameters such as temperature, humidity, air pressure, soil health and solar radiation are widely used to analyze the growth of plants and soil health based on different climate conditions. However, the smart development of an automatic system to measure these vital parameters provides a feasible approach and helps the farmers to monitor their crops productivity. In… More
  •   Views:475       Downloads:305        Download PDF
  • Social Networks Fake Account and Fake News Identification with Reliable Deep Learning
  • Abstract Recent developments of the World Wide Web (WWW) and social networking (Twitter, Instagram, etc.) paves way for data sharing which has never been observed in the human history before. A major security issue in this network is the creation of fake accounts. In addition, the automatic classification of the text article as true or fake is also a crucial process. The ineffectiveness of humans in distinguishing the true and false information exposes the fake news as a risk to credibility, democracy, logical truth, and journalism in government sectors. Besides, the automatic fake news or rumors from the social networking sites… More
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  • Three to Six Phase Power Converter with Partial Resonant AC Link
  • Abstract A six phase system is a balanced multiphase system that can be replace, in certain applications, the three phase system and the application becomes much fault tolerant. In the six phase system a phase angle of 60 degrees is maintained between the phases. Handling power with more number of phases reduces the maximum current in each of the phase in the system. In this work a six phase balanced AC output is derived from a three phase AC source. The proposed system uses a resonant single phase AC link driven by a three phase bidirectional converter unit AC link is… More
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  • Improved Energy Based Multi-Sensor Object Detection in Wireless Sensor Networks
  • Abstract Wireless Sensor Networks (WSNs) are spatially distributed to independent sensor networks that can sense physical characteristics such as temperature, sound, pressure, energy, and so on. WSNs have secure link to physical environment and robustness. Data Aggregation (DA) plays a key role in WSN, and it helps to minimize the Energy Consumption (EC). In order to have trustworthy DA with a rate of high aggregation for WSNs, the existing research works have focused on Data Routing for In-Network Aggregation (DRINA). Yet, there is no accomplishment of an effective balance between overhead and routing. But EC required during DA remained unsolved. The… More
  •   Views:448       Downloads:274       Cited by:2        Download PDF
  • Hybrid Microgrid based on PID Controller with the Modified Particle Swarm Optimization
  • Abstract Microgrids (MG) are distribution networks encompassing distributed energy sources. As it obtains the power from these resources, few problems such as instability along with Steady-State (SS) issues are noticed. To address the stability issues, that arise due to disturbances of low magnitude. Small-Signals Stability (SSS) becomes mandatory in the network. Convergence at local optimum is one of the major issues noticed with the existing optimization algorithms. This paper proposes a detailed model of SSS in Direct Current (DC)-Alternate Current (AC) Hybrid MG (HMG) using Proportional Integral and Derivative Controller (PIDC) tuned with Modified Particle Swarm Optimization (MPSO) algorithm to alleviate… More
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  • Requirements Engineering: Conflict Detection Automation Using Machine Learning
  • Abstract The research community has well recognized the importance of requirement elicitation. Recent research has shown the continuous decreasing success rate of IS projects in the last five years due to the complexity of the requirement conflict refinement process. Requirement conflict is at the heart of requirement elicitation. It is also considered the prime reason for deciding the success or failure of the intended Information System (IS) project. This paper introduces the requirements conflict detection automation model based on the Mean shift clustering unsupervised machine learning model. It utilizes the advantages of Artificial Intelligence in detecting and classifying the requirement conflicts… More
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  • Multi-Domain Deep Convolutional Neural Network for Ancient Urdu Text Recognition System
  • Abstract Deep learning has achieved magnificent success in the field of pattern recognition. In recent years Urdu character recognition system has significantly benefited from the effectiveness of the deep convolutional neural network. Majority of the research on Urdu text recognition are concentrated on formal handwritten and printed Urdu text document. In this paper, we experimented the Challenging issue of text recognition in Urdu ancient literature documents. Due to its cursiveness, complex word formation (ligatures), and context-sensitivity, and inadequate benchmark dataset, recognition of Urdu text from the literature document is very difficult to process compared to the formal Urdu text document. In… More
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  • The Intelligent Trajectory Optimization of Multistage Rocket with Gauss Pseudo-Spectral Method
  • Abstract The rapid developments of artificial intelligence in the last decade are influencing aerospace engineering to a great extent and research in this context is proliferating. In this paper, the trajectory optimization of a three-stage launch vehicle in the powering phase subject to the sun-synchronous orbit is considered. To solve the optimal control problem, the Gauss pseudo-spectral method (GPM) is used to transform the optimization model to a nonlinear programming (NLP) problem and sequential quadratic programming is applied to find the optimal solution. However, the sensitivity of the initial guess may cost the solver significant time to do the Newton iteration… More
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  • Interference-Aware Transmission Scheduling for Internet of Vehicles
  • Abstract Next-generation Intelligent Transportation Systems (ITS) use wirelessly connected vehicles for safety and non-safety applications such as autonomous driving, cooperative awareness, route guidance and multimedia transmissions. This network known as the Internet of Vehicles (IoVs) suffers from many challenges such as collisions due to hidden terminals, and interference from simultaneously transmitting vehicles. Moreover, the packet reception ratio of transmissions between vehicles is significantly reduced at high vehicle densities and severe fading scenarios. As safety applications require periodic broadcast of safety messages from each vehicle to all other vehicles in the neighborhood, the development of an efficient medium access technique is a… More
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  • Face Recognition System Using Deep Belief Network and Particle Swarm Optimization
  • Abstract Facial expression for different emotional feelings makes it interesting for researchers to develop recognition techniques. Facial expression is the outcome of emotions they feel, behavioral acts, and the physiological condition of one’s mind. In the world of computer visions and algorithms, precise facial recognition is tough. In predicting the expression of a face, machine learning/artificial intelligence plays a significant role. The deep learning techniques are widely used in more challenging real-world problems which are highly encouraged in facial emotional analysis. In this article, we use three phases for facial expression recognition techniques. The principal component analysis-based dimensionality reduction techniques are… More
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  • Overhauled Approach to Effectuate the Amelioration in EEG Analysis
  • Abstract Discovering the information about several disorders prevailing in brain and neurology is by no means a new scientific technique. A neurological disorder of any human being can be analyzed using EEG (Electroencephalography) signal from the electrode’s output. Epilepsy (spontaneous recurrent seizure) detection is usually carried out by the physicians using a visual scanning of the signals produced by EEG, which is onerous and may be inaccurate. EEG signal is often used to determine epilepsy, for its merits, such as non-invasive, portable, and economical, can exhibit superior temporal tenacity. This paper surveys the existing artifact removal methods. It puts a new-fangled… More
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  • Kidney Tumor Segmentation Using Two-Stage Bottleneck Block Architecture
  • Abstract Cases of kidney cancer have shown a rapid increase in recent years. Advanced technology has allowed bettering the existing treatment methods. Research on the subject is still continuing. Medical segmentation is also of increasing importance. In particular, deep learning-based studies are of great importance for accurate segmentation. Tumor detection is a relatively difficult procedure for soft tissue organs such as kidneys and the prostate. Kidney tumors, specifically, are a type of cancer with a higher incidence in older people. As age progresses, the importance of having diagnostic tests increases. In some cases, patients with kidney tumors may not show any… More
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  • A Novel Hybrid Deep Learning Framework for Intrusion Detection Systems in WSN-IoT Networks
  • Abstract With the advent of wireless communication and digital technology, low power, Internet-enabled, and reconfigurable wireless devices have been developed, which revolutionized day-to-day human life and the economy across the globe. These devices are realized by leveraging the features of sensing, processing the data and nodes communications. The scale of Internet-enabled wireless devices has increased daily, and these devices are exposed to various cyber-attacks. Since the complexity and dynamics of the attacks on the devices are computationally high, intelligent, scalable and high-speed intrusion detection systems (IDS) are required. Moreover, the wireless devices are battery-driven; implementing them would consume more energy, weakening… More
  •   Views:477       Downloads:289        Download PDF
  • A Machine-Learning Framework to Improve Wi-Fi Based Indoorpositioning
  • Abstract The indoor positioning system comprises portable wireless devices that aid in finding the location of people or objects within the buildings. Identification of the items is through the capacity level of the signal received from various access points (i.e., Wi-Fi routers). The positioning of the devices utilizing some algorithms has drawn more attention from the researchers. Yet, the designed algorithm still has problems for accurate floor planning. So, the accuracy of position estimation with minimum error is made possible by introducing Gaussian Distributive Feature Embedding based Deep Recurrent Perceptive Neural Learning (GDFE-DRPNL), a novel framework. Novel features from the dataset… More
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  • Semantic Annotation of Land Cover Remote Sensing Images Using Fuzzy CNN
  • Abstract This paper presents a novel fuzzy logic based Convolution Neural Network intelligent classifier for accurate image classification. The proposed approach employs a semantic class label model that classifies the input land cover images into a set of semantic categories and classes depending on the content. The intelligent feature selection algorithm selects the prominent attributes from the given data set using weighted attribute functions and uses fuzzy logic to build the rules based on the membership values. To annotate remote sensing images, the CNN method effectively creates semantics and categorises images. The decision manager then integrates the fuzzy logic rules with… More
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  • Optimized Compressive Sensing Based ECG Signal Compression and Reconstruction
  • Abstract In wireless body sensor network (WBSN), the set of electrocardiograms (ECG) data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient. However, due to the size of the ECG data, the performance of the signal compression and reconstruction is degraded. For efficient wireless transmission of ECG data, compressive sensing (CS) frame work plays significant role recently in WBSN. So, this work focuses to present CS for ECG signal compression and reconstruction. Although CS minimizes mean square error (MSE), compression rate and reconstruction probability of the CS is… More
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  • Achieving State Space Reduction in Generated Ajax Web Application State Machine
  • Abstract The testing of Ajax (Asynchronous JavaScript and XML) web applications poses novel challenges for testers because Ajax constructs dynamic web applications by using Asynchronous communication and run time Document Object Model (DOM) manipulation. Ajax involves extreme dynamism, which induces novel kind of issues like state explosion, triggering state changes and unreachable states etc. that require more demanding web-testing methods. Model based testing is amongst the effective approaches to detect faults in web applications. However, the state model generated for an Ajax application can be enormous and may be hit by state explosion problem for large number of user action based… More
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  • Sustainable Waste Collection Vehicle Routing Problem for COVID-19
  • Abstract COVID-19 pandemic has imposed many threats. One among them is the accumulation of waste in hospitals. Waste should be disposed regularly and safely using sustainable methods. Sustainability is self development with preservation of society and its resources. The main objective of this research is to achieve sustainability in waste collection by minimizing the cost factor. Minimization of sustainable-cost involves minimization of three sub-components – total travel-cost representing economical component, total emission-cost representing environmental component and total driver-allowance-cost representing social component. Most papers under waste collection implement Tabu search algorithm and fail to consider the environmental and social aspects involved. We… More
  •   Views:514       Downloads:295       Cited by:1        Download PDF
  • A Novel Method of User Identity Recognition Based on Finger Trajectory
  • Abstract User identity recognition is the key shield to protect users’ privacy data from disclosure and embezzlement. The user identity of mobile devices such as mobile phones mainly includes fingerprint recognition, nine-grid password, face recognition, digital password, etc. Due to the requirements of computing resources and convenience of mobile devices, these verification methods have their own shortcomings. In this paper, a user identity recognition technology based on finger trajectory is proposed. Based on the analysis of the users’ finger trajectory data, the feature of the user's finger movement trajectory is extracted to realize the identification of the user. Also, in this… More
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  • Political Ideology Detection of News Articles Using Deep Neural Networks
  • Abstract Individuals inadvertently allow emotions to drive their rational thoughts to predetermined conclusions regarding political partiality issues. Being well-informed about the subject in question mitigates emotions’ influence on humans’ cognitive reasoning, but it does not eliminate bias. By nature, humans tend to pick a side based on their beliefs, personal interests, and principles. Hence, journalists’ political leaning is defining factor in the rise of the polarity of political news coverage. Political bias studies usually align subjects or controversial topics of the news coverage to a particular ideology. However, politicians as private citizens or public officials are also consistently in the media… More
  •   Views:521       Downloads:331        Download PDF
  • Deep Embedded Fuzzy Clustering Model for Collaborative Filtering Recommender System
  • Abstract The increasing user of Internet has witnessed a continued exploration in applications and services that can bring more convenience in people's life than ever before. At the same time, with the exploration of online services, the people face unprecedented difficulty in selecting the most relevant service on the fly. In this context, the need for recommendation system is of paramount importance especially in helping the users to improve their experience with best value-added service. But, most of the traditional techniques including collaborative filtering (CF) which is one of the most successful recommendation technique suffer from two inherent issues namely, rating… More
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  • Efficient Urban Green Space Destruction and Crop Stress Yield Assessment Model
  • Abstract Remote sensing (RS) is a very reliable and effective way to monitor the environment and landscape changes. In today’s world topographic maps are very important in science, research, planning and management. It is quite possible to detect the changes based on RS data which is obtained at two different times. In this paper, we propose an optimal technique that handles problems like urban green space destruction and detection of crop stress assessment. Firstly, the optimal preprocessing is performed on the given RS dataset, for image enhancement using geometric correction and image registration. Secondly, we propose the improved cat swarm optimization… More
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  • To Control Diabetes Using Machine Learning Algorithm and Calorie Measurement Technique
  • Abstract Because of the increasing workload, people are having several clinical examinations to determine their health status, resulting in limited time. Here, we present a healthful consuming device based on rule mining that can modify your parameter dependency and recommend the varieties of meals that will boost your fitness and assist you to avoid the types of meals that increase your risk for sicknesses. Using the meals database, the data mining technique is useful for gathering meal energy from breakfast, after breakfast, lunch, after lunch, dinner, after dinner, and bedtime for ninety days. The purpose of this study is to determine… More
  •   Views:418       Downloads:388        Download PDF
  • Selective Cancellable Multi-Biometric Template Generation Scheme Based on Multi-Exposure Feature Fusion
  • Abstract This article introduces a new cancellable multi-biometric system based on the combination of a selective encryption method and a deep-learning-based fusion technology. The biometric face image is treated with an automatic face segmentation algorithm (Viola-Jones), and the image of the selected eye is XORed with a PRNG (Pseudo Random Number Generator) matrix. The output array is used to create a primary biometric template. This process changes the histogram of the selected eye image. Arnold’s Cat Map is used to superimpose the PRN pixels only on the pixels of the primary image. Arnold’s cat map deformed eyes are encrypted using the… More
  •   Views:673       Downloads:367        Download PDF
  • Forward Flight Performance Analysis of Supercritical Airfoil in Helicopter Main Rotor
  • Abstract In this research, the aerodynamic performance and flow characteristics of NASA SC (2)-0714 airfoil and HH02 airfoil in the helicopter main rotor are evidently analyzed. The supercritical airfoil is used in the aircraft for attaining better transonic and high-speed flow characteristics. Moreover, a specialized helicopter airfoil called HH02 is used in the Apache helicopter rotor for increasing the operational speed. As most of the high-speed helicopters are using four-bladed main rotor configuration, it is analyzed with prior attention. The lift and thrust act in different directions for the forward phase of the flight whereas the lift and thrust act in… More
  •   Views:381       Downloads:301        Download PDF
  • Android Malware Detection Based on Feature Selection and Weight Measurement
  • Abstract With the rapid development of Android devices, Android is currently one of the most popular mobile operating systems. However, it is also believed to be an entry point of many attack vectors. The existing Android malware detection method does not fare well when dealing with complex and intelligent malware applications, especially those based on feature detection systems which have become increasingly elusive. Therefore, we propose a novel feature selection algorithm called frequency differential selection (FDS) and weight measurement for Android malware detection. The purpose is to solve the shortcomings of the existing feature selection algorithms in detection and to filter… More
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  • VANET: Optimal Cluster Head Selection Using Opposition Based Learning
  • Abstract

    Traffic related accidents and route congestions remain to dwell significant issues in the globe. To overcome this, VANET was proposed to enhance the traffic management. However, there are several drawbacks in VANET such as collision of vehicles, data transmission in high probability of network fragmentation and data congestion. To overcome these issues, the Enhanced Pigeon Inspired Optimization (EPIO) and the Adaptive Neuro Fuzzy Inference System (ANFIS) based methods have been proposed. The Cluster Head (CH) has been selected optimally using the EPIO approach, and then the ANFIS has been used for updating and validating the CH and also for enhancing… More

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  • Insider Threat Detection Based on NLP Word Embedding and Machine Learning
  • Abstract The growth of edge computing, the Internet of Things (IoT), and cloud computing have been accompanied by new security issues evolving in the information security infrastructure. Recent studies suggest that the cost of insider attacks is higher than the external threats, making it an essential aspect of information security for organizations. Efficient insider threat detection requires state-of-the-art Artificial Intelligence models and utility. Although significant have been made to detect insider threats for more than a decade, there are many limitations, including a lack of real data, low accuracy, and a relatively low false alarm, which are major concerns needing further… More
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  • Multi-Model CNN-RNN-LSTM Based Fruit Recognition and Classification
  • Abstract Contemporary vision and pattern recognition issues such as image, face, fingerprint identification, and recognition, DNA sequencing, often have a large number of properties and classes. To handle such types of complex problems, one type of feature descriptor is not enough. To overcome these issues, this paper proposed a multi-model recognition and classification strategy using multi-feature fusion approaches. One of the growing topics in computer and machine vision is fruit and vegetable identification and categorization. A fruit identification system may be employed to assist customers and purchasers in identifying the species and quality of fruit. Using Convolution Neural Network (CNN), Recurrent… More
  •   Views:525       Downloads:292       Cited by:5        Download PDF
  • Integrated Renewable Smart Grid System Using Fuzzy Based Intelligent Controller
  • Abstract In high power medium voltage applications, the utilization of 5-H Bridge Multi-Level Inverter (MLI) has grown vastly in recent years. The 5-H Bridge MLI can effectively control link voltage as well as power factor. However, the inverter imparts harmonics owing to the high switching frequency. Hence Inductance Capacitance Inductance (LCL) filter is implemented at its output to mitigate harmonics in presence of non-linear load. It’s of highly important to choose LCL parameters wisely in order to attain good filtering effect. This work investigates the application of 5-H Bridge MLI with LCL filter at the output for efficient integration of renewable… More
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  • Enhanced Mesh Network Using Novel Forgotten Polynomial Algorithm for Pharmaceutical Design
  • Abstract The molecular structures are modelled as graphs which are called the molecular graphs. In these graphs, each vertex represents an atom and each edge denotes covalent bond between atoms. It is shown that the topological indices defined on the molecular graphs can reflect the chemical characteristics of chemical compounds and drugs. A large number of previous drug experiments revealed that there are strong inherent connections between the drug’s molecular structures and the bio-medical and pharmacology characteristics. The forgotten topological index is introduced to be applied into chemical compound and drug molecular structures, which is quite helpful for medical and pharmaceutical… More
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