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A Quantitative Analysis of Trading Strategy Performance Over Ten Years
This study conducts a comparative analysis of two trading strategies over a ten-year period to assess their profitability and risk. Strategy 1 operates on a simple buy at close and sell at open principle, while Strategy 2 trades only when the closing price is above the 200-day moving average, introducing a conditional filter for market entry. Through the evaluation of performance metrics including total PNL, drawdown, standard deviation, and Sharpe ratio, the research highlights the differences in risk and return between the strategies. Results indicate Strategy 1 achieves higher profitability but at the cost of greater risk, as shown by larger drawdowns. Conversely, Strategy 2's conditional approach yields slightly lower returns but demonstrates a superior risk-adjusted performance. The findings emphasize the significance of risk management and the potential benefits of conditional filters in trading strategies, offering valuable insights for traders and investors in making informed strategy selections. 2024 IEEE. -
A real time fog computing applications their privacy issues and solutions
Edge Computing (EC) has brought cloud technology to the channel's edge. It inherits some qualities from cloud services, but it also has some distinctive features such as geo-distribution, network connectivity, and reduced power. Along with the genetic inheritance, it also acquires the issues and concerns cloud computing services, such as renewable energy and resource allocation. This work provides a critical analysis of the fog architectural design in terms of security. Since 2018, the state of the artwork has been critically analyzed in terms of security mechanisms and security threats. The existing security methods are classified based on the security objectives they achieved. It would provide a complete and coherent difference between both the security areas investigated and those that have not. 2021 IEEE. -
A Reconfigurable Multilevel Inverters with Minimal Switches for Battery Charging and Renewable Energy Applications
In recent years, classical inverters such as the H-bridged cascaded multilevel inverter, flying capacitor, and flying capacitor multilevel inverter have contributed in electric vehicle and non-conventional energy applications. Due to higher switching and conduction losses, as well as a greater number of power switches and driver circuits, conventional multilevel inverters do not achieve the highest performance. To obtain higher performance while reducing power losses and total harmonic distortion, individual switches are controlled by logic gates. In this proposed work, one of the inverters is considered symmetrical voltage another is asymmetrical voltage for implementing these effective topologies. The proposed single-phase seven-level voltage output and current for both symmetric and asymmetric multilevel inverters are employed to test the intended computation. The MATLAB/Simulink tool is used to implement and investigate the various parameters of proposed topologies. 2022 IEEE. -
A Relative Analysis on the Spotting of Cardiovascular Disease Employing Machine Learning Techniques
Heart is one of the significant segments in the human body since it powers blood to the all the pieces of the body. Blood courses through the vein. Cardiovascular sickness is corresponded with the blockage of vein. The sign of heart sickness depends whereupon condition is impacting an individual. The term coronary illness is ordinarily utilized instead of cardiovascular infection. Dilated cardiomyopathy, Heart failure, Arrhythmia, Pulmonary stenosis, Mitral regurgitation, Coronary artery disease, Myocardial infraction, Mitral valve prolapse, Hypertrophic cardiomyopathy are the sorts of coronary illness. The several machine learning techniques are analyzed to spot heart disease. This paper gives relative investigation of coronary illness expectation utilizing machine learning. 2021 IEEE. -
A Reliable Method of Predicting Water Quality Using Supervised Machine Learning Model
Water contributes to around 70% of the world's exterior and is perhaps the primary source essential to supporting life. The rapid growth of urban and industrial geographies has prompted a disintegration of the quality of water at a concerning pace, bringing about nerve-racking sicknesses. Water quality has been expectedly assessed through costly and tedious lab and measurable examinations, which render the contemporary thought of continuous observing disputable. The disturbing results of helpless water quality require an elective strategy, which is speedier and more economical. With this inspiration, this exploration investigates a progression of administered AI calculations to appraise the Water Quality Index (WQI), which acts as a unique attribute to express the generic nature of water. The proposed system utilizes multiple info boundaries, specifically, temperature, pH, dissolved O2 concentration, and all-out broken down molecules. Of the multitude of utilized regression calculations and slope boosting, the water quality index can be expected most productively, with an MSE of 0.27. The propositioned study accomplishes acceptable precision by utilizing a minimum number of features to improve the chances of it getting implemented progressively in water quality recognition frameworks. 2022 IEEE. -
A Review and Comparative Study on Surface Vehicle Path Planning Algorithm
Autonomous Surface Vehicles (ASV) is very active area of robotics. There are so many projects are going on and doing research on monitoring and surveying on environment. There are significant studies on AS V's reverie, sea and coastal environments. Many algorithms are used by different researchers for path planning or route planning. Programmed recreation projects of boat route can be a useful asset for operational arranging and Layout investigations of conduits. In such a recreation framework the key undertakings of self-ruling course finding, and impact evasion are done by a reproduction program itself without or minimum interaction of a human pilot. That is from numerous points of view like programmed route frameworks in that they are intended to do self-governing route securely and proficiently without the requirement for Human intercession or to offer exhortation to the guide in regard to the best game-plan to take in certain circumstances. There are two key errands of programmed transport route frameworks: course finding and Collision evasion. 2021 ACM. -
A Review of Algorithms for Mental Stress Analysis Using EEG Signal
Mental stress is an enduring problem in human life. The level of stress increases exponentially with an increase in the complexity of work life. Hence, it is imperative to understand the causes of stress, a prerequisite of which is the ability to determine the level of stress. Electroencephalography (EEG) has been the most widely used signal for understanding stress levels. However, EEG signal is useful only when appropriate algorithms can be used to extract the properties relevant to stress analysis. This paper reviews algorithms for preprocessing, feature extraction and learning, and classification of EEG, and reports on their advantages and disadvantages for stress analysis. This review will help researchers to choose the most effective pipeline of algorithms for stress analysis using EEG signals. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Review of biophilic design at Kuttikattoor school for the children
The objective of this paper is the creation of a school to help the children of Kutikatoor live a more accessible and simpler life: students in the concentration range from 4 to 16 (Play School - - Grade 10). The proposed location, which is around 18.3 acres in size, lies in Kuttikattoor, Kozhikode, and Kerala. The land is surrounded by greenery and situated in a mountainous area. The study will concentrate on how biophilic architecture and design may enhance students' lives. This paper will discuss how biophilic design may benefit schools by creating circulation and spatial connections between the built and natural environments. The biophilic design can have quantifiable beneficial effects on student performance and well-being by including natural components. It is necessary to thoroughly analyze the biophilic design in relation to the learning environment for students, using ideas of ecological, visual, and spatial integration. By fostering a soothing atmosphere, lowering anxiety, and boosting physical fitness, biophilic design, which incorporates natural light, greenery, and nature vistas, can increase attention, decrease stress, stimulate creativity, and improve academic accomplishment. The school's design will be implemented by incorporating the architectural design into the contoured regions and using the idea of biophilic design patterns. Depending on the climate and the site's orientation, the design will be implemented such that locally accessible materials are employed in a hilly area. This detailed analysis of the case study and literature review for the school design will help us to design and conceptualize as an architect. Further, the study will also emphasize biophilic design which is aligned with the built environment in school design. The Authors, published by EDP Sciences. -
A review of buoyancy driven underwater gliders
In the past few years, several techniques and approaches have been developed by researchers for the ocean survey. An autonomous underwater vehicle primarily known as the glider is vastly used for oceanographic study and survey. With the help of these vehicles now it possible to have a study on the effects of pesticides, metal, biological toxins, or chemicals on the living organisms of the sea. Additionally, monitoring of threats such as biological weapons, radioactive leakage, and detection of mines is a very important parameter for keeping safety in check. Considering these parameters autonomous vehicles primarily known as glider are vastly used by oceanographers as they are relatively inexpensive, reusable, and have long mission durations. Such vehicle uses advanced sensors to perform automated monitoring and fast data acquisition. Since their inception in the 1980s, there have been considerable developments that have led to the augmentation of scientifically and commercially focused products. A comprehensive analysis of various underwater gliders and their working principle has been done here, emphasizing their architecture and working capabilities. 2022 Author(s). -
A Review of Channel Estimation Mechanisms in Wireless Communication Networks
The fluctuating nature of wireless networks influences network performance. Estimation of channel condition is essential for many reasons. The accurate estimation and prediction help to improve the performance, like better rate adaptation in Wi-Fi, improved video streaming, reduce energy consumption, and better scheduling. There are many different approaches introduced past two decades. In this paper, we are focusing on providing a brief review of different channel estimation approaches and their importance in improving performance. 2021 IEEE. -
A Review of Deep Learning Methods in Cervical Cancer Detection
Cervical cancer is one of the most widespread and lethal malignancy that affects women aged 25 to 55 across the globe. Early detection of cervical cancer reduces burden of living and mortality drastically. Cervical cancer is caused through human papillomavirus transmitted sexually. Since the hereditary aspect is absent in cervical cancer, it can be cured completely if diagnosed early. Cervix cell image analysis is gold standard for classifying cervical cancer. Also known as pap smear, this histopathological test can provide dependable, and accurate diagnostic support. The current study examines the most recent research breakthroughs in deep learning models to classify cervical cancer. Three benchmark datasets are comprehensively described. Selective key classification models were implemented and comparative analysis was conducted on their performance. The findings of this study will allow researchers, publishers, and professionals to examine developing research patterns. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
A Review of Optimization Algorithms Used in Proportional Integral Controllers (PID) for Automatic Voltage Regulators
The voltage in electrical grids is maintained at its nominal value by automatic voltage regulators (AVR). In AVR systems, proportional-integral-derivative (PID) is the most popular controllers due to their robust performance and simplicity. Controlling the parameters of proportional-integral-derivative (PID) controllers, which are used in AVR technology, is a nonlinear optimization problem. Optimization issues are of great importance to both the industrial and scientific worlds. A PID controller's objective function is designed to minimize the settling time, rise time, and overshoot of the step response of the resultant voltage. This paper presents the performance comparison of six optimization algorithms such as Enhanced Crow Search Algorithm (ECSA-PID), Slime Mould algorithm (SMA-PID), Future Search Algorithm (FSA-PID), Whale Optimization Algorithm (WOAPID), Equilibrium Optimizer (EO-PID) and Archimede's Optimization algorithm (AOA-PID) used in recent literatures. The Electrochemical Society -
A Review of Smart Grid Management Systems Using Machine Learning Algorithms for Efficient Energy Distribution
The smart grid is an intelligent electricity network that uses digital technology to improve the efficiency, reliability, and sustainability of power delivery. Machine learning is a type of artificial intelligence that can be used to analyze data and learn from it. This makes it a valuable tool for the smart grid, as it can be used to solve a variety of problems, such asorecasting energy demand, detecting, and preventing outages, optimizing power flows, managing distributed energy resources, ensuring grid security.In this article, we will review the use of machine learning in the smart grid. We will discuss the different machine learning algorithms that are being used, the challenges that need to be addressed, and the future of machine learning in the smart grid.. The Authors, published by EDP Sciences, 2024. -
A Review of the Detection of Pulmonary Embolism from Computed Tomography Images Using Deep Learning Methods
Medical imaging has been evolving at a steady pace generating enormous amounts of health data, and the use of deep learning (DL) has helped a great deal in processing the detailed data. Deep learning-based methods are used in different medical imaging tasks to detect and diagnose diseases. For example, medical imaging is used to diagnose pulmonary embolism (PE), a commonly occurring cardiovascular disease with high mortality and prevalence and a low diagnosis rate. According to medical experts, PE has resulted in many deaths because of missed diagnoses for the medical condition. Another critical aspect of the disease is the possibility of permanent lung damage if left untreated. The use of deep learning methods in medical imaging is attributed to their ability to use learning-based methods to process enormous amounts of data. However, there are some unique challenges in the detection of PE. PE is not specific in its clinical presentation and is easily ignored, making it difficult to diagnose. Deep learning-based detection methods help a great deal in the disease detection in miniature sub-branches of the alveoli, and images with noisy artifacts easily compared to manual diagnosis. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Review of Various Line Segmentation Techniques Used in Handwritten Character Recognition
Segmentation is a very critical stage in the character recognition process as the performance of any character recognition system depends heavily on the accuracy of segmentation. Although segmentation is a well-researched area, segmentation of handwritten text is still difficult owing to several factors like skewed and overlapping lines, the presence of touching, broken and degraded characters, and variations in writing styles. Therefore, researchers in this area are working continuously to develop new techniques for the efficient segmentation and recognition of characters. In the character recognition process, segmentation can be implemented at the line, word, and character level. Text line segmentation is the first step in the text/character recognition process. The line segmentation methods used in the character recognition of handwritten documents are presented in this paper. The various levels of segmentation which include line, word, and character segmentation are discussed with a focus on line segmentation. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Review on Artificial Intelligence Techniques for Multilingual SMS Spam Detection
With social networks increased popularity and smartphone technology advancements, Facebook, Twitter, and short text messaging services (SMS) have gained popularity. The availability of these low cost text-based communication services has implicitly increased the intrusion of spam messages. These spam messages have started emerging as an important issue, especially to short-duration mobile users such as aged persons, children, and other less skilled users of mobile phones. Unknowingly or mistakenly clicking the hyperlinks in spam messages or subscribing to advertisements puts them under threat of debiting their money from either the bank account or the balance of the network subscriber. Different approaches have been attempted to detect spam messages in the last decade. Many mobile applications have also evolved for spam detection in English, but still, there is a lack of performance. As English has been completely covered under natural language processing, other regional languages, such as Urdu and Hindi variants, have specific issues detecting spam messages. Mobile users suffer greatly from these issues, especially in multilingual countries like India. Thus, this paper critically reviews the artificial intelligence-based spam detection system. The review lists out the existing systems that use machine and deep learning techniques with their limitations, merits, and demerits. In addition, this paper covers the scope for future enhancements in natural language processing to efficiently prevent spam messages rather than detect spam messages. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Review on Condition Monitoring of Wind Turbines Using Machine Learning Techniques
This document examines the most up-to-date research on the application of machine learning (ML) techniques in monitoring the conditions of wind turbines. The focus is on classification methods, which are used to identify different types of faults. The analysis revealed that the majority of the research utilizes Supervisory Control and Data Acquisition (SCADA) information, with neural networks, support vector machines, and decision trees being the most prevalent machine learning algorithms. The review also identifies several areas for future research, such as the development of more robust ML models that can handle noisy data and the use of ML methods for prognosis (predicting future faults). The Authors, published by EDP Sciences, 2024. -
A Review on DC-DC Converters with Photovoltaic System in DC Micro Grid
Photovoltaic system is the low-cost source of electrical power in high solar energy regions. The benefits of PV system are like nonpolluting and minimum maintenance. Solar energy changes as per irradiance and temperature and also one factor which reduces the power output is the partial shading in the cells. Hence f o r th, various algo rith ms a r e p u t fo rth to obta in t h e maximum power f r o m t h e PV arrangement and dc-dc converters intend to regulate the supply. The concept of micro grid is emerging as an excellent solution for inter connecting renewable energy sources and loads. DC micro grid is a necessity in today's world. There is wide increase in usage of DC systems in commercial, residential and industrial systems. DC micro grids are dominant in reliability, control and efficiency. Direct current architectures will be used in demand in the future electrical distribution systems. This paper reviews on all above concepts to be used in DC micro grid for future DC applications. Published under licence by IOP Publishing Ltd. -
A Review on Deep Learning Algorithms in the Detection of Autism Spectrum Disorder
Autism spectrum disorder (ASD) is a neurodisorder that has an impact on how people interact and communicate with each other for the rest of their lives. Most autistic symptoms appear throughout the first two years of a child's life. This is why autism is called a behavioral disease. If you have a child with ASD, the problem starts in childhood and keeps going through adolescence and adulthood. Deep learning techniques are becoming more common in research on medical diagnosis. In this paper, there is an effort to see if convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory network (LSTM), and a fusion technique known as convolutional recurrent neural network (CRNN) can be used to detect ASD problems in a child, adolescents, and adults. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Review on EMG-based Pattern Identification Methods for Effective Controlling of Hand Prostheses
The ability of amputees to do daily duties is significantly restricted by upper limb amputation. The myoelectric prosthesis uses impulses from the surviving muscles in the stump to gradually restore function to such severed limbs. Such myosignals are unfortunately tedious and challenging to gather and employ. The process of transforming it into a user control signal after it has been acquired often consumes a significant amount of processing resources. By modifying machine learning strategies for pattern recognition, the factors that influence the traditional electromyography (EMG)-pattern identification approaches may be significantly minimized. Although more recent developments in intelligent pattern recognition algorithms could discern between a variety of degrees of freedom with high levels of accuracy, their usefulness in practical (amputee) applications was less obvious. This review paper examined how well various pattern recognition algorithms for hand prostheses performed. Finally, we discussed the current difficulties and offered some suggestions for future research in our paper's conclusion. 2023 IEEE.