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A review on feature selection algorithms
A large number of data are increasing in multiple fields such as social media, bioinformatics and health care. These data contain redundant, irrelevant or noisy data which causes high dimensionality. Feature selection is generally used in data mining to define the tools and techniques available for reducing inputs to a controllable size for processing and analysis. Feature selection is also used for dimension reduction, machine learning and other data mining applications. A survey of different feature selection methods are presented in this paper for obtaining relevant features. It also introduces feature selection algorithm called genetic algorithm for detection and diagnosis of biological problems. Genetic algorithm is mainly focused in the field of medicines which can be beneficial for physicians to solve complex problems. Finally, this paper concludes with various challenges and applications in feature selection. Springer Nature Singapore Pte Ltd 2019. -
A Review on Fish Skin-Derived Gelatin: Elucidating the Gelatin PeptidesPreparation, Bioactivity, Mechanistic Insights, and Strategies for Stability Improvement
Fish skin-derived gelatin has garnered significant attention recently due to its abundant availability and promising bioactive properties. This comprehensive review elucidates various intricacies concerning fish skin-derived gelatin peptides, including their preparation techniques, bioactive profiles, underlying mechanisms, and methods for stability enhancement. The review investigates diverse extraction methods and processing approaches for acquiring gelatin peptides from fish skin, emphasizing their impact on the peptide composition and functional characteristics. Furthermore, the review examines the manifold bioactivities demonstrated by fish skin-derived gelatin peptides, encompassing antioxidant, antimicrobial, anti-inflammatory, and anticancer properties, elucidating their potential roles in functional food products, pharmaceuticals, and nutraceuticals. Further, mechanistic insights into the functioning of gelatin peptides are explored, shedding light on their interactions with biological targets and pathways. Additionally, strategies aimed at improving the stability of gelatin peptides, such as encapsulation, modification, and integration into delivery systems, are discussed to extend the shelf life and preserve the bioactivity. Overall, this comprehensive review offers valuable insights into using fish skin-derived gelatin peptides as functional ingredients, providing perspectives for future research endeavors and industrial applications within food science, health, and biotechnology. 2024 by the authors. -
A Review on Flood Prediction Algorithms and A Deep Neural Network Model for Estimation of Flood Occurrence
Flood occurs as often as possible happens due to many environmental changes in our planet in the present years. The occurrence and damages caused by flood is very high. Major cause of flood is due to heavy rainfall which in turn increases the water level of the rivers and other water bodies. The various factors that play a major role in the occurrence of rainfall are rise in temperature, humidity level, dew point, pressure in and around the area of concern, wind speed, etc. In order to reduce the number of victims due to flood it is necessary to have a system to predict flood occurrence. In this paper, we classify and analyzed the various prediction algorithms which show usage of Deep Neural Network produces better results. In addition, a design model has been proposed to predict the flood by training the Deep Neural Network with the above-mentioned factors. 2020, Asian Research Association. All rights reserved. -
A Review On Geospatial Intelligence For Investigative Journalism
Throughout the ongoing Russian invasion of Ukraine, satellite images like the vast convoy of Russian military vehicles approaching the beleaguered Ukrainian city of Kyiv, Russian aircraft deployed at Zyabrovka, Belarus and many more such visuals have been in circulation and are being used as a tool to facilitate investigative journalistic studies. Such satellite-based images are one of the latest means of accessing vital data that can help in expanding the scope and impact of investigative journalism. Geospatial intelligence uses varied graphical content to convey information about the activities that occur on the surface of the earth. It includes colour and panchromatic (black and white) aerial photographs, multispectral or hyperspectral digital imagery, and products such as shaded relief maps or three-dimensional images produced from digital elevation models. The improving technology in geospatial spectra has broadened the scope of its usage to investigative journalism which lies at the core of this review paper. Some of the path-breaking journalistic stories that have come up in the past decade - imaging of the Uttarakhand landslide in 2021 using satellite images, coverage of the Fukushima nuclear plant since 2011, and 2021 tracking of Asia's border disputes emerging due to climate change and the satellite journalism built around the blockage of Suez canal in 2021 - showcase the potential that geospatial intelligence has in the domain of journalism. All four identified stories point out how we can practice satellite-based investigative studies, especially, for scrutinizing and comparing historical records regarding cross-border issues as well as the disappearance of pastures and forests in vast open lands. However, the arena of using geospatial intelligence, enabled by satellite images, remains underutilized and limited to specific journalistic organizations, based in a few countries. This exploratory review of the four mentioned journalistic accounts identifies the contexts where such efforts are feasible, methods that are required, sources that could be tapped, associated skill sets needed for its usage, the dynamics of such investigative approaches, and their scope and limitations. This review and the conclusions drawn from the above-mentioned cases provides a direction for journalists from small organizations and low income countries to explore the potential of satellite-based images in furthering their investigative reporting with a technological edge that persists to be sovereign in the geopolitical powerplay. Copyright 2022 by the International Astronautical Federation (IAF). All rights reserved. -
A Review on Influence of Cutting Fluid on Improving the Machinability of Inconel 718
Nickel-based superalloys are widely used in the production and manufacturing sectors that require processes or applications that endure or operate at very high superheating temperatures. With the properties of high tensile strength, high melting point, and lightweight structural arrangement of molecules within the alloy material composition makes it more suitable for industrial utilization in aerospace industries and marine applications. This review paper discusses the use of various coolant lubricants that improves the machinability of Inconel 718 based on parameters such as surface roughness and tool wear under the influence of cutting speed, feed rate, and depth of cut. The machine used for analysis is CNC milling machine which will be used for experimentation using ceramic inserts as end milling tool. Various cooling techniques such as hybrid cooling, flood emulsion cooling, minimum quantity lubrication, and cryogenic cooling are being summarized in this paper from various experimentations and conclusions of other authors. On the basis of review, the hybrid cooling technique is found to be better than other cooling techniques because of its ability to obtain long tool life and smoother surface finish on the workpiece. With the use of these reviewed data, further research for finding a more compatible and effective cooling lubricant has to be done by experimentation in order to obtain an improved machining process for Inconel 718 material. 2020, Springer Nature Singapore Pte Ltd. -
A review on metal nanoparticles from medicinal plants: Synthesis, characterization and applications
Plant extracts contain secondary metabolites which have the potential to act as reducing and stabilizing agents contributing to a greener and more efficient method to synthesize nanoparticles. Rapid growth of Nanotechnology has led to an increased demand in various fields. This review summarizes the use of potent medicinal plant extracts to synthesize metal nanoparticles, methods employed to characterize the properties of the nanoparticles and its application. Characterization of the nanoparticle based on its shape, size, chemical bonds, surface properties, hydrodynamic diameter and crystalline structure using techniques such as UV-Visible Spectroscopy, XRD (X-ray Diffraction), TEM (Transmission Electron Microscopy), SEM (Scanning Electron Microscopy), EDS (X-ray energy dispersive spectroscopy), DLS (Dynamic Light Scattering), Zeta Potential and FTIR (Fourier Transform-Infrared Spectroscopy) are elaborated. The synthesized metal nanoparticles have wide ranges of applications such as antimicrobial activity, antioxidative capability, anticancer effect, antidiabetic properties, plant growth enhancement, dye degradation effects and anti-larval properties. Recent advances in nanotechnology with special emphasis on plant metabolites provide an insight into their usage as plant-derived edible nanoparticles (PDNPs). Applications, limitations and future prospects of this technology have also been briefly discussed. 2021 Bentham Science Publishers. -
A review on power quality issues in electric vehicle interfaced distribution system and mitigation techniques
Electric vehicles (EV) penetration in the distribution systems is evident and intended to grow day by day. Power quality issues pop up in the distribution system with an increase in EV penetration. Distribution networks need to consider the power quality issues developed due to the penetration of EVs for planning and designing the system. The power quality issues, including voltage imbalance, total harmonic distortion, distribution transformer failure, and related issues, are anticipated due to EV penetration in distribution systems. Detailed review of power quality issues and mitigation techniques are detailed in this paper. Discussion on the effect of these power quality issues on the distribution systems and corresponding mitigation measures are detailed. Power quality impact mitigation techniques have been discussed recently, which exploits the bidirectional power flow of vehicle to grid vehicle to grid (V2G) and grid to vehicle grid-to-vehicle (G2V). Methods and methodologies that mitigate power quality problems in the EV penetrated distribution system is discussed. Bidirectional power flow during EV charging and discharging and power quality issues in this topology is detailed in this review paper. A discussion on future trends and different possible future research paradigms is discussed as the review's conclusion. 2022 Institute of Advanced Engineering and Science. All rights reserved. -
A review on prediction of cardiac arrest analysis in early stage
Cardiac arrest occurs as the heart muscle fails to contract properly, resulting in a sudden loss of blood supply. The ECG signal is one of the techniques for detecting cardiac electrical activity and is used to investigate heart block. In this paper different standardized work for early detection of cardiac arrest is described. Stages of ECG signal pre-processing involves denoised using digital filtering algorithms and extracting different features from clean ECG predicting cardiac arrest in early stage. Several other body parameters were also considered for this purpose. In this work denoising validation parameters were compared for showing effectiveness of the filtering algorithm and several body parameters and its implication on cardiac arrest was described. 2022 Author(s). -
A Review on Preprocessing Techniques for Noise Reduction in PET-CT Images for Lung Cancer
Cancer is one of the leading causes of death. According to World Health Organization, lung cancer is the most common cause of cancer deaths in 2020, with over 1.8 million deaths. Therefore, lung cancer mortality can be reduced with early detection and treatment. The components of early detection require screening and accurate detection of the tumor for staging and treatment planning. Due to the advances in medicine, nuclear medicine has become the forefront of precise lung cancer diagnosis. Currently, PET/CT is the most preferred diagnostic modality for lung cancer detection. However, variable results and noise in the imaging modalities and the lung's complexity as an organ have made it challenging to identify lung tumors from the clinical images. In addition, the factors such as respiration can cause blurry images and introduce other artifacts in the images. Although nuclear medicine is at the forefront of diagnosing, evaluating, and treating various diseases, it is highly dependent on image quality, which has led to many approaches, such as the fusion of modalities to evaluate the disease. In addition, the fusion of diagnostic modalities can be accurate when well-processed images are acquired, which is challenging due to different diagnostic machines and external and internal factors associated with lung cancer patients. The current works focus on single imaging modalities for lung cancer detection, and there are no specific techniques identified individually for PET and CT images, respectively, for attaining effective and noise-free hybrid imaging for lung cancer detection. Based on the survey, it has been identified that several image preprocessing filters are used for different noise types. However, for successful preprocessing, it is essential to identify the types of noise present in PET and CT images and the appropriate techniques that perform well for these modalities. Therefore, the primary aim of the review is to identify efficient preprocessing techniques for noise and artifact removal in the PET/CT images that can preserve the critical features of the tumor for accurate lung cancer diagnosis. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A review on quantum utility for secure authentication protocol towards cryptographic standard in quantum dot cellular automata
QCA, which stands for Quantum Dot-Cellular Automata, is a nanotechnology model that offers an alternative solution to the widely used CMOS technology. Unlike CMOS, QCA is a semiconductor-less technology that transmits information based on the charge of electrons and the electrostatic repulsion between them. This technology provides several advantages over CMOS, including higher device density, faster switching speed, and lower power consumption. When it comes to cryptographic applications, QCA circuits can be extremely useful. Both encryption and decryption processes can be implemented using logic circuits based on QCA. The research paper describes a basic method for generating ciphertext in QCA, which is useful in secure nano communication based on QCA. The paper discusses how to achieve secure authentication in encrypted communication using QCA. To evaluate the performance and test the proposed method, the researchers used the QCA Designer-2.0.3 tool. This is a software tool specifically designed for designing and simulating QCA circuits. It enables researchers to model and analyze the behavior of QCA-based systems, allowing them to evaluate the effectiveness and feasibility of their proposed encryption technique. Overall, the research paper aims to present a secure encryption method using QCA and demonstrates its implementation and testing using the QCA Designer-2.0.3 tool. By leveraging the unique properties of QCA, such as high device density and low power consumption, the researchers aim to provide a novel approach for secure nano communication and cryptographic applications. Taru Publications. -
A Review on Recent Scheduling Algorithms in the Cloud Environment
Cloud users and service providers are the leading players in the cloud computing environment. This environment comprises data centers, hosts, agents and virtual machines. The cloud users application of varied loads is leased on the providers resources. Scientific applications are large-scale complex workflow problems that demand more computing power. The cloud fulfills the workflow requirements of huge availability and increased computational power. One of the most crucial issues of cloud computing is scheduling tasks for the systems effective functioning. This paper reviews several existing task-scheduling techniques based on diverse metrics. This work will help the investigators to gain a better understanding of task scheduling techniques. In order to boost an algorithms performance, a few strategies are offered. 2023 American Institute of Physics Inc.. All rights reserved. -
A Review on Recent Trends in Biological Applications of Non-conjugated Polymer Dots
With the advancement of zero-dimensional carbon materials, carbon dots (CDs) have received immense attention owing to their exceptional optical properties, tailoring of size, and ease of functionalization. They have wide applications in fluorescent sensing, chemical sensing, bioimaging, photocatalysis, etc. Zero-dimensional polymer nanoparticles are called polymer dots (PDs) and are classified into conjugated and non-conjugated PDs based on their conjugated system. Non-conjugated polymer dots (NCPDs) do not have specific conjugated fluorophore groups, but they have superior chemical stability and water solubility than the conjugated PDs. The carbon core of NCPDs is surrounded by polymer chains containing ample functional groups such as C=O, N=O, and C=N, which are responsible for the luminescent PDs. NCPDs are less toxic, photostable, and biocompatible and are relevant in biological explorations in bioimaging, drug delivery, biosensing, etc. This mini-review provides a systematic overview of the inherent properties and the biological applications of NCPDs. It also emphasises the synergistic impacts on the optical performance of modified PDs and significant future research concerns. Graphical Abstract: [Figure not available: see fulltext.]. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
A Review on Rural Womens Entrepreneurship Using Machine Learning Models
Rural womens entrepreneurship has contributed significantly to the countrys economy. Entrepreneurship rates have fluctuated in recent years, according to a variety of reasons including economic, social, and cultural influences. Therefore, machine learning models are used to assess the features to make better business decisions. In this research paper, papers from 2009 to 2022 were studied and found that machine learning models are being used to improve womens entrepreneurship. In this paper, nine machine learning models have been described in detail which include multiple regression, lasso regression, logistic regression, decision tree, Naive Bayes, clustering, classification, deep learning, artificial neural network, etc. In the study of all these models, it was found how accurately this model has been used in womens entrepreneurship work. It has been observed that by using different machine learning models with the data acquired from rural entrepreneurship, women entrepreneurs may use a new way of understanding the dynamics of rural entrepreneurship. Various machine learning models have been studied to improve rural development for women working in rural areas. Thus, we have proposed a comparative study of various machine learning models to predict entrepreneurship-based data. The findings of this study may be used to assess how rural women entrepreneurs may change the decisions made in several domains, such as making use of different economic policies and promoting the long-term viability of women entrepreneurs for the countrys economic growth. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
A review on semiconductor nanoparticles in photovoltaic cells /
International Journal of Advanced Scientific Research And Management, Vol.4, Issue 4, pp.43-50, ISSN No: 2455-6378. -
A review on serverless architectures-Function as a service (FaaS) in cloud computing
Emergence of cloud computing as the inevitable IT computing paradigm, the perception of the compute reference model and building of services has evolved into new dimensions. Serverless computing is an execution model in which the cloud service provider dynamically manages the allocation of compute resources of the server. The consumer is billed for the actual volume of resources consumed by them, instead paying for the pre-purchased units of compute capacity. This model evolved as a way to achieve optimum cost, minimum configuration overheads, and increases the application's ability to scale in the cloud. The prospective of the serverless compute model is well conceived by the major cloud service providers and reflected in the adoption of serverless computing paradigm. This review paper presents a comprehensive study on serverless computing architecture and also extends an experimentation of the working principle of serverless computing reference model adapted by AWS Lambda. The various research avenues in serverless computing are identified and presented. Universitas Ahmad Dahlan. -
A Review on Synchronization and Localization of Devices in WSN
Wireless sensor networks are communication networks that deal with sensor devices that are wirelessly interconnected in order to collect and forward data between different environments. Network scaling of small sensor devices with all its limitations has a foolproof scope for future applications. The advantage of minimal infrastructural cost and applicability within challenging environments make it an attractive choice. Statistics have been shown to prove the demand for research for synchronization and localization as a research problem. WSNs are capable of dynamically building virtual infrastructure and getting synchronized with the rhythm of communication setup. Limitations in the amount of energy that can be utilized make it a necessity for the networks to be more optimal in terms of energy consumption. These challenges necessitate the need to study and analyze the recent advancements implemented in approaching synchronization and localization problems. This paper reviews recent research proposals and methodologies to identify related attributes and their relation to the system. A detailed comparative study is conducted to identify relevant patterns that influence the performance of the networks in terms of energy consumption. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A review on synthetic approaches towards kavalactones
Kavalactones are classes of ?-pyrone and 5,6-dihydropyrone derivatives showing various biological activities, and numerous approaches have been reported for the preparation of these molecules. In this review, we discuss the different synthetic approaches towards these naturally occurring lactones, in both racemic and enantiomerically pure forms, that have been reported in the literature to date. It is hoped that this review will assist researchers in the development of additional and efficient synthetic routes towards kavalactones. 1 Introduction 2 Synthetic Approaches for the Preparation of Kavalactones 3 Conclusion. 2021. Thieme. -
A review on the efficacy of artificial intelligence for managing anxiety disorders
Anxiety disorders are psychiatric conditions characterized by prolonged and generalized anxiety experienced by individuals in response to various events or situations. At present, anxiety disorders are regarded as the most widespread psychiatric disorders globally. Medication and different types of psychotherapies are employed as the primary therapeutic modalities in clinical practice for the treatment of anxiety disorders. However, combining these two approaches is known to yield more significant benefits than medication alone. Nevertheless, there is a lack of resources and a limited availability of psychotherapy options in underdeveloped areas. Psychotherapy methods encompass relaxation techniques, controlled breathing exercises, visualization exercises, controlled exposure exercises, and cognitive interventions such as challenging negative thoughts. These methods are vital in the treatment of anxiety disorders, but executing them proficiently can be demanding. Moreover, individuals with distinct anxiety disorders are prescribed medications that may cause withdrawal symptoms in some instances. Additionally, there is inadequate availability of face-to-face psychotherapy and a restricted capacity to predict and monitor the health, behavioral, and environmental aspects of individuals with anxiety disorders during the initial phases. In recent years, there has been notable progress in developing and utilizing artificial intelligence (AI) based applications and environments to improve the precision and sensitivity of diagnosing and treating various categories of anxiety disorders. As a result, this study aims to establish the efficacy of AI-enabled environments in addressing the existing challenges in managing anxiety disorders, reducing reliance on medication, and investigating the potential advantages, issues, and opportunities of integrating AI-assisted healthcare for anxiety disorders and enabling personalized therapy. Copyright 2024 Das and Gavade. -
A review on the electrochemical behavior of graphenetransition metal oxide nanocomposites for energy storage applications
Electrochemical energy storage devices like supercapacitors and rechargeable batteries require an improvement in their performance at the commercial level. Among them, supercapacitors are beneficial in sustainable nanotechnologies for energy conversion and storage systems and have high power rates compared to batteries. High chemical and mechanical stability, huge electrical conductivity, and high specific surface area have been beneficial for selecting graphene as a supercapacitor electrode material. The excellent properties of transition metal oxides are accountable for the application in the field of energy storage. The synergistic effects of the composites of graphene derivatives with transition metal oxides will boost the performance of the devices. Recently, several studies have been done for developing supercapacitor electrodes with these nanocomposites. This review article presents an analysis of the performance of these nanocomposites with an overview of their specific capacitance, energy density, and cycling stability for supercapacitor electrode application. A brief introduction of the theory and experimental analysis of supercapacitors is also given. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
A review on the immunomodulatory properties of functional nutraceuticals as dietary interventions for children to combat COVID-19 related infections
COVID-19 is a significant threat to humanity in the present day due to the rapid increase in the number of infections worldwide. While most children may be spared of the direct mortality effects of the disease, those with weak immune systems are prone to adverse effects. Child mortality increases due to the stress caused to the health care system that disrupts essential health care needs such as immunisation and antenatal care. The use of functional foods (FF) aids in disease-prevention as they are known to have protective effects against COVID-19 by boosting childrens cellular and humoral immunity. Plant components such as glycyrrhizin, epigallocatechin gallate, allicin, and fucoidan exhibit antiviral properties against various viruses, including SARS-CoV 2. Microbial foods that are made of probiotics, can enhance immunity against various respiratory viruses. Food enriched with additives such as lactoferrin, piperine, and zinc can boost immunity against COVID-19. With proper definitive drug therapy not available for treating COVID-19 and most of the disease management tools rely on symptoms and non-specific supportive care, developing a functional paediatric formulation will prevent further deterioration in infant health. It is wise to investigate the toxicological aspects of Functional Foods components especially when formulating for children. The safe limits of ingredients should be strictly followed during FFs formulation. Stronger regulations with advanced analytical techniques can help to formulate functional foods into the mainstream in child nutraceuticals. The purpose of this review is to compile collective information on the functional nutraceuticals specifically for infants and children up to the age of 10 years that could confer immunity against COVID-19 and other related viruses. Graphical Abstract: [Figure not available: see fulltext.]. 2023, The Author(s).