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A review on ensembles-based approach to overcome class imbalance problem
Predictive analytics incorporate various statistical techniques from predictive modelling, machine learning and data mining to analyse large database for future prediction. Data mining is a powerful technology to help organization to concentrate on most important data by extracting useful information from large database. With the improvement in technology day by day large amount of data are collected in raw form and as a result necessity of using data mining techniques in various domains are increasing. Class imbalance is an open challenge problem in data mining and machine learning. It occurs due to imbalanced data set. A data set is considered as imbalanced when a data set contains number of instance in one class vastly outnumber the number of instances in other class. When traditional data mining algorithms trained with imbalanced data sets, it gives suboptimal classification model. Recently class imbalance problem have gain significance attention from data mining and machine learning researcher community due to its presence in many real world problem such as remote-sensing, pollution detection, risk management, fraud detection and medical diagnosis. Several methods have been proposed to overcome the problem of class imbalance problem. In this paper, our goal is to review various methods which are proposed to overcome the effect of imbalance data on classification learning algorithms. Springer Nature Singapore Pte Ltd 2019. -
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 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 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 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 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 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 the scope of using calcium fluoride as a multiphase coating and reinforcement material for wear resistant applications
Solid lubricants play a vital role in the smooth and safe operation of many tribological industrial applications like cutting and forming tools, rolling and sliding contact bearings, gears, cams and protective coating in gas turbine engines for aerospace applications. Generally liquid lubricants are widely used for reducing the friction between the contacting parts which reduce the wear rate and increase the life of the parts. However, these liquid lubricants become useless when they are exposed to high temperature, high pressure and vacuum environmental conditions. Solid lubricants are those materials that can suitably reduce the friction and wear between the contacting or sliding surfaces that are in extreme environments like low and high temperature and pressure. Among the different types of solid lubricants, calcium fluoride is widely used owing to its excellent lubricity at elevated temperature. This paper initially describes the criteria for selecting solid lubricant and provides a comprehensive summary on calcium fluoride solid lubricant which can be used as a coating material in various high temperature metal and ceramic matrix composites for wear resistant applications. Further, investigations related to the selection of optimized coating parameters, synerging multiphase solid lubricants and soft metals with optimal percentage, selection of filler materials, mismatch in coefficient of thermal expansion and its impact on coating life are summarised and discussed. Finally, the scope of synthesizing calcium fluoride solid lubricant from discarded eggshell powders is explored. 2022 Elsevier Ltd. All rights reserved. -
A Review on Utilization of Construction and Demolition Waste (CDW) Toward Green and Circular Economy
Globally, policy makers have realized the significance of infrastructure development with respect to safety and environment-friendly approach. This has resulted in reuse and recycling initiatives in various industries including construction and building sector. Further, it is imperative to understand new techniques and methods to improve the effectiveness of recycling, keeping environment and carbon emissions in check. Recently, utilization of construction and demolition waste (CDW) as precursors in synthesizing alkali-activated and geopolymer binders have caught attention of researchers as green building material. This review paper discusses the findings of the latest research and promotes the use of CDW as a potential starting or precursor material in alkali-activated or geopolymer concrete toward green and circular economy. If processed appropriately, CDW can be used to produce environment-friendly binders that can reduce our dependence on conventional binders like Portland cement, thus promoting recycling in sustainable and eco-friendly manner. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Revised Study of Stability Issues in Mobile Ad-Hoc Networks
Adhoc or short live network has developed tremendously in the recent time, which can work without any access point or mobile towers. That means it is an infrastructure less network. Mobile Ad-hoc Networks can be referred as MANETs. The locations can be changed, and it can discover the path dynamically. In other words, the nodes move dynamically leading to the update of the topology, frequent change in topology, optimization of routing and fading the interference of multiuser are few issues connected with MANETs that affects the efficiency of the data transfer. The purpose of this survey is to reveal the various types of mechanisms which can be used to resolve the problem of routing performance related issues in MANETs. This paper also presents the classification of link stability, route repair and stable path algorithms in tabular format. 2020, Springer Nature Switzerland AG. -
A Road to Become Successful in The Fashion Industry of China: A Case Study of Zara
In this research, it was found that Zara is facing issues while maintain its profitability and also while maintaining its large stores. Existing information collected from websites and articles show that Zara provides inferior quality products, does not have factory in China, focuses less on e-commerce activities and contributes directly to environment pollution through waste generation in China. These are reasons that the organization is losing its brand image in China. To improve its current condition, it is recommended that Zara should improve its products, focus more on marketing, develop factories in China and reduce environment pollution. The Electrochemical Society -
A Scoping review of Deep Reinforcement Learning methods in Visual Navigation
Reinforcement Learning (RL) is a subset of Machine Learning that trains an agent to make a series of decisions and take action by interacting directly with the environment. In this approach, the agent learns to attain the goal by the response from its action as rewards or punishment. Recent advances in reinforcement learning combined with deep learning methods have led to breakthrough research in solving many complex problems in the field of Artificial Intelligence. This paper presents recent literature on autonomous visual navigation of robots using Deep Reinforcement Learning (DRL) algorithms and methods. It also describes the algorithms evaluated, the environment used for implementation, and the policy applied to maximize the rewards earned by the agent. The paper concludes with a discussion of the new models created by various authors, their merits over the existing methods, and a briefing on further research. 2023 IEEE. -
A Secure Communication Gateway with Parity Generator Implementation in QCA Platform
Quantum-Dot Cellular Automata (QCA) has arisen as a potential option in contrast to CMOS in the late time of nanotechnology. Some appealing highlights of QCA incorporate incredibly low force utilization and dissemination, high gadget pressing thickness, high velocity (arranged by THz). QCA based plans of normal advanced modules were concentrated broadly in the ongoing past. Equality generator and equality checker circuits assume a significant part in blunder discovery and subsequently, go about as fundamental segments in correspondence circuits. In any case, not very many endeavors were made for an efficient plan of QCA based equality generator as well as equality checker circuits up until now. In addition, these current plans need functional feasibility as they bargain a ton with normally acknowledged plan measurements like territory, postponement, intricacy, and manufacture cost. This article depicts new plans of equality generator and equality checker circuits in QCA which beat every one of the current plans as far as previously mentioned measurements. The proposed plans can likewise be effortlessly reached out to deal with an enormous number of contributions with a straight expansion in territory and inactivity. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
A Secure Data Encryption Mechanism in Cloud Using Elliptic Curve Cryptography
Cloud computing is undergoing continuous evolution and is widely regarded as the next generation architecture for computing. Cloud computing technology allows users to store their data and applications on a remote server infrastructure known as the cloud. Cloud service providers, such Amazon, Rackspace, VMware, iCloud, Dropbox, Google's Application, and Microsoft Azure, provide customers the opportunity to create and deploy their own applications inside a cloud-based environment. These providers also grant users the ability to access and use these applications from any location worldwide. The subject of security poses significant challenges in contemporary times. The primary objective of cloud security is to establish a sense of confidence between cloud service providers and data owners inside the cloud environment. The cloud service provider is responsible for ensuring user data's security and integrity. Therefore, the use of several encryption techniques may effectively ensure cloud security. Data encryption is a commonly used procedure utilised to ensure the security of data. This study analyses the Elliptic Curve Cryptography method, focusing on its implementation in the context of encryption and digital signature processes. The objective is to enhance the security of cloud applications. Elliptic curve cryptography is a very effective and robust encryption system due to its ability to provide reduced key sizes, decreased CPU time requirements, and lower memory utilisation. 2024 IEEE. -
A Secure Deep Q-Reinforcement Learning Framework for Network Intrusion Detection in IoT-Fog Systems
IoT-Fog system security depends on intrusion detection system (IDS) since the growing number of Internet-of-Things (IoT) devices has increased the attack surface for cyber threats. The dynamic nature of cyberattacks often makes it difficult for traditional IDS techniques to stay up to date. Because it can adapt to changing threat landscapes, deep Q-reinforcement learning (DQRL) has become a potential technique for ID in IoT-Fog situations. In this paper, an IDS system for IoT-Fog networks based on DQRL is proposed. The suggested solution makes use of fog nodes' distributed computing power to provide real-time IDS with excellent accuracy and minimal latency. With feedback from the network environment, the DQRL agent learns to recognize and categorize network traffic patterns as either normal or intrusive. Adaptive exploration techniques, effective reward functions, and deep neural networks for feature extraction are adopted by the system to improve predictive performance. The evaluation findings show that, in terms of detection accuracy, precision, recall and f-measure, the proposed DQRL provides flexibility to changing threat patterns as compared to conventional IDS techniques. A vast array of cyberattacks, such as malware infections, denial-of-service (DoS) attacks, and command-and-control communications, are successfully recognized and categorized by the system. It is possible that the suggested solution will be crucial in safeguarding IoT-Fog networks and preventing cyberattacks 2024 IEEE. -
A secure image-based authentication scheme employing DNA crypto and steganography
Authentication is considered as one of the critical aspects of Information security to ensure identity. Authentication is generally carried out using conventional authentication methods such as text based passwords, but considering the increased usage of electronic services a user has to remember many id-password pairs which often leads to memorability issues. This inspire users to reuse passwords across e-services, but this practice is vulnerable to security attacks. To improve security strength, various authentication techniques have been proposed including two factor schemes based on smart card, tokens etc. and advanced biometric techniques. Graphical Image based authentication systems has received relevant diligence as it provides better usability by way of memorable image passwords. But the tradeoff between usability and security is a major concern while strengthening authentication. This paper proposes a novel twoway secure authentication scheme using DNA cryptography and steganography considering both security and usability. The protocol uses text and image password of which text password is converted into cipher text using DNA cryptography and embedded into image password by applying steganography. Hash value of the generated stego image is calculated using SHA-256 and the same will be used for verification to authenticate legitimate user. 2015 ACM. -
A Self-Attention Bidirectional Long Short-Term Memory for Cold Start Movie Recommendation Models
Movie recommendation systems are useful tools that help users find relevant results and prevent information overload. On the other hand, the user cold-start issue has arisen because the system lacks sufficient user data. Furthermore, they are not very scalable for use in extensive real-world applications. One of the key strategies to address the sparsity and cold-start problems is to leverage other sources of information, including item or user profiles or user reviews. Processing client feedback is typically a challenging process that involves challenging the interpretation and analysis of the textual data. Thus, this research implements an efficient deep learning-based recommendation architecture. Following the acquisition of textual data from the Amazon product reviews database, stop word removal, lemmatization, and stemming techniques are applied to the data pre-processing which eliminate inconsistent and redundant data, facilitating the process of interpreting and utilising data. Then, the Term Frequency-Inverse Document Frequency (TF-IDF) method is applied to extract the feature values from the pre-processed text data. The extracted feature values are fed to the Self-Attention Bidirectional Long Short-Term Memory (SA-BiLSTM) that utilises the matrix factorization method framework's information sources. The SA-BiLSTM model obtained 95.93% of recall, 94.76% of precision, and 97.84% of accuracy on the amazon product reviews database. 2023 IEEE. -
A Shortest Path Problem for Drug Delivery Using Domination and Eccentricity
The concept of domination was first introduced in by Ore in 1962. With this, the study of domination gained importance and has been vigorously studied since then. The idea about eccentricity for vertices in a graph was given by Buckley and Harary in 1990. This paper combined the ideas about domination and eccentricity and provides the observation obtained during the study. Most of the basic ideas about domination and eccentricity has been covered and also a comparative study between these two has been stated along with problem of drug transportation through networks. These ideas can be further used to solve the real-world problems which uses concepts of domination and eccentricity like for example drug delivery game theory problems, routing problem, assignment problem and many more. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Single Sign on based secure remote user authentication scheme for Multi-Server Environments
A Multi-Server Architecture comprises of a server environment having many different servers which provides the user the flexibility of accessing resources from multiple Service Providing Servers using the same credential. The primary objective of a Multi Server Environment (MSE) is to provide services of different Service Providers (SPs) without repeating registration at each SP server, and to get a unique single credential for all the servers in MSE. However, the conventional MSEs, proposed by various researchers, proposes the individual authentication service by each SP on their respective server using the credential issued by the Registration Authority of MSE. The mechanism requires the user to access each SP by keying the same credentials for every SP separately. Single Sign On (SSO) is an authentication mechanism that enables a user to sign-on once and access the services of various SPs in the same session. SAML is generally used as a Single Sign-On protocol. This work analyzes the smart card based authentication scheme for Multi-Server Environment proposed by Li et al.'s and discuss various security attacks on the said scheme. The paper also proposes a Secure Dynamic-ID based scheme using smart cards or crypto cards which do not require a verifier table and implements Single Sign On feature using SAML protocol, thus allowing the user to enjoy all the features of an MSE along with SSO. 2014 IEEE. -
A Slotted Circular Patch Antenna with Defected Ground for Sub 6 GHz 5G Communications
In this paper, a slotted circular patch antenna with Defected Ground Structure (DGS) is presented. The slots created on radiating element and the defect introduced on the ground plane shifted the resonance frequency from 2.49 GHz to 1.17 GHz. This corresponds to 53% reduction in size at 1.17 GHz. The proposed antenna is designed on FR-4 substrate (r=4.4) with thickness of 1.6 mm. Simulations are carried out using HFSS Ver. 18.2. The simulated reflection coefficient of Circular Patch Antenna (CPA) at 2.49 GHz, Slotted Circular Patch antenna (SCPA) at 2.34 GHz and Slotted Circular Patch antenna with Defected Ground Structure (SCPA-DGS) at 1.17 GHz are - 28.7 dB, -31.33 dB and -11.03 dB respectively. For validating the simulated design, SCPA-DGS is fabricated and measured its reflection coefficient and VSWR using Vector Network Analyzer (Anritrsu S820E). The measured and simulated values are very well matched with each other. Therefore the proposed antennas may be used in sub 6 GHz 5G communication applications. 2022 IEEE.