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Ternary Blended Geo-Polymer Concrete - A Review
The manufacturing of ordinary Portland cement produces carbon di oxide which is responsible for global warming. Geopolymer concrete in the field of construction leads to economic sustainability and reduces adverse effects on environment. Geopolymers are inorganic polymers obtained from chemical reaction between an alkaline activator's solution and an alumina-silicate material without using cement. Alkali activators are Homogeneous mixture consisting of two (NaOH and Na2SO3) or more chemicals in different proportions are highly corrosive and difficult to handle. There are still some limitations with respect to the alkaline activators in geopolymer concrete. To overcome ordinary portland cement, many wastes materials such as Silica-fume, GGBS, fly ash etc. have been used in recent studies to create eco-friendly cements by geo-polymerization reactions. Geopolymers are economic & good alternative construction material in making concrete This review paper briefly explains on previous literatures, properties, materials of geopolymer concrete, testing and practical applications of geopolymer concrete. Published under licence by IOP Publishing Ltd. -
Text Summarization Techniques for Kannada Language
Text Summarization is summarizing the original text document into a shorter description. This short version should retain the meaning and information content of the original text document. A concise summary can help humans quickly understand a large original document better in a short time. Summarization can be used in many text documents, such as reviews of books, movies, newspaper articles, content, and huge documents. Text summarization is broadly classified into extractive Text Summarization (ETS) and Abstractive Text Summarization (ATS). Even though more research works are carried out using extractive methods, meaningful summaries can be attained using abstractive summary techniques, which are more complex. In Indian languages, very few works are carried out in abstract summarization, and there is a high need for research in this area. The paper aims to generate extractive and abstractive summaries of the text by using deep learning and extractive summaries and comparisons between them in the Kannada language. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Text-Based Sentimental Analysis to Understand User Experience Using Machine Learning Approaches
Data Analysis is turning into a driving force in every industry. It is a process in which data is analyzed in multiple ways to come to certain conclusions for the given situation. Sentiment analysis can be said to be a sub-section of data analysis where analysis is carried out on the emotions and opinions of the text. Social media has a plethora of sentiment data in various forms such as tweets, updates on the status, and so forth. Sentiment analysis on the huge volume of data can help in identifying the opinions of the general mass.The primary goal is to find the opinion of customers on the services of the Bangalore airport and to enhance the nature of these services according to the feedback provided. In this paper, we aim to measure customer opinion on services provided by Bangalore Airport through sentiment. Data is collected by a python-based scraper. The tweets are processed to determine whether they are of positive or negative opinion. These opinions are then analyzed to determine the factors which cause the negative opinions and the airport staff are alerted about the same. Various algorithms were used as part of the experimental analysis. LSTM produces more accuracy compared with existing approaches. 2023 IEEE. -
The Creation of Intelligent Surfaces for the Purpose of Next-Gen Wireless Networks
In preparation of the changing environment of 5th wave (5G) and prospective networks of cells, this study explores new methods to meet challenges that result from the erratic character of the communication medium. Traditionally viewed as a chance factor, the relationship between broadcast radio waves with surrounding factors lowers signal quality in modern times of wireless communications. This paper performs a full literature review on customizable autonomous surfaces (RISs) alongside their uses, stressing the chance for network managers to control radio wave features and minimize environmental spread problems. RISs allow effective control over waveform parameters, including the amplitude, phase, number, and polarization, that without needing complex encoder, decoder, or radio wave processing methods. Leveraging technical developments, metasurfaces, reflectarrays, phase shifts, and liquid crystals appear as potential options for RIS application, placing them as pioneers in the realization of 5G as well as subsequent networks. The study dives into current actions in the RIS-operated mobile phone network area and covers core research issues that deserve exploration to feed unlocking the full promise of RISs at wireless communication networks. 2024 IEEE. -
The design and analysis of helical cross - Axis wind turbine
Environmental conditions such as high turbulence, low wind speed, and persistent changes in oncoming wind direction can minimize the performance of a horizontal axis wind turbine (HAWT). Some specific vertical axis wind turbine (VAWT) designs can work fine in these rare functioning conditions but still, they pose an occasional power coefficient. So a unique design of a helical cross-axis wind turbine (HCAWT) was modeled which will operate under multiple wind directions such as horizontal wind stream and vertical wind stream from the underside of the turbine. The HCAWT consists of three helical vertical blades and six horizontal blades arranged in cross-axis orientation for enhancing its performance and self-starting behavior. The obtained analysis study results show that the power generated by the HCWAT was improved when compared to the Straight-Bladed VAWT. Both the turbines were placed at height of 100, 150, 200 & 250?mm in the simulation study, coefficient of power (Cp) achieved by HCAWT was 0.43, 0.52, 0.48, and 0.51 at an RPM of 554, 512, 474 and 449 respectively whereas in the case of Straight-Bladed VAWT was 0.15, 0.18, 0.13 and 0.23 at an RPM of 179, 189, 212 and 233 were obtained. 2022 Author(s). -
The Design of Driver Fatigue Detection Based on Eye Blinking and Mouth Yawing
In modern era, the Intelligent Transportation System (ITS) is very essential for the betterment of transport management, autonomous vehicles and especially for safe driving. The statistics suggest that the major severe accidents occur because of drivers drowsiness. The main objective of this work is to give the alert alarm when the driver is falling asleep. In the proposed study, the driver's face is detected using the Viola Jones algorithm, and a novel approach to detecting eye blinks using template matching and a similarity measure. For effective eye tracking, the normalized correlation coefficient is calculated. The correlation score is used to identify eye blinks since a blink causes a significant change in the correlation score. In tracking of mouth yawing finding the darkest region between the lips. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
The Development of ID System for Detecting Attacks in WSN Through Ontology Method and its Strategy
Cybercriminals are becoming increasingly targeted by the rapid expansion of the Internet of Things (IoT), leading to an increase in cyberattacks targeting IoT devices and their communication channels These attacks, if failure to detect may result in significant service disruption, financial loss or damage to sensitive data. Real-time intrusion detection is essential to ensure reliability, security and profitability of IoT applications. This study introduces a new intrusion detection system designed for IoT devices that uses deep learning (DL). Utilizing ontology in wireless sensor networks (WSN), this intelligent system detects suspicious activities that pose a threat to connected IoT devices with configuration-neutral design provides ease of use, while the test performance analysis is simulated and real-world. It highlights its strong performance in determining admissions. The effectiveness of the system against many types of attacks such as denial of service, workholes, blackholes, opportunistic service attacks, etc. is confirmed by experimental research and furthermore, the system achieves F1 scores, accuracy and the number of memories. This advanced deep learning intrusion detection system shows great promise to improve IoT network security due to its high detection rate. 2024 IEEE. -
The Development of Structured Tele Based Medicine Concept Using Programmable System
In the medical field, clinics and hospitals frequently use dispersed applications like telediagnosis. These apps must nevertheless provide information security in order to properly transit security measures like firewalls and proxies. The User Datagram Protocol (UDP) is often recommended for videoconferencing applications because of its low latency; nevertheless, security problems occur when UDP tries to pass through firewalls and proxies without a specified set of fixed ports. In order to overcome these obstacles, this study presents a revolutionary platform that uses Transmission Control Protocol (TCP) rather of UDP: VAGABOND, which stands for 'Video Adaptation framework, across security gateways, based on transcription,' Adaptation Proxies (APs) that are designed to accommodate user preferences, device variations, and dynamic changes in network capacity comprise VAGABOND. This platform's versatility at the user and network levels guarantees seamless operation in a range of scenarios. VAGABOND uses a binomial probability distribution to start making adaptation decisions. This distribution is formed from the retention of video packets inside a certain time period. VAGABOND gets beyond firewall and proxy constraints by using ordinary TCP ports (like 80 or 443) to provide videoconferencing data via TCP. But even though TCP is a dependable transport protocol, it can occasionally have latency and socket timeout problems. VAGABOND has clever adaptation techniques to deal with these problems and ensure smooth data transfer. 2024 IEEE. -
The effect of airline service quality on customer satisfaction and loyalty in India
Indian Aviation Industry has been one of the world's fastest-growing aviation industries with private airlines representing more than 75 percent of the domestic aviation industry. With an 18 percent compound annual growth rate (CAGR) and 454 airports and airstrips in place in the country, 16 of which are designated as international airports, it has been stated that by 2011 the aviation sector will be witnessing a revival. In 2009, with traffic movement rising and revenues rising by nearly US$ 21.4 million, India's Airports Authority appears expected to earn better margins in 2009-10, as indicated by the Civil Aviation Ministry's latest estimates. The most crucial step in identifying and providing high-quality service is to understand exactly what customers expect. Quality of service is one of the best models for measuring customer expectations and perceptions. A company's performance results in customer satisfaction with a product or service. Passenger satisfaction is important to customer sovereignty. Customers can be loyal without being highly satisfied and being highly satisfied and yet not being loyal. Companies are required to gain a better understanding of the online environment relationship between satisfaction and behavioural intention, and to assign online marketing strategies between satisfaction initiatives and behavioural intention programme. In addition, the findings of this research will assist airline managers to better serve their customers, track and improve quality of service and achieve the highest level of satisfaction for their passengers. 2020 Elsevier Ltd. All rights reserved. -
The Effect of Bloom's Taxonomy on Random Forest Classifier for cognitive level identification of E-content
With the advancement in internet, the efficiency of e-learning increased and currently e-learning is one of the primary method of learning for most learners after the regular academics studies. The knowledge delivery through e-learning web sites increased exponentially over the years because of the advancement in internet and e-learning technologies. The learner can find many website with lots of information on the relevant domain. However learners often found it difficult to Figure out the right leaning content from the humongous availability of e-content. In the proposed work an intelligent framework is developed to address this issue. The framework recommend the right learning content to a user from the e-learning web sites with the knowledge level of the user. The e-contents available in web sites were divided in to three cognitive levels such as beginner, intermediate and advanced level. The current work uses Blooms Taxonomy verbs and its synonyms to improve the accuracy of the classifier used in the framework. 2020 IEEE. -
The effect of cutting fluid in improving the machinability of Inconel 718 using ceramic AS20 tool
Industries demand a vast usage of superalloys in heat resistant and high temperature applications. These include nozzle of rocket fuel engines, throttle valve of turbojet engines, turbine blade discs of aerospace industries, rivets and fittings of chemical and production industries, biomedical applications in super strength resistive steels. These superalloys such as Inconel 718 finds its vast applications in all such industries. To machine such materials a lot of wear and tear occurs at the cutting tool. Hence, cutting fluid helps in reduction of tool wear and improving surface roughness. In this paper, two cutting fluids Koolkut 40 and Hicut 590 have been used in emulsified form during the machining of Inconel 718 with Ceramic cutting tool. Hicut 590 has been seen a better heat resistive cutting fluid in reducing the tool wear and thus improving the life of the tool. 2021 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of the scientific committee of the Global Conference on Recent Advances in Sustainable Materials 2021. -
The effect of non-thermal argon plasma treatment on material properties and photo-catalytic behavior of TiO2 nanoparticles
In this paper, a brief study on the effect of non-thermal plasma generated with argon carrier on material properties and photo-catalytic reduction behavior of TiO2 is presented. Commercially available TiO2 nanoparticles (20 nm size) were subjected to Ar cold plasma at different time durations. Then the plasma treated materials were explored for chemical reduction of carbon dioxide (CO2) into methane (CH4) using sunlight as photo-irradiation source. The results show that the non-thermal plasma affects the material properties of TiO2 such as UV-visible absorption, XRD patterns and Raman scattering significantly and also the enhancement of CH4 yields in CO2photo-chemical reduction. 2020 American Institute of Physics Inc.. All rights reserved. -
The Effect of Prediction on Employee Engagement Organizational Commitment and Employee Performance Using Denoised Auto Encoder and SVM Based Model
The purpose of human resources is to ensure that the appropriate people are hired for open positions at appropriate times, that the system receive the necessary training, and that their performance is monitored and their perspective skills are secure through the use of evaluation methods. Despite the importance of this data to decision-makers, it can be difficult to glean useful insights from large datasets. Data mining has made it possible for human resources experts to automate the hitherto tedious task of manually processing enormous data sets. Finding almost perfect outcomes is the main goal of data mining, which is to discover hidden knowledge in data patterns and trends. The proposed method goes as follows: preprocessing is done by data cleaning and data normalization, feature selection using correlation and information theoretic ranking criteria. The last step in training and evaluating the model is using AE-SVM, which stands for Auto Encoder Support Vector Machine. The suggested model is more effective and performs better than two existing models: Support Vector Machine and AE-CNN. The suggested approach attains an accuracy rate of 94%. 2024 IEEE. -
The Effect of Sustainable Development Goals (SDG's) on the Financial Performance of Listed Companies
The corporate sector is emerging as a significant stakeholder in this transformative journey asnations throughout the world work to align their policies and practices with the SDGs. Theincorporation of SDGs into financial planning has made tremendous headway in India, acountry with a rapidly expanding economy and a diverse corporate landscape. The 50companies that made up the Nifty 50 at the end of 2023 are examined in this study. Twosources provided the financial data on these companies: the Bloomberg platform andSecurities and Exchange Commission (SEC) reports. Only thirty of the NIFTY 50 companieswere putting the SDGs into practise on the previously indicated date. There are fourconfigurations in the successful FP model that describe how the SDGs and FP relate to oneanother. The lack of SDGs, when combined with other variables, explains a high ROE in twoof these four configurations. The examination of the data concludes that businesses who havetraditionally attained higher FP (i.e., higher ROE) have not included SDGs into their strategy.Furthermore, the inclusion of SDGs in strategies results in a lower return on equity (ROE).The paper however takes into consideration only size and risk as the main variables tocalculate the ROE. We recommend the future researchers to consider the other financialvariables while doing the analysis to get a more insightful analysis on the effect of SDGs. Grenze Scientific Society, 2024. -
The Efficiency of Ensemble Machine Learning Models on Network Intrusion Detection using KDDCup 99 Dataset
With the advent of data communication the increased usage of the technologies results in network intrusions and associated attacks. Consequently, the data violation rates are increased abundantly and that sacrifices Confidentiality, Integrity and Availability. This article focused on the network Intrusion Detection System (IDS) that detects various attacks and types. Machine learning (ML) has the potential to spot known-experience and Zero-day attacks. Consequently, the article has considered ML and ensembled models for the various attack classification. The major contributions of the current article are 3-fold. Initially, to understand the relevance and sufficiency of the dataset through exploratory data analysis. Second, the comprehensive understanding of the various attacks, its nature, various types and classifications and finally, the empirical analysis of the dataset through the potential of various ML models. The article utilized various discriminative models for the execution and all of the models have shown better accuracy. The tree-based ensemble model, Random Forest has outperformed the rest of the models with higher accuracy in the training and testing samples of 99.997 % and 99.969 % respectively. 2023 IEEE. -
The Empirical Analysis of Machine Learning Approaches for Enhancing the Cyber security for better Quality
In recent years, there have been significant advances in both technologies tactics so in area of cyber security, with (ML) machine learning at the forefront of the transformation. It is the ability to obtain security event characteristics or findings from cyber security information and then develop a matching information model that will allow a security system to become autonomous and smart. The widespread proliferation and the usage of Web and Smartphone applications has increased the size of cyber world as a consequence. When a computerized assault takes too long to complete, the internet becomes vulnerable. Security measures may be improved by recognizing and reacting to cyber-attacks, thanks to cyber security techniques. Security measures that were previously used aren't any longer appropriate because scammers have learned how to evade them. It is getting more difficult to detect formerly unknown and unpredictable security breaches, which are growing more widespread. Cyber security is becoming more dependent on machine learning (ML) techniques. Machine learning algorithms' dependability remains a major challenge, given its continual advancement. It is possible to find malicious hackers in internet that are ready to exploit ML defects that have been made public. A thorough review of machine learning techniques safeguarding cyberspace against attacks is provided in this paper, which presents a literature review on Cyber security using machine learning methods, such as vulnerability scanning, spam filtering, or threat detection on desktop networks as well as smart phone networks. Among other things, this paper provides brief descriptions of each machine-learning technique and security info, essential machine-learning technology, and evaluation parameters for a classification method. 2022 IEEE. -
The future of urban life: The technological and humanistic dimensions of cognitive cities
A smart city implies realising sustainable city growth enabled by technology-based intelligent solutions to give its citizens a good quality of life. Information and communication technologies play a crucial role as the nerve centre of the smart city for collecting and analysing data from various sources, like mobile, social media, and sensors. The Internet of things (IoT) and big data (BD) also play a critical role in smart city infrastructures, changing how we analyse patterns and trends in human behaviour. Smart cities generate massive amounts of data and therefore need many flexible ways to process data and implement solutions. Recently, cognitive analytics have attracted the attention of researchers and practitioners worldwide as a technology-based innovative solution. It is a novel approach to information discovery and decision-making which uses multiple intelligent technologies such as statistical machine learning, deep learning, distributed artificial intelligence, natural language processing and visual pattern recognition to understand data and generate insights. A cognitive smart city refers to the convergence of emerging IoT and smart city technologies to realise cyber-physical social systems, their generated big data from sensing to communication and computing, and artificial intelligence techniques for all aspects of collaborative computing in sensors, actuators and human-machine interfaces. The field of humanities typically approaches the concept of cognitive cities from a cultural, philosophical, and humanistic perspective. Humanities scholars examine how cities shape our thoughts, beliefs, values, and experiences and how they impact our collective memory and identity. They consider the role of cities as sites of cultural production and consumption and explore the social and political implications of urbanisation and technological advancement. This paper aims to highlight the connection between technology and the humanities in the context of cognitive cities. The paper will explore the technological aspects of cognitive cities and their cultural, humanistic, and philosophical implications. 2023 Author(s). -
The Future Warfare with Multidomain Applications of Artificial Intelligence: Research Perspective
We live in a period when historical fiction has become current reality. With our future being automated, using AI on a daily basis will only get more convenient. Making military weapons to detect, monitor, and engage a human being with attacks may all be done in the privacy of one's own garden. There is a plethora of AI software out there that can be readily integrated into combat weapons. The automobile industry is already incorporating AI into vehicles to assess driving circumstances and give augmented reality to drivers via heads-up displays in order to assist avert accidents. Similarly, artificial intelligence will be utilized to study the battlefield and give soldiers with augmented reality information via heads-up displays and weapon control systems. Since AI is not a single technology, it has been argued that it might be used by the military in a variety of ways. Intelligence, surveillance, and reconnaissance (ISR) activities, as well as processing and interpreting sensor data and geographic imaging analysis, are all examples of AI. Artificial intelligence has the potential to reduce human involvement in conflict, whether it is employed for combat robots or data analysis. AI has the potential to profoundly alter the nature of war. The article mainly focussed on warfare technologies and applications. The main aim of this review is to understand the current applications being used in armed forces and proposed technologies of artificial intelligence. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
The impact of Covid-19 on global upstream and downstream supply chain management activities
COVID-19 pandemic has affected thousands of people worldwide; with significant economic changes in the past and to the changes to be made for future. Many organisations especially; The Intergovernmental economic organisation (OECD - The Organisation for Economic Co-operation and Development) warned the companies and industries on the global economic cut, the corona virus will be boarding. The global economy and international markets pitched down with the spread of corona virus spreading from China which is the world's second largest economy to other countries including Asia; Europe; Australia; Europe; America and the Middle East. Many economies came up with many policies to prevent the further spread of this virus; including restrictions on travel and quarantines; which has disrupted international supply chains affecting a lot of business operations and dwindle revenues. About 75 percent of business including Wholesale; Manufacturing; Retail and Services in China and about 51,000 companies have this impact at a global level according to data from Dun and Bradstreet. The success or failure of every Business depends on how well they manage their supply chain management activities. The impact of corona virus on supply chain activities is twofold. One is; Upstream Supply chain management where companies should monitor the backward integrated activities in procuring the inventory; which has accommodated a loss in the production because of closure of factories and a slowdown in the economy. Second is; Downstream Supply chain management where the intermediaries and middlemen face a lot of problems because of scarcity in inventory and many quarantine measures taken by many economies. Many disruptions in both Upstream and Downstream Supply chains lead to severe scarcity of inventory which was experienced globally by all the economies. This situation has made many economies to think of the inter connectivity and inter dependency among global nations in terms of supply chain. This article is aimed to highlight the effects and changes COVID-19 pandemic has brought in the supply chain industry from both Upstream and Downstream perspective. 2022 Author(s). -
The Impactful Role of ML Algo in the Field of Enactment Nostrum: An Intensive/Deep Review
Machine translation (MT) research has explored a variety of models, including statistical machine translation (SMT), rule-based machine translation (RBMT), and hybrid approaches. Hybrid MT systems aim to improve translation quality by using the strengths of different models. However, the complex set of functions associated with MT systems is still difficult to understand and optimize. This instant study propose an approach based on ML with respect to hybrid MT that addresses these issues by automatically interpreting and weighting features using ML tools. This research framework includes a classification approach to classify and compare translations from multiple black-box A system that uses ML classifiers trained on feature vectors derived from natural language processing tools. This study presents a method to train and use an SVM-based classifier to generate hybrid interpretations. The test results for English-Chinese pairs show the potential of this research approach to improve translation quality. The proposed framework is a simple and efficient way to combine different MT systems, improving translation results without manual intervention. 2024 IEEE.