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Transforming online class recording into useful information repositories using NLP methods: An Empirical Study
Most educational institutions have adapted to the mode of online teaching which has resulted in an increase of online video recordings. Learner community can be benefited with the ability to retrieve required information from the online class recordings. In this paper, we propose a methodology for converting video transcript data into useful information repositories for the purpose of retrieving class transcripts relevant to user's information needs. We focus on the online video recording transcript data. We also discuss challenges in transcribing which are crucial to understand preliminary processing. Our dataset consists of transcripts from diverse subject domains deeper experimental insights. We use interactive transcripts obtained from ASR (automatic speech recognition) services and non-interactive human generated transcripts. State-of-the-art methods for keyword retrieval: Latent Dirichlet Topic Modelling (LDA), Term Frequency (TF.IDF) and Text Rank (graph based) are applied on the video transcript data. Further, cosine similarity metric is applied to obtain the similarity measure between the transcript documents and keywords. 2022 IEEE. -
Digital Forensics Chain of Custody Using Blockchain
In todays world, Digital Forensics is a crucial subject with much scope as data storage becomes more decentralised. The collection and preservation of digital media is a topic of concern across the Cyber Security and Digital Forensics field. With Cloud Infrastructure and other technologies, data is not permanently stored in one place and gathering and analysing it can become a headache for Forensic Investigators. Blockchain, however, works as a decentralised, distributed peer-to-peer network and thus can be considered a suitable solution for the mentioned problems. With the help of a blockchain network and Smart Contracts, Digital Forensics can be significantly improved to adapt to modern digital architecture. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques
IoT is an emerging giant in the field of technol- ogy, taking over traditional systems, providing interconnected- ness, convenience, efficiency, and automation, making our lives unimaginably better. However, security for these IoT systems is challenging, especially due to their interconnectedness, making them vulnerable to various cyber threats. The rising tide of IoT botnets, especially, presents a unique challenge. This has urgently increased the need for Intrusion Detection research. Modern Intrusion Detection approaches often employ Machine Learning for effective results. Feature Selection is extremely important while creating Machine Learning Classification models to avoid overfitting and poor performance. This paper focuses on running a Feature Selection study on the Bot-IoT dataset provided by UNSW to increase the accuracy of a ML model. The paper tests 5 types of Feature Selection methods, from Filter- based, Wrapper-based and Embedded methods, combined with two distinct ensemble classifiers: Random Forest + Adaboost and XGBoost. Each combination is tested with the dataset, and the accuracy is compared to find the most effective and versatile feature selection method that can assist both Stacking and Voting- type Ensemble classifiers. The results show that Karl Pearson can provide the best accuracy when applied to both Ensemble Classifiers. 2024 IEEE. -
Computationally efficient wavelet domain solver for florescence diffuse optical tomography
Estrogen induced proliferation of mutant cells is a growth signal hallmark of breast cancer. Fluorescent molecule that can tag Estrogen Receptor (ER) can be effectively used for detecting cancerous tissue at an early stage. A novel targetspecific NIRf dye conjugate aimed at measuring ER status was synthesized by ester formation between 17-? estradiol and a hydrophilic derivative of ICG, cyanine dye, bis-1,1-(4-sulfobutyl) indotricarbocyanine-5-carboxylic acid, sodium salt. In-vitro studies provided specific binding on ER+ve [MCF-7] cells clearly indicating nuclear localization of the dye for ER+ve as compared to plasma level staining for MDAMB-231. Furthermore, cancer prone cells showed 4.5-fold increase in fluorescence signal intensity compared to control.; A model of breast phantom was simulated to study the in-vivo efficiency of dye with the parameters of dye obtained from photo-physical and in-vitro studies. The excitation (754 nm) and emission (787 nm) equation are solved independently using parallel processing strategies. The results were obtained by carrying out wavelet transformation on forward and the inverse data sets. An improvisation of the Information content of system matrix was suggested in wavelet domain. The inverse problem was addressed using LevenbergMarquardt (LM) procedure with the minimization of objective function using Tikhonov approach. The multi resolution property of wavelet transform was explored in reducing error and increasing computational efficiency. Our results were compared with the single resolution approach on various parameters like computational time, error function, and Normalized Root Mean Square (NRMS) error. A model with background absorption coefficient of 0.01 mm-1 with anomalies of 0.02 mm-1 with constant reduced scattering of 2.0 mm for different concentration of dye was compared in the result. The reconstructed optical properties were in concurrence with the tissue property at 787 nm. We intend our future plans on in-vivo study on developing a complete instrumentation for imaging a target specific lipophilic dye. Springer International Publishing Switzerland 2014. -
Modelling and CFD simulation of vortex bladeless wind turbine
When the forces act on a bluff body in the wind flow direction, vortices are formed. Vortex bladeless wind turbine oscillates as a result of the vortices generated due to VIV. When the vortex shedding frequency is nearer to the natural frequency of the structure, maximum amplitude of vibration occurs and coincidentally power is generated. 3D models are designed to stimulate flow at a Reynolds number of 50000. This paper focuses on modelling the bladeless wind turbine based on semi-vortex angle and also 1) to study the vortices pattern and vorticity of different models 2) to study the drag and lift coefficients. In this paper vortex turbine is designed with certain parameters of dimension in Solid Edge and CFD analysis is carried out in Simscale software. Different model performance parameters like power, natural frequency and coefficient of power are compared among different models to opt for the best vortex bladeless wind turbine design. 2022 Author(s). -
Design and Analysis of Vortex Bladeless Wind Turbine
Vortex bladeless turbine antiquates the conventional wind turbine and adopts a radically innovative and novel approach to captivate the moving wind energy. This device effectively captures the energy of vorticity, an aerodynamic instability condition. As the wind passes a structure, the flow steers and cyclical patterns of vortices are generated. Once the strength of wind force is suffice, the structure starts vibrating and reaches resonance. Vortex bladeless is a vortex induced vibration resonant power generator. It harnesses wind energy from a phenomenon of vorticity, called vortex shedding effect. Clearly bladeless technology consists of a cylinder fixed vertically on an elastic rod, instead of tower, nacelle and blades which are the crucial parts of a conventional wind turbine. The cylinder oscillates on a specifically mentioned wind range, which then generate electricity through an alternator and a tuning system. In this paper the vortex turbine is designed with certain existing parameters of dimensions in Solidworks and the same is analyzed for different materials and dimensions of mast, which is an important part in the vortex turbine. Also various performance parameters like displacement, frequency etc. are also compared among different models. 2021 Elsevier Ltd. All rights reserved. -
Removal of arsenic using ecofriendly egg shell and black toner powder
This work is primarily focused on the study of the possible usage of ecofriendly black toner powder and egg shell powder as adsorbent material for the removal of arsenic from industrial effluent. Batch experiments were conducted by varying the concentration, size of the reinforcement particles, time and its pH value. The optimal pH for the effective removal of arsenic was found to be 7. The size of the particles played a significant role in removing the arsenic. Smaller size particles outperformed the bigger size particles and the joint action of intra-particle transfer and pore diffusion mechanism played a major role in the removal of arsenic. 2022 -
Cost Effective and Energy Efficient Drip Irrigation System for IoT Enabled Smart Agriculture
The conventional methods of smart farming consume a significant percentage of the resources such as water, electricity, and manpower. This approach demands more time, money, and effort. The state of the art drip irrigation methods make use of the solenoid valve to control the water flow. The problem with such a system is reflected in its power consumption which is a significant factor for large-scale demands. The method proposed in this paper addresses this problem by developing an automated drip irrigation system that replaces components used in conventional methods with its economical counterparts in the market. A system using Node MCU, DC submersible motor, and soil moisture sensor is developed to automate the irrigation process ensuring efficient water and energy consumption. Since the proposed system utilizes economically cheaper components, it provides an upper edge over other systems in terms of expenditure and in turn economically feasible for large-scale demands. A mobile application is also developed to control, monitor, and schedule irrigation processes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Automated Contactless Continuous Temperature Monitoring System for Pandemic Disease Controlling Infrastructures
People are being thermally screened in hospitals and in such facilities, all the data collected must be stored and displayed. The person responsible for keeping track of people's body temperatures must put in more time and effort. This approach is a tedious task, especially during times of dealing with the pandemic diseases like Covid-19. Hence, in this paper, an automated contactless continuous temperature monitoring system is designed to eliminate this time-consuming process. If a person's temperature is too high, that is, higher than the usual temperature range, the system records it and monitors it continuously via a mobile application. In this paper, we present the development of an Automated contactless continuous body temperature monitoring system using a Raspberry Pi camera and mobile application. 2023 IEEE. -
Occupancy improvement in serviced apartments: Customer profiling
Sustaining and improving higher occupancy and generating steady revenue by bringing the experience of 'Home away from Home'for the Customers is the business model of ServicedApartments Industry. Serviced Apartment Industry has to be highly competitive. Its performance is governed by many factors such as competition, technology, social factors and lastly Customers themselves. This study focuses only on Customer profile. To achieve results, the Serviced Apartment Owners/Managers will need to study Customers' profile and their needs. Customer satisfaction and retention lead to better customer loyalty, occupancy rates, and revenue. In this paper a methodological framework to analyze and profile Serviced Apartment Customers is discussed, focusing on the factors and particularly the Customer information which could help in increasing the Occupancy. There is a trend that would normally go unnoticed if analysis of data is taken at the aggregate level but looking at them individually, it provides interesting information. 2012 Taylor & Francis Group. -
Ethnic Food: A Solution With Sustainable Food Resources A Study On Consumer Awareness Of Ethnic Food And Its Impact On Consumption Attitude
Food has seen numerous transformations over the centuries and has been a focus of study pertaining to culture and evolution. Besides being a celebration of diversity and a marker of human adaptations, food is also a broad knowledge domain that represents various geographic, cultural and lifestyle outlooks. Ethnic food relates to a heritage or the culture of an ethnic group with them incorporating the local produce and animal sources into their diet. Ethnic food also has a sustainability aspect to it in terms of food miles and carbon emissions since more transportation involved means higher level of GHG released, economic aspect such as with composition changes and food security, and community relationship. This paper finds that when consumer awareness of ethnic food increases, the consumption attitude towards it does, too. This could be of importance in policy implementation and identifying sustainability systems. A connection with the land and a community relationship involving food could help represent more ethnic food, to increase awareness on a global level and also allow more people to experience these vast cultural diversities. If understood well and implemented, ethnic food could be of use as a tourism brochure, sustainability driver, economical promoter and community supporter. The Electrochemical Society -
Growth of cerium oxide nanorods by hydrothermal method and electrochromic properties of CeO2/WO3 hybrid thin films for smart window applications
Innovative electrochromogenic nanomaterials such as composite materials and also hybrid films can improve electrochromic performance, because of their potential application qualities in electronic, low-power screens, automotive anti-reflect mirrors, and smart windows. In this study, we used a hydrothermal method to used grow the CeO2 nanorods both with and without HCl added to the solution. And also, DC magnetron sputtering was used to deposit the tungsten oxide films on the cerium oxide nanorods. The surface plasmon effect changes with the size of CeO2 Nanorods, and this phenomenon influences electrochromic outcomes. The electrochromic characteristics of CeO2/WO3 nanostructures on FTO-coated glass are examined in the visible spectrum to use a 0.5 M concentration of H2SO4 as such electrolyte. At 600 nm, these structures produce significant optical modulation (50 %) and coloring efficiency (11.60 cm2/C at 700 nm). 2022 -
Thickness dependent tungsten trioxide thin films deposited using DC magnetron sputtering for electrochromic applications
DC magnetron sputtering was used to grow tungsten oxide (WO3) thin films on FTO and corning substrates. SEM, XRD, Electrochemical Analyzer, and UVVis Spectrometer were used to analyze surface morphology, structural properties, electrochromic characteristics, and optical characteristics. At an 800 nm wavelength, a decrease in thin-film thickness increased optical transmittance from 87 % to 95 %. Furthermore, coloring efficiency was observed to vary with the thickness of thin films for both 500 nm and 375 nm are 10.34 cm2 C-1 to 18.57 cm2 C-1. In comparison to the high-thickness thin film, the lesser-thickness deposited nano-thin film has a higher diffusion coefficient. At 8 10-4 mbar partial pressure, the diffusion coefficients for the smaller and the high-thickness thin film are 7.28x10-14 cm2s?1 and 6.0x10-14 cm2s?1, respectively. The diffusion coefficient and coloring efficiency have been found to have a considerable influence on the thickness and surface-to-volume ratio, which could be important in electrochromic applications. 2022 -
Image Pre-Processing Algorithms for the Quality Detection of Tea Leaves
This Identification and prediction of the tea quality is the essential research focus nowadays in the field of agriculture. Nowadays the Artificial Intelligence has become the latest topic in the region of pattern recognition. The various combination and permutation of the different techniques has resulted in proper solving the problem as well as have better accuracy in recognition. Therefore, there is urge need of a detailed survey AI techniques used for the identification of the tea leaf quality for the different grades of tea plants. In this paper, we aim on the various methods used for the pre- processing of the input image to extract the processed image which will further be useful for the feature extraction and the classification of the proposed image. It is very important to get the effective and accurate processed data which will further act as an input for the next level modules. This paper shows various methods of edge detection are applied on the image like Canny, Sobel and Laplacian are used. The further results are compared for quality metrics parameters such as the Mean Square Error (MSE) & Structural Similarity Index Metric (SSIM). The main agenda of this paper is to perform the edge detection and to check the quality measure of the processed image. The software used here is python. 2022 IEEE. -
Identification Of Quality Of Tea Leaves By Using Artificial Intelligence Techniques: A Review
This paper summarizes the outcome of the survey carried out for quality identification of a tea leaf and eventually price prediction. Quality identification can allow to categorizing leaf in different grades, which helps the buyer and seller to acquire suitable quality to their need. Price prediction is an important feature, which can bring certainty at price and farmers can be benefitted more for their good quality. Additionally, if the leaf disease is identified at the initial stage that would also allow farmers to timely resolve the concerned issues and save their corps. In the field of agriculture, this has been always a research area to identify and predict the quality of tea leaves. Various artificial intelligence techniques are hot topics in the field of recognition and their effective combination can not only solve the problem but also enhance recognition accuracy. Therefore, there is an imminent need for a detailed survey on compiling techniques used for the identification of different varieties of tea plants. In this research, we aim to propose a review of the various techniques which can be utilized for determining the quality and price prediction. The Survey is hybrid with a combination of different artificial techniques, which is a suitable approach to target effective tea leaf identification. Further for the classification of tea leaf images, various algorithms can be combined as well to obtain better results and different algorithms can be used for feature extraction based on texture extraction, color extraction, and shape extraction. The Electrochemical Society -
An Examination of Methodological Approaches for Segmentating Fetal Brain MRI Images - Analysis
In today's world and in the country like India, Women's health needs more care. Especially the women's health during the pregnancy period plays a vital role in both the mother as well as the baby's care. As per a survey, among thousands three of them found to have fetal brain abnormalities. If these abnormalities are predicted at the early stage, then it will be an added advantage in saving both the life of mother and baby. During the pregnancy number of tests have to be performed to monitor fetal development. Tests like fetal ultrasound, Chorionic Villus Sampling, Amniocentesis, Fetal Echocardiogram, Fetal MRI imaging SCAN etc. The fetal brain abnormality can be predicted as well as treated at the early stage by analyzing the fetal brain MRI during the gestational period. Identifying abnormalities in fetal brain MRI images involves several essential steps, including image segmentation, analyzing images involves extracting distinctive features, refining their quality, identifying relevant patterns, and categorizing them based on specific criteria. The process of classification determines whether an abnormality is present or not. Analyzing images presents a complex undertaking owing to the diversity in shapes, spatial arrangements, and intensity levels within the images. This paper focuses on reviewing and comparing various segmentation techniques, highlighting their respective strengths and weaknesses. 2024 IEEE. -
Improved Crypto Algorithm for High-Speed Internet of Things (IoT) Applications
Modern technologies focus on integrated systems based on the Internet of Things (IoT). IoT based devices are unified with various levels of high-speed internet communication, computation process, secure authentication and privacy policies. One of the significant demands of present IoT is focused on its secure high-speed communication. However, traditional authentication and secure communication find it very difficult to manage the current need for IoT applications. Therefore, the need for such a reliable high-speed IoT scheme must be addressed. This proposed title introduces an enhanced version of the Rijndael Cryptographic Algorithm (Advanced Encryption Standard AES) to obtain fast-speed IoT-based application transmission. Pipeline-based AES technique promises for the high-speed crypto process, and this secure algorithm targeted to fast-speed Field Programmable Gate Array (FPGA) hardware. Thus, high-speed AES crypto algorithms, along with FPGA hardware, will improve the efficiency of future IoT design. Our proposed method also shows the tradeoff between High-Speed communications along with various FPGA platforms. 2020, Springer Nature Switzerland AG. -
Information Extraction Using Data Mining Techniques For Big Data Processing in Digital Marketing Platforms
In the dynamic landscape of digital marketing, harnessing the potential of big data has become paramount for informed decision-making. This study explores the integration of data mining techniques within big data processing frameworks to extract valuable information in digital marketing platforms. With the exponential growth of data generated through online interactions, social media, and e-commerce, traditional methods fall short of uncovering meaningful insights. This research focuses on leveraging advanced data mining algorithms to sift through vast datasets, identifying patterns, trends, and user behaviours. The proposed approach aims to enhance marketing strategies by extracting actionable intelligence from diverse data sources. Techniques such as association rule mining, clustering, and sentiment analysis will be employed to unveil hidden correlations, segment target audiences, and gauge consumer sentiment. The scalability of big data frameworks ensures efficient processing of massive datasets, allowing marketers to make real-time, data-driven decisions. Additionally, the study explores the challenges and opportunities associated with implementing data mining in big data environments for digital marketing. This research contributes to the evolving field of digital marketing by providing a comprehensive framework for extracting, processing, and utilizing information from big data. The findings promise to empower marketers with a deeper understanding of consumer behaviour, enabling the development of more personalized and effective marketing strategies in the ever-evolving digital ecosystem. 2023 IEEE. -
Impact of Demographicson Green Behavior
The need to preserve the environment, lower pollution levels, expand the amount of green space, and encourage environmentally responsible behavior has grown in recent years, all of which will contribute to a more sustainable society. This study seeks to determine the probability that demographic variables of students in higher education in Delhi NCR will influence their desire to participate in environmental education. Binary Logistics Regression has been used on the data gathered from 302 respondents and the model has been found to have been a good one as shown by Omnibus Test. It is found that 'Gender' and 'Field of Study' are the two most significant variables, which have a higher probability impact on students' willingness to join environmental education. Specifically, female students vis-vis male students and students with engineering & and science background vis-vis other students have more chance of joining environmental education courses. 2024 IEEE. -
Knee-Osteoarthritis Detection Using Deep Learning
Arthritis is a condition that causes pain, stiffness, inflammation, and other symptoms in one or more joints. It is more common in older adults and tends to worsen with age. There are different types of arthritis, but osteoarthritis is the most prevalent. A study discusses the use of Convolutional Neural Networks (CNN) for detecting knee osteoarthritis. CNN is a deep learning algorithm that can analyze data and classify images accurately, like the human brain. The purpose of this study is to classify different knee X-ray images to predict the severity of the disorder, allowing for early detection and lifestyle changes to prevent the disease from worsening. An online tool has been developed to diagnose knee osteoarthritis and provide remedies based on various K-grade predictions. This tool can help patients understand their knee's condition and take necessary measures to manage the disease. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.