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Inter Frame Statistical Feature Fusion for Human Gait Recognition
Researches showed that gait is unique for individuals and human gait recognition gained much attention nowadays. The sequence of gait silhouettes extracted from the video sequences has its own significance for gait recognition performance. In this paper, a novel inter frame feature discriminating the individual gait characteristics is proposed. Consecutive frames within a gait cycle are divided into equal number of blocks and corresponding block differences are calculated. It can preserve the minute temporal variations of the different body parts within each block and the cumulative difference provide a unique feature capable of discriminating individuals. To avoid synchronization problems, secondary statistical features are extracted from the primary inter frame variations. Finally, feature level fusion schemes are applied on these statistical features with existing features extracted from CEI representation. The efficiency of the proposed feature is evaluated on widely adopted CASIA gait dataset B using subspace discriminant analysis. The experimental results show that our proposed feature has better recognition accuracy in comparison with existing features. 2019 IEEE. -
Optimized Metamaterial Loaded Square Fractal Antenna for Gain and Bandwidth Enhancement
This paper presents a report on the enhanced performance of an optimized metamaterial loaded square fractal antenna (OMSFA). The design and simulation of the antenna was carried out using Electronic Desk Top HFSS version 18.2 software. The antenna layer spreads over an area of 23 square millimeter on a FR4 substrate whose dielectric permittivity is 4.4. The substrate size measures an area of 46 mm X 28 mm, with 1.6 mm thickness. Also the design includes a microstrip feed and truncated ground. The antenna resonates well with a deep return loss of-38.9 dB in a broad bandwidth of 3.2 GHz (128 %) between 2 GHz and 5.2 GHz. The OMSFA produces enhanced gain of 9.8 dB at 2.5 GHz. The radiation is more focused due to the effect of metamaterial loading. The proposed antenna is recommended for wireless application in the lower region (S band) of the microwave spectrum. 2018 IEEE. -
Ground Truncated Broadband Slotted Circular Microstrip Antenna
In this growing era of wireless technology, large sized devices have become obsolete. In response to the increasing demand for miniaturization over the past decades, microstrip antennas have drawn attention due to its various features like light weight, low cost, small in size and its greater resistivity to shock and vibrations. These can also easily get conformed to any surface. These antennas are also capable of operating at high frequencies, providing large bandwidth and gain by using various techniques slots and truncation of shapes. This report describes the design, simulation, fabrication and measurement results of a microstrip fed Slotted Circular Microstrip Antenna for broadband applications. The antenna was designed for an operating frequency of 2.45 GHz on a double side printed FR4 substrate measuring 55 mm x 55 mm x 1.6 mm with ?r of 4.4. It measured a very large resonant band of 1.3 - 9.05 GHz at a return loss level as low as -36.5 dB at 7.98 GHz. A maximum gain of 2.46 dB was achieved at 2.33 GHz. The enhancement in bandwidth was achieved by truncation in ground and inclusion of thin circular slot. The HFSS version 18.2 software and VNA model Anritsu SA20E were used for simulation and measurement respectively. It is found that the simulation and measurement results agree. 2018 IEEE. -
ANN based pattern generation, design and simulation of broadband fractal antenna for wireless applications
The synthesis of microstrip antenna(MSA) remains complex and time consuming from convenient design point of view. The Artificial Neural Network (ANN) on the other hand provides quicker and accurate solutions while multiple parameters controlling MSA designs. This paper proposes a new type of square fractal antenna (SFA) structure iterated and optimized by ANN developed using Advanced C and simulated using HFSS for optimum resonance characteristics covering 1.6-6.6 GHz frequency range. The motivation behind this work is size reduction of MSAs through FA concept with broadband resonance. It is suggested that the proposed antenna can be a right choice for various wireless applications because of its broadband functionality. 2016 IEEE. -
Utilizing Machine Learning for Sport Data Analytics in Cricket: Score Prediction and Player Categorization
Cricket is a popular sport with complex gameplay and numerous variables that contribute to team performance. In recent years, sports analytics has gained significant attention, aiming to extract valuable insights from large volumes of cricket data. Cricket has many fans in India. With a strong fan following, many try to use their cricket intuition to predict the outcome of a match. A set of rules and a points system govern the game. The venue and the performance of each player greatly affect the outcome of the match. The game is difficult to predict accurately as the various components are closely related. The CRR (Current Run Rate) approach is used to predict the final score of the first innings of a cricket match. Total points are calculated by multiplying the average number of runs scored in each over by the total number of overs. For ODI cricket, these methods are useless as the game can change very quickly regardless of the current run rate. The game may be decided by 1 or 2 overs. For more accurate score predictions, a system is needed that can more accurately predict the outcome of an inning. This research paper explores the application of machine learning techniques to predict scores and classify players based on their roles in the squad. The study utilizes a comprehensive dataset comprising various attributes of cricket matches, including player statistics, match conditions, and historical performance. Linear Regression, Logistic Regression, Naive Bayes, Support Vector Machines (SVM), Decision Tree, and Random Forest regression models are employed to predict scores. Additionally, player categorization is performed using a classification approach. The results demonstrate the effectiveness of machine learning techniques in enhancing performance analysis and decision-making in the game of cricket. 2023 IEEE. -
Explaining Autism Diagnosis Model Through Local Interpretability Techniques - A Post-hoc Approach
In this era of machine learning and deep learning algorithms dominating the Artificial Intelligence (AI) world, the trustworthiness of these black box models is still questionable. Life-caring sectors like healthcare and banking make use of these black box models as assistance in critical decision-making processes, but the degree of reliability of these decisions is still uncertain. This is because these black box models will not reveal the causation of the predicted outcome. However, creating an interpretable model that can explain the internal workings of these black box models can provide some reliable insights and trustable justifications for the predicted outcome. This study aimed to create an interpretable model for autism diagnosis which can give some trustable explanations for its predicted outcome. Using local interpretability methods such as LIME, SHAP, and Anchors the predicted outcome for each instance is explained well with some standard visual representations. As a result, this study developed an interpretable autism diagnosis model with an accuracy rate of 91.37% and with good local model explanations. 2023 IEEE. -
Efficient Method for Personality Prediction using Hybrid Method of Convolutional Neural Network and LSTM
Users' contributions and the emotions conveyed in status updates may prove invaluable to studies of human behavior and character. A number of other research have taken a similar approach, and the field itself is still growing. The goal of this proposed is to create a technique for deducing a user's personality traits based on their social media profiles. Among the many customer services now available on SNSs are media and recommendations of user involvement. The need to give internet users with more specialized and customized services that meet their specific requirements, which sometimes depend heavily on the users' inner personalities, is significant. However, there hasn't been much work done on the psychological analysis that's needed to deduce the user's inner nature from their outward activities. In this instance, LSTM-CNN was fed pre-processed and vectorized text documents. SNF is used for feature extraction. The proposed method employs CFS for the purpose of Feature Selection. Finally, LSTM-CNN was used to train the model. While CNN is good at extracting features that are independent of time, LSTM is better at capturing long-term dependencies. combination of features for personality prediction, the LSTM-CNN model is superior to the individual models. 2023 IEEE. -
A critical review of Cr(VI) ion effect on mankind and its amputation through adsorption by activated carbon
A toxic heavy metal is a one which is plausibly dense metal or metalloid that is eminent for its prospective toxicity, particularly in environmental context. Heavy metal poisoning may crop up as an upshot of air or water contamination, exposure to industrial activities, foodstuffs, medicines, coarsely coated food containers, etc. The present review highlights various issues related to the effects of Cr (VI) heavy metal toxicity to human health and its adsorption from wastewater using low cost adsorbents. Many researchers have lay their endeavor to ascertain low-priced adsorbents that are effortlessly available and have power over the sensible adsorption capacity. It is perceptible from the literature survey that the revealed adsorbents have established stupendous removal capabilities for Cr (VI) metal ions. As the convention of heavy metal Cr (VI) is increased, it is implicit that there is a strong need for research to remove Cr (VI) heavy metal ions from wastewater to trim down the problem of soaring anthropogenic pressure and burly tendency to mount up in living organisms. 2020 Elsevier Ltd. All rights reserved. -
Investigation of detoxification nature of activated carbons developed from Manilkara zapota and de oiled soya
Heavy metals are poisonous and detrimental water contaminant. Their existence affects human beings, animals and vegetation as a outcome of their mobility in aqueous ecosystem, toxicity and nonbiodegradability. This work aimed at the development of new adsorbent in the detoxification of heavy metals using Manilkara zapota tree wood and de oiled soya. The study completely focused on the characterization of the developed activation in the view of using it as a adsorbent. The characterization of activated carbon was effected SEM analysis, FTIR, XRD analysis and surface area determination. Both the activation carbon have showed a tremendous characterization in their employability as adsorbent in adsorption of heavy metals in aqueous solution. 2019 Elsevier Ltd. All rights reserved. -
Comparative Study on GANs and VAEs in Credit Card Fraud Detection
In today's world, the major issue credit card sectors encounter is fraud. This comparative study deals with how GANs and VAEs detect fraudulent transactions. The dataset comprised 284807 transactions, of which 492 were fraudulent. These two models, GANs and VAEs, are trained on this dataset, during which, in the training process, the models are learned to deal with the imbalance in the dataset. VAEs are trained so that fraud transactions are considered anomalies, and only legitimate transactions are passed onto the model for training. Conversely, GANs generate synthetic data of fraud by addressing the problem of data imbalance and passed on to the ML model for classification. We can observe that Both the models have very good AUC-ROC scores of around 96%, which indicates their distinguishing capability between the classes. In all other aspects, GANs outperformed VAEs, which makes GANs a better option for fraud detection. 2024 IEEE. -
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. -
Design and Development of Teaching and Learning Tool Using Sign Language Translator to Enhance the Learning Skills for Students With Hearing and Verbal Impairment
This research paper presents a system designed for the students with verbal and hearing impairments by enabling realtime Sign-to-Text and Text-to-Sign Language conversion, with a specific focus on the Indian Sign Language (ISL). The proposed study aligns to the United Nations Sustainable Development Goal (SDG) of Quality Education. The system leverages cutting-edge technologies, MediaPipe for holistic key point extraction encompassing hand and facial movements, and Long Short-Term Memory (LSTM) architecture powered by TensorFlow and Keras for accurate sign language interpretation. This comprehensive approach ensures nuanced aspects of sign language, such as facial expressions and hand movements, are faithfully represented. On the receiving end, the system excels at Text-to-Sign Language conversion, allowing non-sign language users to interact naturally with sign language users through textual input transformed into sign language animations and Sign-to-Text conversion where the information from the sign language users is converted to text which ensures smooth communication. A user-friendly web application, developed using HTML, CSS, and JavaScript, enhances accessibility and intuitive usage for realtime communication. This research represents a significant advancement in assistive technology, promoting inclusivity and communication accessibility. It underlines the transformative potential of innovation infostering a more connected and inclusive world for all, regardless of their hearing abilities 2024 IEEE. -
Artificial Intelligence and Machine Learning Combined Security Enhancement Using ENIGMA
Enigma is a relatively new and emerging field that has the potential to bring significant benefits to the way contracts are executed and managed. The integration of Artificial Intelligence (AI) into smart contract technology can automate repetitive tasks, reduce the need for human intervention, improve decision-making, and provide transparency and trust. It can also provide more flexibility, handle more complex tasks, learn from past experiences, have predictive capabilities, and have human oversight and intervention. All these features make Enigma contracts more advanced than traditional smart contracts. AI-powered smart contracts, or Enigma contracts, can also improve contract execution, increase efficiency, facilitate better negotiation, and facilitate automated dispute resolution. However, as the technology is still in its early stages, major challenges and risks can adopted but the need for robust security. The potential for AI is to make decisions that are not in the best interests of its parties. Despite these challenges, the potential benefits of AI-powered smart contracts make them an area of on-going research and development that is worth exploring further. Enigma can be used or applied in various fields, and can be used to secure the sensitive information by applying robust security system. Enigma contract is a AI powered smart contract which is used to automate decision-making processes and improve its efficiency, Enigma as the name suggest it is a complex security network which has the potential to revolutionize the security system by increasing efficiency. 2023 IEEE. -
Advancing Predictive Analytics in E-Learning Platform: The Dominance of Blended Models in Enrollment Forecasts
The rapid expansion of e-learning platforms has revolutionized the landscape of education, particularly highlighting the significance of online courses in contemporary learning environments. This research focuses on Udemy, a prominent online learning platform, and aims to enhance the predictability of course enrollments within its IT & Software category. The study's central purpose is to leverage advanced machine learning techniques to predict course subscriber numbers, a crucial indicator of a course's popularity and success. Employing an extensive dataset from (Kaggle DB)Udemy, encompassing various course attributes such as ratings, reviews, and pricing, the study explores multiple machine learning models. These include Linear Regression, Decision Tree, Random Forest, Gradient Boosting, and K-Nearest Neighbors Regression. A key innovation of this research is the application of ensemble methods, particularly a blended model approach, to integrate predictions from multiple models, thereby enhancing accuracy and reliability. The findings of this study are significant. The ensemble approach, notably the blended model, outperforms individual predictive models in accuracy. Among the single models, Gradient Boosting Regression shows the highest effectiveness in forecasting enrollments. The research highlights the vital role of course characteristics, including ratings and reviews, in determining course popularity. This study contributes to the field of e-learning by introducing a novel, data-driven approach to predict course enrollments. It offers valuable insights for educators, course creators, and platform developers, emphasizing the potential of machine learning in optimizing content strategy and marketing efforts in the digital education domain. The application of ensemble machine learning methods presents a new horizon in educational analytics, paving the way for more nuanced and effective strategies in online education delivery and promotion. 2024 IEEE. -
Safe cloud: Secure and usable authentication framework for cloud environment
Cloud computing an emerging computing model having its roots in grid and utility computing is gaining increasing attention of both the industry and laymen. The ready availability of storage, compute, and infrastructure services provides a potentially attractive option for business enterprises to process and store data without investing on computing infrastructure. The attractions of Cloud are accompanied by many concerns among which Data Security is the one that requires immediate attention. Strong user authentication mechanisms which prevent illegal access to Cloud services and resources are one of the core requirements to ensure secure access. This paper proposes a user authentication framework for Cloud which facilitates authentication by individual service providers as well as by a third party identity provider. The proposed two-factor authentication protocols uses password as the first factor and a Smart card or Mobile Phone as the second factor. The protocols are resistant to various known security attacks. Springer India 2016. -
Blast resistance of steel plate shear walls designed for seismic loading
Since a blast loading or explosion can create nonlinear wave action and impact pressure on a structure, it necessary to construct a structure to resist blast loading as like other loads. In this study the nonlinear behaviour of a blast loading is simulated by calculating the pressure diagram with respect to time under the guidance of IS 4991-1968, code for "Criteria for Blast Resistant Design of Structures for Explosions above Ground". The study carried out for different charge weight (100kg TNT, 200kg TNT and 400kg TNT) and standoff distances of 20metre. Nonlinear behaviour of a Blast loading to steel structures with shear plates of thickness 6 mm, 8 mm and 10 mm are modelled in ETABS and the analysis is carried out to obtain base shear, story displacement, story deformation pattern, column forces, etc. Published under licence by IOP Publishing Ltd. -
Sustainability Concepts in the Design of Tall Structures
Construction industry is a rapid growing industry with various new technologies coming into practice. Sustainability concept is also a call for the present generation as many natural resources are getting exhausted. Thus the new era of development of Tall Structures with respect to Sustainability concept is being studied by concentrating on the Structural systems that can be adopted for construction of the same. In the present study we have considered two different 3D RC frame structural systems i.e., normal Beam-Column structural system and Outrigger structural system. The following two systems were modelled in ETABS 15.2 software in seismic zone V with three different heights that is 150m (50 storeys), 240m (80 storeys) and 300m (100 storeys). Response spectrum analysis is carried out considering Earthquake forces and the results are tabulated for maximum storey displacement and maximum storey drift. Then finally the structural system which is sustainable in construction of Tall structures is identified. 2020, Springer Nature Switzerland AG. -
Effect of different base isolation techniques in multistoried rc regular and irregular building
Base confinement system for a structure is acquainted to decouple the building structure from possible movement incited by the movement of the seismic tremor, keeping the building superstructures from retaining the quake vitality. Base isolator increases the regular time period of the general structure and diminishes its shear increasing speed reaction to the seismic movement. In this explanatory examination, a ten-storey reinforced concrete (RC) building with lead elastic bearing, high damping elastic bearing and triple-contact pendulum framework bearing is acquainted with the structures, and correlation is made between fixed base and the base-secluded structures. Demonstrating and investigation are conveyed utilizing ETABS 2015 v15.2.2. The investigation examination is performed to check the ampleness of the working against the lateral displacement, inter-storey drift, story shear and story acceleration. It is found from the investigation that reaction of working to lateral load diminishes, while modular period is expanded in both X and Y bearings. Furthermore, it was reasoned that triple grating pendulum bearing is increasingly compelling in examination of different direction utilized in this investigation. Springer Nature Singapore Pte Ltd 2021. -
Response surface optimization and process design for glycidol synthesis using potassium modified rice husk silica
Glycerol, an inexpensive by-product from biodiesel production can be converted into many useful products notably glycidol, which has a wide range of uses. In this study, glycidol synthesis has been done using a biowaste mediated catalyst in a single step process. Silica and potassium incorporated silica were synthesized from biowaste rice husk. These catalysts were characterized by different spectroscopic techniques. Basic sites in the catalysts were estimated using temperature-programmed desorption study. Four operational parameters were optimized using Box Behnken Design (BBD) of response surface methodology (RSM). Potassium incorporated rice husk was found to be one of the best catalysts for glycidol production with 60.8% glycerol conversion and 62.9% selectivity within one hour of reaction time. 2020 Elsevier Ltd. All rights reserved. -
A Translator for Indian Sign Boards to English using Tesseract and SEQ2SEQ Model
Language translator for Indian language to English have been developed and it have proven to a challenging domain due to large combination of character in Indic scripts such as Tamil, Kannada and Hindi. In this paper we propose a system which captures Indian printed character and translates it into English, we have discussed the various method and machine learning model that was used to build this system with an accuracy of 87%. 2021 IEEE.