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A Review on EMG-based Pattern Identification Methods for Effective Controlling of Hand Prostheses
The ability of amputees to do daily duties is significantly restricted by upper limb amputation. The myoelectric prosthesis uses impulses from the surviving muscles in the stump to gradually restore function to such severed limbs. Such myosignals are unfortunately tedious and challenging to gather and employ. The process of transforming it into a user control signal after it has been acquired often consumes a significant amount of processing resources. By modifying machine learning strategies for pattern recognition, the factors that influence the traditional electromyography (EMG)-pattern identification approaches may be significantly minimized. Although more recent developments in intelligent pattern recognition algorithms could discern between a variety of degrees of freedom with high levels of accuracy, their usefulness in practical (amputee) applications was less obvious. This review paper examined how well various pattern recognition algorithms for hand prostheses performed. Finally, we discussed the current difficulties and offered some suggestions for future research in our paper's conclusion. 2023 IEEE. -
Bipolar Disease Data Prediction Using Adaptive Structure Convolutional Neuron Classifier Using Deep Learning
The symptoms of bipolar disorder include extreme mood swings. It is the most common mental health disorder and is often overlooked in all age groups. Bipolar disorder is often inherited, but not all siblings in a family will have bipolar disorder. In recent years, bipolar disorder has been characterised by unsatisfactory clinical diagnosis and treatment. Relapse rates and misdiagnosis are persistent problems with the disease. Bipolar disorder has yet to be precisely determined. To overcome this issue, the proposed work Adaptive Structure Convolutional Neuron Classifier (ASCNC) method to identify bipolar disorder. The Imbalanced Subclass Feature Filtering (ISF2) for visualising bipolar data was originally intended to extract and communicate meaningful information from complex bipolar datasets in order to predict and improve day-to-day analytics. Using the Scaled Features Chi-square Testing (SFCsT), extract the maximum dimensional features in the bipolar dataset and assign weights. In order to select features that have the largest Chi-square score, the Chi-square value for each feature should be calculated between it and the target. Before extracting features for the training and testing method, evaluate the Softmax neural activation function to compute the average weight of the features before the feature weights. Diagnostic criteria for bipolar disorder are discussed as an assessment strategy that helps diagnose the disorder. It then discusses appropriate treatments for children and their families. Finally, it presents some conclusions about managing people with bipolar disorder. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Deep learning based federated learning scheme for decentralized blockchain
Blockchain has the characteristics of immutability and decentralization, and its combination with federated learning has become a hot topic in the field of artificial intelligence. At present, decentralized, federated learning has the problem of performance degradation caused by non-independent and identical training data distribution. To solve this problem, a calculation method for model similarity is proposed, and then a decentralized, federated learning strategy based on the similarity of the model is designed and tested using five federated learning tasks: CNN model training fashion-mnist dataset, alexnet model training cifar10 dataset, TextRnn model training thusnews dataset, Resnet18 model training SVHN dataset and LSTM model training sentiment140 dataset. The experimental results show that the designed strategy performs decentralized, federated learning under the nonindependent and identically distributed data of five tasks, and the accuracy rates are increased by 2.51, 5.16, 17.58, 2.46 and 5.23 percentage points, respectively. 2024 The Author(s). -
Deep learning based federated learning scheme for decentralized blockchain
Blockchain has the characteristics of immutability and decentralization, and its combination with federated learning has become a hot topic in the field of artificial intelligence. At present, decentralized, federated learning has the problem of performance degradation caused by non-independent and identical training data distribution. To solve this problem, a calculation method for model similarity is proposed, and then a decentralized, federated learning strategy based on the similarity of the model is designed and tested using five federated learning tasks: CNN model training fashion-mnist dataset, alexnet model training cifar10 dataset, TextRnn model training thusnews dataset, Resnet18 model training SVHN dataset and LSTM model training sentiment140 dataset. The experimental results show that the designed strategy performs decentralized, federated learning under the nonindependent and identically distributed data of five tasks, and the accuracy rates are increased by 2.51, 5.16, 17.58, 2.46 and 5.23 percentage points, respectively. 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors. -
Optimization of Flexible Manufacturing Production Line System Based on Digital Twin
This research presents a revolutionary Digital Twin (DT)driven method aimed at quick customization of computerized industrial processes. The DT includes dual components, the semi-physical replication that transfers system information and gives data input to the subsequent clause, which is enhanced. The outcomes of the optimum section are returned directly to the semi-physical replication used for validation. The term Open-Architecture Machine Tool (OAMT) led to a fundamental class of machine tools that consists of a basic unified platform and many individually designed modules that may be quickly added or replaced away. Designers can dynamically modify the production system for responding to process planning by inserting personalized components into its OAMTs. Major enabling approaches, along with how to identical virtual and substantial systems and how to instantly bi-level program the invention size and efficiency of developed structures to accommodate sudden variations of goods, are explained. A real execution is done to demonstrate the efficacy of the method to achieve increased enactment of the system by minimizing the overhead cost of the recompose method by systematizing and quickly enhancing it. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Quality enhanced framework through integration of blockchain with supply chain management
Recently, there has been significant growth in the consumption of the most widely diversified Internet of Things (IoT) technological knowledge, and devices, which has resulted in an impact on not only electrical items and the agricultural and food industries (Agri-Food) supply chain networks. This has sparked intense curiosity about the development of information sharing that is reliable, traceable, and transparent, and also increased significant research and advancement efforts. Existing IoT-based trace & authenticity methods for agri-food distribution networks are constructed on top of centralized architectures, which creates the potential for significant issues such as data security, manipulation, and standard points of weakness. A creative and scraping methodological approach to implementing decentralized trust-free networks is represented by blockchain technologies, the decentralized blockchain technologies that underpin cryptocurrencies. The fault tolerance, data integrity, visibility, and complete tracing of saved transactional data, along with cohesive digital information of property resources and independent transactions implementations, are in fact features built into this digitalization. This study introduces Agri-BlockIoT, a completely decentralized blockchain-based traceable platform for managing a global agro-food distribution network that can seamlessly connect IoT systems that produce and consume digital information all along the distribution chain. We implemented a use caseto achieve transparency and traceability. Lastly, we analyzed and contrasted the implementations' capability in terms of delay, CPU, or network utilization. 2022 The Authors -
A STRUCTURAL EQUATION MODELLING APPROACH TOWARDS TAXPAYERS PERCEPTIONS ON GOODS AND SERVICES TAX IN INDIA; [UMA ABORDAGEM DE MODELAGEM DE EQUAES ESTRUTURAIS PARA AS PERCEPES DOS CONTRIBUINTES SOBRE O IMPOSTO SOBRE BENS E SERVIS NA DIA]; [UN ENFOQUE DE MODELADO DE ECUACIONES ESTRUCTURALES HACIA LAS PERCEPCIONES DE LOS CONTRIBUYENTES SOBRE EL IMPUESTO SOBRE BIENES Y SERVICIOS EN LA INDIA]
Purpose: The Purpose of this article is to comprehend how Indian taxpayers perceive the goods and services tax. Theoretical Framework: India has completed five years after the successful implementation of Goods and Services Tax (GST). Many economic benefits were promised at the time of implementation of this tax regime. Thus, it becomes essential to understand tax payers perceptions by developing a strong framework that influences their perceptions. Design/Methodology/Approach: A descriptive study approach was adopted for this objective. 200 replies were obtained in total. Using SPSS Amos, structural equation modelling was utilised to assess the assumptions produced. Attitude, knowledge, Equity, and fairness of taxpayers served as exogenous factors, while taxpayer impression served as the dependent variable. The real-world implication is used as a mediating variable in order to examine the impacts. Findings: The findings of the research indicate that tax knowledge, Equity, and fairness impact tax attitudes. This study provides some useful recommendations for further research in this sector. Research Implications: This study considers tax knowledge, tax equity and fairness and tax attitudes to measure tax payers perception. However, tax rates, regular amendments, circulars, technology and other variables could also be considered by future researchers on this study. Originality/Value: Using a Structural Equation Modelling in understanding Tax Payers Perceptions was hardly adopted in these types of studies. Variables considered for this study were also unique. 2023 AOS-Estratagia and Inovacao. All rights reserved. -
FinTech in India: A systematic literature review
India is the second-most populous country in the world, with a rapidly growing economy. Its population is highly tech-savvy and has a high level of adoption of digital technologies. The Indian government has taken several initiatives to promote digital transactions and financial inclusion. These initiatives have been instrumental in the growth of fintech in India. Fintech, or financial technology, is transforming the financial sector worldwide. Fintech solutions have led to the creation of new business models, streamlined operations, and enhanced customer experience. India is no exception to this trend, as it has witnessed a significant growth in fintech in recent years. The fintech ecosystem in India is highly diverse, consisting of startups, technology companies, banks, and non-banking financial companies (NBFCs). There are various challenges faced by fintech companies in India, such as lack of access to capital, regulatory hurdles, and competition from established players. This chapter proposal aims to provide a basic literature review on the development of fintech in India. 2023, IGI Global. All rights reserved. -
Opinion mining on newspaper headlines using SVM and NLP
Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Optimization of Friction Stir Welding Parameters Using Taguchi Method for Aerospace Applications
The current research work investigated the optimization of the input parameters for the friction stir welding of AA3103 and AA7075 aluminum alloys for its applications in aerospace components. Friction stir welding is rapidly growing welding process which is being widely used in aerospace industries due to the added advantage of strong strengths without any residual stresses and minimal weld defects, in addition to its flexibility with respect to the position and direction of welding. Thus, the demand for this type of welding is very high; however, the welding of aluminum alloys is a key aspect for its use in aircraft components, particularly with respect to bracket mounting frames, braces and wing components. Henceforth in the current work, research is focused on optimization of welding of aluminum alloys, viz. AA 3103 and AA 7075; AA 3103 is a non-heat treatable alloy which is having good weldability, while AA 7075 is having higher strength. Therefore, the welding of these aluminum alloys will produce superior mechanical properties. The optimization of input parameters was accomplished in this work based on L9 orthogonal array designed in accordance with Taguchi methodusing which the friction stir welding experiment was conducted. There were nine experimental runs in total after formulating the L9 orthogonal array table in Minitab software. The input parameters which were selected for optimization weretool rotation speed, feed rate, tool pin profile. The output parameters which were optimized were hardness, tensile strength and impact strength. In addition, the microstructure of the fractured surfaces of the friction stir welded joint was analyzed. It was found from the optimization of the process parameters that strong friction stir welded joints for aerospace applications can be produced at an optimized set of parameters of tool rotational speed of 1100rpm, traverse speed of 15mm/min with a FSW tool of triangular pin profile of H13 tool steel material. 2020, Springer Nature Singapore Pte Ltd. -
Design and optimization of the process parameters for friction stir welding of dissimilar aluminium alloys
Friction Stir Welding (FSW) is one of the unique solid state welding technique that is fast gaining importance because of its ability to produce strong joints. The friction stir welding technique is effectively used in this research to join 5 mm thick dissimilar aluminium alloys of AA 7075-O and AA 5052-O grade. The effect of tool pin profile and tool rotational speed on the mechanical properties like micro-hardness and tensile strength are studied by the optimized Design of Experiments (DOE). The experiments are designed based on L16 orthogonal array considering TAGUCHI techniques for four design parameters and four parametric levels. The outcomes of experimental techniques are tabulated and TAGUCHI analysis, Analysis of Variance (ANOVA) are carried out in Minitab software. From the experimental results and statistical techniques, the methodology is validated and the outcomes of the experiments are found to be in close agreement with the statistical results with the error less than 5% of the mean difference value. The optimized process parameters for better micro hardness are as follows: tool rotational speed of 1200 rpm, feed of 120 mm/min, tool offset of 1 mm, and cylindrical tapered pin tool profile; while the optimized design of process parameters for better tensile strength are as follows: tool rotational speed of 1400 rpm, feed of 120 mm/min, tool offset of 1 mm and cylindrical tapered pin profile. The design and optimization of the process parameters for friction stir welding of dissimilar aluminium alloys is necessary for high strength weld joints. 2021, Paulus Editora. All rights reserved. -
Influence of heat treatment on the tensile and hardness characteristics of friction stir weld joints of dissimilar aluminium alloys
Friction stir welding (FSW) is a solid-state low energy input welding technique. Most capable of joining very high strength alloys, which are finding wide range of applications in automobile and aerospace components. The current research focuses on the influence of post weld heat treatment on mechanical properties of friction stir weld joints of AA 7075 and AA 5052 dissimilar aluminum alloys. The trial experiments have been carried out using design of experiments (L16 Orthogonal Array) and the optimized process parameters have been selected based on the maximum hardness and the corresponding ultimate tensile strength (UTS). Further, the friction stir welding is accomplished with optimized process parameters (L9 Experimental trial) viz., the feed rate of 100?mm/min, tool rotational speed of 1200?rpm, tool offset of (-) 0.5?mm and using a cylindrical taper pin tool profile. The post heat treatment has been carried out on the friction stir weld joints obtained using the optimized parameters and the mechanical properties of the L9 Heat Treated (L9 - HT) and L9 - Non Heat Treated (L9 - NHT) specimens have been compared. The results shows that the post heat treated weld joints have higher micro hardness and tensile strength compared to the non-heat-treated weld joints. This is majorly attributed to recrystallization and elimination of voids due to the change in the microstructure of the weld joint. 2022 Author(s). -
Friction Stir Welding of Dissimilar Aluminium Alloys for Vehicle Structures
Welding process in vehicle structures has gained importance, especially for better strength and mechanical properties. Hence, there is vast research going on in the domain of newer welding techniques. Friction Stir Welding (FSW) is one of them. FSW is used in this research to join two different grades of aluminium alloys by varying the process parameters. The process parameters are optimized based on the Design of Experiments (DoE) and the Taguchi techniques. From the experimental findings for different process parameters, the optimized set of conditions involving the normal, transverse forces and the torque are determined. Further, the process methodology is validated. 2022, MechAero Found. for Techn. Res. and Educ. Excellence. All rights reserved. -
Corrosion Characterization of Friction Stir Weld Dissimilar Aluminium Alloy Joints
The course of contact mix welding is quick acquiring conspicuousness in aviation, marine and car industry because of its benefits as far as mechanical strength, effect and hardness characteristics. There is as yet a requirement for sure fire consideration from the exploration local area to erosion in grating mix welding zones, hence the work introduced here centres around the consumption portrayal of the grinding mix weld divergent aluminium composite. This study looks into friction stir welding under various parametric settings and shows how corrosion happens in a sodium chloride electrolytic media under potentio-dynamic conditions. The friction stir weld joints of dissimilar alloys aluminium are constructed using three sets of parameters. Straight cylinder, taper cylinder, and straight triangular tool profiles; tool rotational speeds of 800, 1000, and 1200 rpm; tool feed rates of 100, 120, and 140 mm/min; and tool offsets of 0.5, 0 mm, and-1.5 mm. The corrosion current (Icorr) reduces as tool rotating speed increases up to 1200 rpm, after which it slightly increases due to the creation of ridges all around the periphery of the friction stir weld area. 2022, Books and Journals Private Ltd.. All rights reserved. -
Effect of the Process Parameters on Machining of GFRP Composites for Different Conditions of Abrasive Water Suspension Jet Machining
The selection of parameters for abrasive water suspension jet (AWSJ) machining of GFRP composites is a major aspect to be considered for optimizing the process. Generally, machining of plastics, polymer matrix composites are accomplished by the AWSJ machining carried out in the presence of atmospheric air; however, the existence of air around the AWSJ may lead to expansion of jet which results in increase in the kerf width and surface roughness; thus to overcome this drawback, an effort has been made in the current work to compare the effect of different process parameters on kerf width and surface roughness while using AWSJ techniques for machining glass fibre-reinforced plastic composite submerged in water. The exploratory outcomes have herewith validated the fact that the surface roughness and kerf width diminishes in under water machining when contrasted with that of free air machining; this is majorly attributed to the fact that the jet diameter reduces in under water AWSJ machining, thereby reducing the kerf width and surface roughness for optimized values of the parameters of speed, feed and standoff distance. Further, the experimental trials have clearly shown that the AWSJ machining used with an optimized set of parameters yields better machining capabilities as compared to abrasive water jet machining. 2019, King Fahd University of Petroleum & Minerals. -
Fabrication of cobalt oxide@cellulose/nitrogen doped carbon nanotubes decorated metal organic frameworks composite for symmetric supercapacitor applications
The two main issues facing the world's population now are energy storage needs and environmental protection. A lot of work has gone into creating electrochemical energy storage using chemical processes and a variety of possible electrode active materials. Supercapacitors, which are energy storage devices with a unique structure and morphology of cellulose materials for green energy resource. In this regard, solid state hydrothermal process is used to fabricate Co3O4@Cellulose (CE), Co3O4@CE/N-MWCNT, and Co3O4@CE/N-MWCNT/ZIF-67 composite materials. XRD, XPS, BET, and HR-TEM analyses verified the structural, surface, and morphological analysis. The electrochemical studies by a three- and two-electrode fabrication in presence of 1M KOH electrolyte for supercapacitor applications. When 1M KOH electrolyte is present, the fabricated Co3O4@CE/N-MWCNT/ZIF-67composite electrode displayed exceptional cyclic stability and a specific capacitance of ?835 F g?1 at 1 A/g. The constructed composite electrodes of Co3O4, Co3O4@CE, and Co3O4@CE/N-MWCNT have specific capacitances of 263, 406, and 576 F g?1 at 1 A/g, respectively, which improves electrochemical properties using a three-electrode design. The Co3O4@CE-N-MWCNT/ZIF-67//1MKOH/SSC composite is produced using two electrode configurations. The final material showed a capacitance of 258 F g?1 at 1 A/g, a capacitance retention of 84.95 % across 8000 cycles, and an energy density of 30.99 W h kg?1 at a power density of 5409 W kg?1. Hence, the composite electrodes that have been produced have the potential to be used in electrochemical systems. 2025 Elsevier B.V. -
Improvement to Recommendation system using Hybrid techniques
Currently, recommendation systems are a common tool for providing individualized recommendations and item information to users. For personalization in the recommendation system, there are a variety of strategies that can be used. To improve system performance and offset the shortcomings of individual recommendation strategies, a hybrid recommender system integrates two or even more recommendation techniques. The demand to summarize all of the knowledge on actual methods and algorithms utilized in hybrid recommended systems necessitates the need for a systematic review in the domain. These materials will be employed to aid in the development of an auto-switching hybrid recommender system. In the content-based filtering technique, the algorithm is based on the contents of items and the collaborative filtering technique algorithm combines the relationship between user and item. Both of the approaches of recommendation system are suffers from some limitations, this is a big issue to predict better recommendations to the user. Hybrid systems are introduced to overcome the main limitations of both techniques. These systems are made with a combination of content-based and collaborative filtering techniques and have advantages of both techniques. With the use of hybrid systems, the quality of recommendations is improved. Hybrid recommendation systems use previous data of a user to find his/her interest and then they target the set of an adjacent user which is similar with that user and according to adjacent user recommend things to the user. Hybrid systems offer the items that share the common things that a user rated highly (Content-based filtering) and make suggestions by comparing the interest of a similar user (Collaborative filtering). 2022 IEEE. -
Determinants of Procrastination among young adults in their academics and professional lives
Procrastination is one of the most pervasive issues which exist in contemporary times among students and professionals. This research aims to understand the modern determinants of procrastination, including factors such as Fear of Missing out (FOMO), Social Zapping, and Sensation Seeking with Impulsivity as a mediator. Limited research has talked about these variables connection with one another. The study was conducted by collecting data from 294 young adults using convenience and snowball sampling with scales of the five variables in question. A mediation analysis was performed which concluded that FOMO has a significant effect on procrastination. Additionally, Social Zapping and FOMO showed a significant relationship with impulsivity. This suggests that FOMO is the key factor that leads to students and professionals choosing to procrastinate their academic/work-related activities in favour of other alternatives such as social or recreational activities. 2023 RJ4All. -
Litigating for Climate JusticeChasing a Chimera?
Across the world, in recent decades, climate litigations have been playing essential roles in shaping domestic policies and legal frameworks on climate change and also in rendering climate justice. There has also been a continuous rise in the development of climate actions, and climate claim litigations by individuals, civil society, and non-state actors. The Indian Supreme Court, High Courts, and the National Green Tribunal have played a significant role in environmental governance by interpreting constitutional and statutory rights to include a right to the environment over the past decades. Nevertheless, with the latest trends in climate litigations, climate challenges have grown across varied climate-related issues, requiring a new judicial approach. In its analysis of climate claims, the justice dispensation mechanism ought to comprehend the shortcomings and be able to generate solutions, similar to those adopted by the courts in the United States, the United Kingdom, and the Netherlands. An analyses of the approach taken by courts in developing nations namely in the Philippines, South Africa, and Pakistan that have compelled governments and corporates to meet their climate commitments are examined. Climate litigation in India has been emerging rapidly over the past decade. As the claims are increasing, the courts and the National Green Tribunal need enhanced capacity building to address climate litigations. This chapter seeks to address the feasibility and implication of equipping courts to address climate litigation. We review the scope of climate litigation and consider the challenges and opportunities to ensure climate justice. This chapter concludes by outlining possible opportunities and challenges in interlinking climate litigation and climate justice in India. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Efficient Lung Cancer Classification on Multi level Convolution Neural Network using Histopathological Images
Lung cancer can be detected by lung nodules, which are a key sign. An early diagnosis enhances the likelihood that the patient will survive by enabling the appropriate therapy to start. To reduce the responsibility of radiologists' difficult and time-consuming labour of finding and categorising malignancy in Computed Tomography (CT) images, researchers have created CAD (computer-assisted diagnosis) systems. The likelihood and kind of malignancy are commonly determined by pathologists using histopathological images of biopsy specimens taken from potentially sick areas of the lungs. To categorise lung nodule malignancy, we recommend employing a four-level convolutional neural network (ML-CNN). From lung nodule CT scan images, multiple scales are extracted. ML-CNN's employs four CNNs network model structure. After the result of the last pooling layer has been flattened to a vector with a single dimension for each level, the vectors are concatenated. These four ML-CNNs will help our model perform better. The ML-CNN model can recognise and classify different forms of lung cancer with reasonable accuracy. The 25000 images employed in the ML-CNN model have been separated into three categories: training, validation, and testing. Three distinct tissue types were assessed and training and validation took up within 80% and 15% of the total time and 5% for testing, respectively. The histopathological images included the following tissue type's 1.Benign tissue 2. Large cell carcinoma 3.squamous cell carcinoma. The proposed model demonstrated superior performance on both the training set, achieving an accuracy of 78%, and the validation set, achieving an accuracy of 89.6% by the end of the evaluation 2023 IEEE.