bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Responsive With Big Data Technologies
Authors: Ashif Ali, Dr. Nisarg Gandhewar
Published: December 2023

The volume of produced digital data & everyday usage is increasing as a result of rising man-machine interaction, process automation, and falling hardware and software costs.

 The vast amount of digital information produced per second worldwide in unstructured, structured, and semi-structured formats is referred to as big data in this context.

Big data analytics is a new topic that has inspired researchers all over the world to create, develop, and implement a variety of mechanisms, technologies, architectures, and areas for evaluating the enormous amount of data created every day. Traditional database management systems struggle to make sense of the massive amounts of data that make up "big data." The paper describes a few analyses, including word count, sentiment analysis, and responsive analysis.

Responsiveness is crucial for improving the system, identifying vulnerabilities, and ensuring that tasks are distributed fairly. Responsive is important data that can be used for making wise decisions.

Responsive is significant for both strengths and weaknesses, not just when it identifies flaws. If the responsive analysis is performed incorrectly, the analysis's conclusion will likewise be incorrect. As a result, the system as a whole will be inaccurate because the pattern that was discovered will also be incorrect. We will utilize the Map-Reduce framework to create this suggested system for responsive analysis, and Hadoop will be used for storage.
Crime Classification Using GRU and 1CNN Techniques
Authors: S. Jeya Selvakumari, V. Joseph Peter
Published: December 2023

Criminal activity is a widespread social problem that affects a nation's standard of living, rate of economic development, and international standing.  Numerous crimes have proliferated worldwide in recent years. It has a variety of effects on people of all types. Classification of crime is necessary to create a safe society.  Many researchers classify the crimes using machine learning techniques with a common dataset, which presents numerous computational opportunities and challenges. Using the learning capabilities of several deep learning architectures, we are trying to classify the criminal activity from the real-time dataset of particular city. In the proposed work, Gated Recurrent Unit (GRU) and Convolutional Neural Network (1CNN) deep learning architecture are used for crime classification. We have conducted performance comparison for classifying the crime with real-time dataset. The effectiveness of the aforementioned deep learning models was assessed by Training and Validation Loss curve. It was discovered that 1CNN models achieve better result compared to GRU.  1CNN produced 97% accuracy.
Ensuring Data Integrity And Security In Diverse Cloud Environments To Prevent Duplicacy.
Authors: Jibin Joy, Dr. S. Devaraju
Published: December 2023

Data deduplication is a valuable technique for compressing and minimizing data duplication during data transfers, especially in cloud environments. By eliminating redundant data, it optimizes transmission capacity and reduces memory usage. To ensure the integrity of sensitive data, encryption is applied throughout the deduplication process. The SHA algorithm is commonly used for storing text data during deduplication. It generates security bits by padding the text and computes a hash consisting of hexadecimal, string, and integer data. Hash-based deduplication involves hashing the entire file and treating the hash values of text data as unique identifiers. This allows clients to identify duplicate data within the cloud. The Proof-of-Work (PoW) algorithm is widely utilized in blockchain networks like Bitcoin and Ethereum. Its primary function is to verify transactions through a process called mining. Miners engage in competition to solve complex mathematical problems, and the first one to find a solution is granted the right to add a new block to the blockchain. PoW relies on cryptographic hashing, such as the SHA-256 hashing function, to validate and secure transactions. On the other hand, the Proof of Retrievability (PoR) algorithm finds application in cloud computing systems. It serves as a consensus mechanism, ensuring that cloud storage providers store and retrieve data accurately. PoR enables cloud providers to demonstrate to consumers that their files can be fully recovered. This algorithm incorporates cryptographic proofs that validate the integrity and availability of the stored data. In cloud storage, deduplication is often implemented using the Memory mapping technique (MPT). This allows multiple data owners to store the same data in a single copy, enhancing storage efficiency. To maintain data security, encryption is applied both before and during the deduplication process, ensuring that sensitive information remains protected. In summary, data deduplication is a powerful method for compressing data, minimizing duplication, and optimizing transmission capacity. With encryption techniques and hash-based identification, it provides secure and efficient deduplication in cloud environments, while memory deduplication and MPT support further enhance performance and storage efficiency.
Over-the-Top (OTT) Watching Behaviour of Consumers in Secunderabad
Authors: Mr. Venkateswara Rao Neerati, Dr. D. Immanuvel
Published: December 2023

Consumer desire for selected content available on the OTT platform. Over the top media Platform A streaming media service provided directly to viewers via the Internet. Over the Top (OTT) video platform considered a luxury item, but it is now a product. In India, there was an unprecedented increase in numbers Consumers are adapting. Indian streaming services like Hot star and Jio Cinema have become more powerful with a foothold, global players like Netflix and Amazon Prime have grown tremendously in India. The data from this survey was collected through both primary data. For primary data, structured questionnaire method used to collect the data. To do so, understating how users enjoy OTT and analyzing usage patterns is essential. This research aims to identify on OTT in Secunderabad. Awareness of OTT services, time spent on OTT, preferred OTT platform, preferred program, watching OTT with companion, penetration of OTT and source of internet have been taken as a independent variables and customers satisfaction on OTT services considered and dependent variable. Further frequency analysis and chi-square test have been used to analyse the data. The results found that consumer watching behaviour associated with their satisfaction level.
Forensic Accounting's Crucial Role in Safeguarding the Integrity of India's Banking Sector
Authors: CA Hardik Patwa, Dr. Purvi Dipen Derashri
Published: December 2023

The increased use of technology in practically every industry has led to an increase in the complexity of scams. Authorities and corporate leaders face substantial difficulties as a result of the increased difficulty in preventing fraud and other risks. In order to help businesses better manage risk, control, and governance practises while also preventing fraud and accomplishing their goals, forensic accounting has become necessary due to the surge in frauds and hazards brought on by modern technology. Investigative and accounting-based forensic accounting is a technique for identifying and preventing financial crime. Forensic accounting procedures are used to conduct investigations into allegations of fraud, which involve gathering evidence, speaking with witnesses, creating reports, and testifying. Forensic accounting ought to be used while discussing financial catastrophes and the surge in commercial fraud. In all types of economic contexts, forensic accounting offers investigation and litigation support services. A new study focuses on forensic accounting in India. This study demonstrates that forensic accounting lowers fraud in India's banking industry. This conceptual study's compilation used both primary and secondary sources.
Impact of emotional intelligence of primary school teachers on workplace environment of private schools
Authors: Mrs. Namrata Abhinandan Patil, Dr. Nitin Nayak
Published: December 2023

This research paper delves into the compelling relationship between the emotional intelligence of primary school teachers and its impact on the workplace environment and job satisfaction levels in private primary schools. Through a comprehensive survey of educators, the study uncovers significant findings. The results indicate that teachers attribute their ability to understand and manage emotions, empathize with students, and cultivate positive relationships with colleagues to a nurturing classroom environment and a better overall workplace atmosphere. These findings underscore the pivotal role of emotional intelligence in creating positive educational environments, benefiting both teachers and students.
Increased export performance and growth trajectory of African continent in general and Libya in specific with Asian Nations
Authors: Ahmed Milod a anaid, Prof. R. Sivarama Prasad
Published: December 2023

The trade relations between Africa and Asian nations are consistently growing, especially with India and China. When we observe the growth trajectory of African nations and more specifically, Libya in the last three decades, the economic growth is stimulated due to the fast-paced growth and developments in oil market. The highest dollar value achieved by Libya due to the export of hydrocarbon products including oil to select Asian nations, notably India. The rising middle classes in Asia's developing economic giants, China and India are driving these steady flows, as are the rising economic growth in Sub-Saharan Africa (SSA), which is increasing the demand for Asian manufactured goods. When we study the changing pattern of exports and imports of Africa, there is a consistent growth from 1990 onwards, with even more sustained momentum after the year 2005. Among leading nations in north Africa, Libya has been striving to increase its global economic performance by liberalizing their business policies and the entire business world looks towards Libya to establish their new business foot prints.
Correlation Coefficient in Interval Valued Fermatean Picture Fuzzy Set for Crop-Based Bio-ethanol Production
Authors: Amala Kaavya .C ,Dr. A. Sahaya Sudha
Published: December 2023

This comprehensive review explores the critical aspects of bio-ethanol production in Tamil Nadu, focusing on three primary feedstocks: Sugar Cane, Corn, and Sweet Sorghum. The analysis delves into key criteria such as yield, environmental impact, and cost-effectiveness, providing valuable insights for stakeholders, researchers, and policymakers. Additionally, the study investigates the application of fuzzy logic in bio-ethanol production, emphasizing its role in addressing uncertainty and imprecision. This research serves as a vital resource for understanding the nuances of bioethanol production, enabling informed decision-making for a sustainable energy future in Tamil Nadu and beyond.
Direct Marketing Of Banks And Its Effects In Corporate Clients
Authors: Harsan Thambi. N.J., Dr. G. Jones Green
Published: December 2023

Banks use direct marketing as a tactic to attract and retain profitable business relationships with its customers. Direct marketing in this sense comprises a broad variety of customized communication tactics, such as one-on-one interactions, specific offers, and a wide range of monetary goods and services. The importance of direct marketing by banks to corporate customers is highlighted in this study as it is investigated over a wide spectrum.

This abstract presents a summary of the most important components of direct marketing in corporate banking based on a survey of the relevant literature. It highlights the importance of direct marketing in attracting new business and maintaining relationships with current customers. It also discusses the benefits and drawbacks of digital transformation, with special emphasis on data protection, ethical issues, and the evaluation of campaigns' success.
Adversarial Regularized Class Incremental Autoencoder Technique with Convolution Neural Network for Secure Clustering of Short Text in the High Dimensional Data
Authors: Kiruthika B, Dr. B. Srinivasan, Dr. P. Prabhusundhar
Published: December 2023

High Dimensional data Clustering with differential privacy has been gained significant attention recently in the large scale distributed cloud data center to cluster the high dimensional data with increased security to the data outsourced. Especially deep learning architecture based on deep adversarial regularized hierarchical autoencoder structure provides efficient clustering solution to high dimensional data containing non linear long text. However it provides class imbalance problem in terms of over-fitting and under-fitting issues with high computation complexity. To mitigate those challenges, class incremental deep adversarial regularized multi-view autoencoder technique is proposed in this work. Initially, missing value prediction using factor analysis and dimensionality reduction and data normalization is carried out to high dimensional data using principle component analysis. Principle component analysis technique eliminates the reconstruction errors occurring due to short text in the dimension space to project the sparse matrix. Sparse matrix is used to the select the discriminative features using non linear discriminant analysis. Selected feature is employed to the convolution neural network to process the selected features to establish the cluster using fully connected layer with activation function and softmax layer. Convolution Neural Network is capable of clustering structurally similar attributes and its information containing the normalized short text with increased classes. Proposed model achieves minimized intra cluster similarity and inter cluster variation by computing the data affinity of new representation. Further autoencoder model is to encode cluster structure containing attribute information by reconstructing the adjacency matrix. Decoder model retrieves or suggest the data records on basis of the attribute similarity or structure similarity of the record attribute or user attribute of the high dimensional data. Detailed experimental analysis has been performed on benchmarks datasets to compute the proposed model performance with conventional approaches using cross fold validation. The performance outcome represents that proposed architecture can produce good accuracy and effectiveness on high dimensional data containing the short-text.
Developing a system used to determine the path of raising and lowering the plane's wheel
Authors: Yousif k. A. Al-juboori
Published: December 2023

As is known, the types of aviation accidents are divided into: 40% occur in the air and 60% occur on the ground. Most accidents that occur on the ground occur during landing, and most of them are due to a defect in the wheel raising and lowering system. This case was studied and a user system was developed to determine the path of the front wheel while raising and lowering the wheel of the aircraft
The Elzaki transform and its applications for Hyers -Ulam stability of higher order differential equations using Covid 19 method
Authors: Dr. M. K. Sivasankari, K. Ramya
Published: December 2023

The Elzaki transform is being used to present the generic solution and prove the Hyers-Ulam stability of higher-order linear differential equations. In addition, the stability of homogeneous and non-homogeneous linear differential equations is discussed using the Mittag-Leffler-Hyers-Ulam method. Furthermore, such equations’ stability constants are determined.
Dense Convolution Neural Network for Automated Diabetic Retinopathy Detection
Authors: K. Geethalakshmi, Dr. V. S. Meenakshi
Published: December 2023

Diabetic Retinopathy is a diabetes complication due to uncontrolled levels of glucose in the body which leads to abnormality in the eyes which owing to distortion in the retina and leading to permanent damage of the eye or vision losses. Hence identification and classification of diabetic retinopathy through manual observation is a challenging process due to composite boundaries and features with a high degree of intraclass variation and a low degree of interclass variation.

Unsupervised machine learning algorithms have been used to classify diseases automatically based on the appearance of lesions and their characteristics. These models require more processing time and less reliability. It has been proposed that deep learning architecture can overcome these limitations as it is more efficient and accurate at detecting the lesion’s features.

The novel dense convolution neural network has been proposed with preprocessing, segmentation, feature extraction and classification steps. Laplacian filter and CLAHE techniques have been used in a preprocessing step. The region growing algorithm, Principal Component Analysis and Dense CNN have been used for segmentation, Feature extraction and classification of DR lesions.  Furthermore, the proposed model was compared with conventional classifiers in terms of Accuracy parameters. The proposed model achieves 97.88 % of Accuracy. It improves computing efficiency and minimizes network complexity.  Hence the proposed model can accurately detect the lesions in the retinal fundus images.
Posture Guardian With Smart Muscle Strain Detection And Correction Using TINYML
Authors: Dr. G. Sophia Reena
Published: December 2023

Millions of dollars are spent in the healthcare business on muscle strain-related illnesses, notably low back pain. Inadequate posture is typically the cause of lower back discomfort, especially in the transverse abdominal muscle. Intelligent monitoring systems encourage proper posture and exercise on a global scale to improve quality of life and treat this frequent issue. As pervasive intelligent technology emerges, medical equipment such as pulse monitors, hearing aids, and health monitors are being developed. This research discusses the design, development, and testing of a tiny wearable interface that uses an array of sensors to monitor and notify a person's back position in real time. A good posture helps to alleviate muscle stress during physical exertion and promotes general health. As a result, a gadget capable of detecting and correcting improper posture has become important. We created a wearable electromyography (EMG) sensor and flex sensor. EMG and flex sensors are used in our technology to monitor human posture and decide if it is right or improper. TinyML (Tiny Machine Learning) is used by our technology to assess and categorize posture data in real time, offering quick feedback and remedial help. Our TinyML-powered Smart Posture technology helps people improve their posture, reduce muscle discomfort, and improve their overall health and quality of life.
Reputation-Based Opportunistic Routing Protocol Using Q-Learning For Manet Attacked By Malicious Nodes: A Survey
Authors: P. Saranya, Dr. A. Nithya
Published: December 2023

Mobile Ad-hoc Networks (MANETs) have emerged as a viable paradigm for creating dynamic communication between mobile nodes in the absence of a permanent infrastructure. However, since MANETs are inherently decentralized and self-organizing, they are vulnerable to a variety of security concerns, especially when hostile nodes seek to interrupt communication and undermine network operations. As a consequence, developing efficient and robust routing protocols is critical to ensuring dependable and secure data transmission in MANETs. This review study gives an in-depth look into Reputation-Based Opportunistic Routing Protocols (RORPs), which use Q-Learning methods to improve MANET resistance against malicious node assaults. We provide a thorough study of current RORPs, delving into their core design ideas, underlying processes, and comparative performance evaluations. We begin with a comprehensive introduction to the fundamental concepts, issues, and security risks associated with MANETs. Following that, we explore the concepts of reputation-based routing, emphasizing the need of reputation management in discriminating between malicious and genuine nodes. The review then goes into further detail on the incorporation of Q-Learning methods into RORPs, emphasizing on how reinforcement learning mechanisms may adaptively change routing choices depending on the developing network state and the dynamic reputations of participating nodes. This article reviews 38 research papers of opportunistic routing protocol and explores the potential of computer-assisted methods for MANET.  We next divide the studied RORPs into categories depending on their approach to reputation computation, learning algorithms, and cooperative enforcement methods. Furthermore, we analyze their strengths and weaknesses in terms of resilience to different sorts of attacks, as well as their influence on network performance measures including packet delivery ratio, end-to-end latency, and throughput.
Cluster Based Improved Particle Swarm Optimization for Optimum Cluster Head Election for Energy Efficient Routing in Wireless Sensor Networks
Authors: Mrs. T. Nivetha, Dr. K. Prabhavathy
Published: December 2023

The proposed methodology addresses critical challenges in Wireless Sensor Networks (WSN), focusing on optimizing cluster head and forwarding node selection. Leveraging an enhanced Particle Swarm Algorithm (PSO), the approach prioritizes residual energy and spatial balance in node selection. It efficiently assigns cluster head nodes to ordinary nodes and selects forwarding nodes within clusters. The algorithm incorporates proximity principles to ensure balanced positioning of nodes. Through iterative iterations, the method refines node selections, favoring candidates with higher residual energy and improved spatial distribution. This approach optimizes WSN performance, enhancing data transmission efficiency and network longevity by minimizing energy consumption. Moreover, it reduces communication overhead through piggybacking and ensures dynamic node adaptation for evolving network conditions.
An Energy Efficient and Trust-based Quality of Service Routing in Mobile Adhoc Networks
Authors: G. Sripriya, Dr. T. Santha
Published: December 2023

Mobile Ad-hoc Networks are widely used in all applications and are gaining attention towards reliable data delivery and efficient energy usage. Reliability of the data delivery is assured when the routing protocols are securely reliable and energy efficient. Limited resources being the main limitation of the wireless networks, there is a lack in providing security to the data and energy utilization. Trust aware routing can resolve the resource problems in MANETS. So, to overcome these limitations, an Energy Efficient and Trust-based Quality of Service Routing in Mobile Adhoc Networks is proposed. This protocol evaluates the trust score of the nodes based on the connectivity of the nodes and ACK sincerity, based on the comprehensive trust of the node's energy consumption required and communication cost for the communication between node is evaluated. This protocol efficiently identifies the malicious nodes based on the trust score it is the additional advantage of evaluating the trust score of nodes. The simulation results prove that the proposed protocol guarantees the reliable data delivery and aid to extend the life time of the network thereby improves the throughput of the network.
Modeling and Forecasting Freight Delivery for Blocked and Unblocked Streets through Machine Learning Approaches and Analysed by Microsimulation Model
Authors: Prachi. V. Pandya, Dr. Yogesh S. Patel
Published: December 2023

Deliveries of freight on signalized city roads often lead to lane obstructions, contributing to urban traffic congestion. This issue has garnered increasing attention as traffic engineers and city planners seek sustainable solutions to meet growing demand while working within the constraints of limited road capacity. The primary objective of this study is to assess a model designed to quantify the impact of freight deliveries on both the capacity and delay experienced on signalized city roads in Ahmedabad. The model is structured similarly to the approach outlined in the Highway Capacity Manual (HCM2010). The aim of this research is to provide insights into the utilization of these analytical tools in the context of urban freight delivery policies. This research explores the estimation of delay and vehicle capacity, accounting for various factors such as delivery locations, durations, and their distinct effects on different lanes. To predict vehicle capacity and estimate delays, this study employed machine learning techniques, specifically Support Vector Machines and Artificial Neural Networks. The results demonstrate a high level of agreement between our predictions and actual observations.
Innovative Automated Diabetic Retinopathy Severity Grading for Advanced Clinical Insights
Authors: Samiya Majid, Indu Bala
Published: December 2023

Diabetic retinopathy (DR) is a hazardous eye disorder that affects the retina and may lead to vision loss and blindness, especially in diabetics. Early identification is critical for a good outcome, however diabetic retinopathy can only be diagnosed through time-consuming and labor-intensive colour fundus pictures. In order to overcome this challenge, this study proposes a Deep Learning-based strategy that useIntelligent Diabetic Retinopathy Grading through Enhanced Neural Networks (IDR-ENN)to classify retinal pictures into distinct stages of diabetic retinopathy. The proposed approach was trained on a dataset that included 2200 photos from the testing set and 11000 coloured retinal images from the training set. The simulation results suggest that the IDR-ENNbased algorithm can achieve excellent levels of accuracy, sensitivity, and specificity.In this study, we propose a method to significantly reduce the computational time for diabetic retinopathy detection. A novelIDR-ENN approach achieves a remarkable 85% reduction in training computational time for diabetic retinopathy detection. The paper's overall conclusion underlines the potential of deep learning to improve the diagnosis and grading of diabetic retinopathy, which might have a significant impact on the prevention of blindness caused by this disease.
Nonlinear Static Analysis of Building with Different Aspect Ratio
Authors: Sumit Tak, Dakshata Garg
Published: December 2023

The earthquake resistant design of structures requires that systems ought to maintain, adequately, any ground motions of an depth that would arise for the duration of their construction or in their regular use. However floor motions are unique inside the consequences they have got on structural responses. The most accurate evaluation method for structures subjected to strong ground motions is the time history analysis . This evaluation entails the integration of the equations of motion of a multi-degree-of-freedom system, MDOF, within the time domain the usage of a stepwise answer which will constitute the real reaction of a structure. This technique is time-eating even though for application in all sensible functions. The necessity for faster techniques that could make certain a dependable structural assessment or design of structures subjected to seismic loading brought about the pushover analysis.

Pushover analysis is based totally on the belief that systems oscillate predominantly inside the first mode or inside the decrease modes of vibration at some stage in a seismic occasion. This ends in a reduction of the multi-degree-of-freedom, MDOF machine, to an equivalent single-degree- of-freedom, ESDOF gadget, with properties expected by a nonlinear static evaluation of the MDOF system. The ESDOF device is then eventually subjected to a nonlinear time- history analysis or to a response spectrum analysis with consistent- ductility spectra, or damped spectra. The seismic demands calculated for the ESDOF device are converted through modal relationships to the seismic demands of the MDOF machine.

The objective of this thesis record is to emphasise the use of non-linear static procedure i.e pushover analysis a seismic overall performance assessment of R.C frames .
The influence of 4Cs factors on consumers’ attitudes towards digital advertising of Dairy Products in Saudi Arabia
Authors: Alharbi Ali Hawas, Shaizatulaqma Kamalul Ariffin
Published: December 2023

This study aims to investigate the relationship between 4Cs factors which are clarity, consistence, competitiveness and credibility as independent variables and the dairy products consumers’ attitude towards digital advertising in Saudi Arabia relying on the previous studies. 4Cs model was firstly created in 2009 by David Jobber and John Fahy in their book “Foundations of Marketing” and it was found that these four factors have significant and positive effect on the attitude of consumers towards digital advertising. This study implements the same factors in Saudi dairy market to figure out whether they influence Saudi consumers’ attitude or not. The study also explains in details 4Cs model and its applications internationally and locally as well. The 4 Cs of the marketing communication model can help in this research because the 4Cs is a customer-oriented model, which helps Dairy to focus on the relationship with consumers through digital ads.
Mechanical and Electrical Characterization of Friction Stir Welded Copper and Aluminum Alloys
Authors: Yugesh Anil Kharche, Santosh Rama Shekokar
Published: December 2023

Friction Stir Welding, often known as FSW, is a progressive solid state welding process that can be used to combine materials that are similar to one another as well as those that are completely unlike. The motivation behind this exploration is to provide an in-depth evaluation of FSW, with an accentuation on its applications in aluminum alloy welding. The fundamental ideas of FSW, the devices and hardware expected for the interaction, the benefits and drawbacks of FSW, and the different angles that effect weld quality will be generally explored. This paper covers a review that was directed to upgrade input boundaries to expand the rigidity, miniature hardness, and electrical resistivity. The micro hardness dissemination was most elevated in the weld joint's base zone, with lower readings in the center locale and middle readings on the top surface.
Effect of yoga on stress, hyperactivity and inattention of government school students in Andhra Pradesh
Authors: Dr. R. Rajeswari, B. Uma Maheswari
Published: December 2023

Introduction: School days are memorable one, where young people will be prepared to face the adult world with education of morals and attitudes. So many students put their wellness to achieve good marks, ranks and degree, hence stressed. Yoga is an exercise that can be performed by balancing the elements of the body. In this study we examined the impact of yoga and meditation on stress, hyperactivity and inattention of engineering college students.

Method: A total of 100 students are randomly selected whosr age limit is between 13-15 years. These subjects were divided into two groups, yoga group and control group. The data is collected with the questionnaires and analysed.

Results: There were no significant differences in pre test of perceived stress, hyperactivity and inattention between the two groups. The interaction of group and time was not significant in predicting the slope of change in perceived distress. Pairwise t-tests did not show a significant reduction in perceived distress for either group. There was no significant interaction in hyperactivity and significant interaction in inattention of group and time. Pairwise t-tests demonstrated a significant reduction in hyperactivity and inattention for the yoga group but not the control group.

Conclusion: Yoga may improve attention and hyperactivity for the students of government school.
A Novel Crystal Structure Prediction Using Hybrid Method
Authors: Dr. Heren Chellam G.
Published: December 2023

Chemical compositions are used to predict the crystal structure in solid state of new materials. To finding the crystalline arrangements of materials for major unsolved problems in materials science for their chemical compositions. Crystal structure prediction is one of the foremost methods for discovering new materials. In this paper, we propose a deep and machine learning model for approach to classification of the crystal structure. The more than 5000 dataset were used to predict the crystal structure. In previous work, various machine learning models were used for predicting the crystal structure. In this work, we fuse the machine learning and deep neural network algorithm. The dataset were trained and tested to this crystal structure prediction. In this model we evaluate the dataset to classification the models with high accuracy. Our approach, ANB-NET(AlextNet with Naive Bayes) classifier get the best accuracy to predict the crystal structure and time complexity is less than other model.
Measurement of Financial Performance of Public and Private Sector Banks of India
Authors: Ms. Nidhi Sharma, Dr. R. K. Tailor
Published: December 2023

The banking sector of India is one of the fast-growing sectors and considered as a main pillar of Indian Economy. Indian banking sector helps to maintain a healthy financial system and a well growth economy. The banking sector is becoming more complex day by day Hence, performance evaluation, supervision and monitoring of financial statements of Banks is compulsory to ensure the stability of the economy. As the public sector banks comes under the government undertaking, they have to work more to provide better services than private sector banks as they work for customer welfare more than the profitability while on the side public sector banks serves for profit motives. Financial performance of banks helps to review banking errors and regulations related to banking sector. Financial performance measurement is a strategy to provide a platform for improvement and best decision making. This paper is an attempt to analyze and compare the financial performance of selected public & private sector banks of India using some important financial parameters. The study is based on secondary data that were collected from annual reports of the banks. In this study some quantify metrics and descriptive analysis have used to evaluate the actual health position of the selected public & private sector banks. The uses are based on the importance of parameters on financial basis.
Controllability Analysis of Motion of Artificial Satellite Under the Effect of Oblateness of the Earth
Authors: Jaita Sharma, B. S. Ratanpal, Shivam Munshi, Vishant Shah
Published: December 2023

In this article we have studied the controllability of artificial satellite under the effect of zonal harmonic J2 in cylindrical polar coordinates systems. Seven different cases of thrusters in various directions have been analyzed and it is found that the system is controllable if we apply thrusters in either r, θ and z or θ and z direction. The equations governing motion of satellite have been linearized and Kalman controllability test is applied to check the controllability of the system. We have also derived controller u for the linearized system. The trajectory of the system has been plotted to show the controllability of the system.
Characterization of Al 5052 Based Metal Matrix Composites Reinforced with SiC and Al2O3
Authors: Mallaradhya H. M., Shridhar Deshpande Mohan V., N. B. Doddapattar,
Published: December 2023

When compared to traditional aluminium alloys, composites made of aluminium display several material characteristics, including enhanced stiffness, wear resistance, specific strength, vibration dampening, and a lower coefficient of thermal expansion. Non-metallic reinforcement aluminium matrix composites, which have advantages over the base material typically include and preferred SiC& Al2O3 as reinforcements to enhance the desired mechanical qualities. Vertex stir processing (VSP) is a novel technique used to produce hybrid composite to improve mechanical properties and microstructure refinement. In this investigation work the fabrication of all Al5052 aluminium hybrid composite was done by VSP method. An effort has been made to look at the impact of SiC& Al2O3 reinforcing elements in an Al5052 matrix.  Three combinations of composites had been prepared with different % wt of reinforcements. The microstructural alterations were examined using scanning electron microscopy (SEM).Using a Brinell hardness tester, the microhardness of samples was evaluated. A composite with the following reinforcementsAl5052/ 6%wtSiC/ 6%wt Al2O3was found to exhibit superior mechanical properties.
Tribological Behavior of SiC and Al2O3 Reinforced Aluminium Metal Matrix Composite
Authors: Shridhar Deshpande, M. Sreenivasa Reddy Mohan V., N. B. Doddapattar,
Published: December 2023

Stir casting offers greater benefits than other techniques for creating metal matrix composites. Using the high-energy stir casting process, the aluminium alloy Al5052 was strengthened with SiC and Al2O3 particles in varied weight percentages (0, 2, 4, and 6 wt.%). Through the use of XRD and SEM, the structural characterization was carried out. Investigations were made into mechanical characteristics such as hardness, tensile strength, and wear. To conduct wear studies, the load was changed while all other factors remained constant. SEM analysis was used to study the wear process. This study found that SiC and Al2O3 added to aluminium composites made them more resistant to wear. The findings demonstrated that when compared to base materials, the percentage of SiC and Al2O3 contained in the samples enhanced the mechanical characteristics, wear resistance, and hardness.
The Effect of Digital Training Transformation and Psychological Comfort on Employee Performance and Engagement.
Authors: Garima Sainger Palak Dev,
Published: December 2023

At work place, as the management practices are changing rapidly. Simultaneously, employee engagement is an important variable which express and employ person emotionally, cognitively and physically. It enables people to perform the work roles very effectively in the organisation. Transformation of the training also affects the engagement. In comparison of traditional training, digital training is more comfortable. Thus, the aim of this research is to examine the impact of comfort on employee engagement. This research is empirical in nature. Questionnaire was constructed in this regard. This was filled by almost 300 employees. Multiple regressions were applied on the data which showed the significant impact of physical comfort, functional comfort and psychological comfort on the employee engagement. This study is useful for the employers who wants to enhance the engagement of their employees in the organisation.
Key Fitting Points of Agri-food Supply Chain Collaboration and Blockchain: Literature Review on Agri-food Supply Chain Collaboration with the Integration of Blockchain Technology
Authors: Mohd. Yamani Bin Yahya, Yejiao Wang Zhongchao Hao,
Published: December 2023

The Agri-food supply chain is pivotal for sustaining and nourishing global populations. Ensuring its high-efficiency, transparency, and integrity is paramount for each party in the supply chain. This paper aims at identify the fitting points between Agri-food Supply Chain Collaboration and Blockchain Technology through a comprehensive review. Research articles and materials related to Agri-food Supply Chain Collaboration with the integration of Blockchain Technology were collected from online databases such as Scopus, CNKI, and Google Scholar for a period of 10 years (2014-2023). It first provides an overview of the characteristics of Agri-foods and Blockchain Technology, and then details the data collection and analysis process. FineReport software is used to complete content and descriptive analysis. Next, the key fitting points of Agri-food Supply Chain Collaboration and Blockchain Technology are discussed, such as smart contracts, trust, quality and safety control, and information sharing. A case analysis on DongNan Rice as for Agri-food Supply Chain Collaboration integrated with Blockchain Technology is carried out. This paper also discusses the current research gaps. It is ended with the conclusions and future research directions.
COVID Impact On Indian Stock Market And Commodity
Authors: Ritu Rani, Dr. Manju Bala
Published: December 2023

The COVID-19 pandemic that broke out in 2019 tremendously affected worldwide monetary markets, especially the products and stock markets in India. This study investigates the pandemic's perplexing repercussions on India's monetary framework. The review looks at the presentation, instability, and swings of the Indian stock market and commodity exchange all through the pandemic. It investigates the short-and long haul impacts of COVID-19 on stock records, individual values, and different products markets like energy, metals, and agribusiness. Everybody on the planet had various troubles because of the new Covid flare-up. One industry that was answerable for numerous worldwide shocks was finance, explicitly the stock and commodity markets. In the different areas of the planet, the two markets answered in special and shifted ways. This was brought about by various elements, including venture conduct, government assistance software engineers, and government activity. This paper dives further into that topic, including worldwide models and examination discoveries. This study researches how the Indian monetary markets answered the new emergency, taking a gander at how regulative choices, public activities, and financial backer mentalities impacted market elements. It likewise assesses how financial backer way of behaving, risk discernments, and exchanging designs are advancing in response to the vulnerability welcomed on by the pandemic.
Teaching Competencies of the Pre-Service Student Teacher Exchange in Southeast Asia: A Comparative Analysis of Indonesia, Thailand, and the Philippines
Authors: Cruzata Carmen L.
Published: December 2023

The study assessed the teaching competencies of Pre-Service Student Teacher Exchange (SEA) teachers in Indonesia, Thailand, and the Philippines. The results showed that the teaching competence of SEA teachers in Southeast Asia was generally high. The Philippines had the highest level of competence in four areas: content and teaching strategies, supportive learning environments, assessment and evaluation of students' learning, and personal and professional growth and development. They also had high competence in dealing with diverse students and understanding school-community relations. The study emphasizes the importance of assessing and improving teaching competencies in Southeast Asia's Pre-Service Student Teacher Exchange.
A Hamilton Path-Based Approach to DNA Sequence Analysis
Authors: O. K. Berdewad, Laeequr Raheman, V. A. Jadhav
Published: December 2023

The Hamiltonian path is a famous algorithm for determining the existence or nonexistence of a graph. However, for certain types of graphs, we can apply certain conditions to determine its existence. In this paper, we discuss the results show that there can be at most (n-1)/2 edges disjoint Hamiltonian paths in a complete graph. Also, discussed examples of its applications in different fields. Furthermore, the application of Hamilton path in the DNA sequence has been investigated.
A New Approach for Roni Based Medical Image Watermarking Using Contourlet Transform
Authors: G. Murugeswari J. Jensy Rajakumari,
Published: December 2023

The majority of modern healthcare systems rely their diagnoses on data collected from the internet. The medical imaging and patient health records are transmitted over the network via the internet-enabled healthcare (E-health) applications. In practical medical applications, the primary problem is the security of medical images transmitted over the internet. For the purpose of embedding secret data on medical images without affecting important information, a trustworthy watermarking approach is required. We provide a unique watermarking approach in this research that embeds the watermark data in the medical image's Region of Non- Interest (RONI). The medical image is subjected to a contourlet transform, and RONI is determined using contourlet coefficients. In this work, we demonstrate the embedding and extraction process of medical image watermarking using Contourlet transform. Experiments are carried out on medical images such as MRI and CT images. The performance of the proposed scheme is analyzed using the metric PSNR. The proposed scheme provides an average PSNR value of 47.1604% for MRI images and 57.5452% of PSNR for CT images.
Perception of Engineering and Science Students on Smartwatch Gadget Usage
Authors: Ravula Bharathi R. Sujatha,
Published: December 2023

Smartwatches are the foremost well-known wearable gadget and progressively subject to experimental inquire about. In later a long time, the center has moved from uncovering determinants of smartwatch selection to understanding components that cause long-term utilization. The smartwatch usage is especially gaining much importance in the college going students due to the associated attractive inbuilt beneficial features. Based on this background, the present study was emphasized on understanding the perceptions of college going students in terms of assessing Knowledge, Attitudes and Practice (KAP) on the utilization of smartwatches in day to day life. The investigation was conducted in the two main streams of engineering and sciences with the sample size of 60 members each and thus had the total sample size of 120 students. Structured questionnaire was employed to evaluate the levels of KAP as low, moderate and high and accordingly data was interpreted. The findings clearly revealed that the levels of KAP found to be better in engineering students than science students but the differences calculated were not differed significantly. The overall mean scores calculated from the students of both streams had expressed with moderate levels of knowledge, attitudes and practices components. Efforts on raising the awareness among the students might be helpful to understand the various features in depth and extend the utilization of benefits to the maximum extent.