bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating Motivation and Its Role in Workforce Efficiency: A Comparative Study of Non-Executive Employees in Western Coalfields Ltd. and Nanotechnology Firms
Authors: Avinash Prasad  , Dr. Amishi Arora
Published: December 2024

This study compares non-executive workers at Western Coalfields Ltd. with nanotechnology companies to examine the function of motivation in increasing workforce efficiency. The effect of motivation on organisational efficiency and personnel performance may differ between sectors, despite the fact that it is an essential component. This research is to investigate and contrast the effects of motivating techniques on worker productivity in a high-tech industrial environment with that of a more conventional mining sector. Various nanotechnology enterprises including the Maharashtra-based Western Coalfields Ltd. surveyed and interviewed non-executive staff to gather data. The study looks at the connection between employee efficiency and productivity and important motivating elements including job happiness, incentives, and the work environment.
Despite the universality of basic motivational concepts, the results show that their efficacy is affected by industry-specific variables in both sectors. While extrinsic motivators like performance-based compensation and career progression chances have a greater impact in nanotechnology companies, intrinsic motivators like job satisfaction and work environment play a larger role in Western Coalfields Ltd. In order to maximise labour productivity, the research stresses the requirement of customised motivating tactics that correspond with industry characteristics. Businesses that want to boost employee output via strategic incentive programs might learn a lot from this comparison study. These results have important implications for how we might improve organisational performance by creating more effective motivating techniques that meet the specific demands of workers in various industries.
SEQABC: Revolutionizing Single Document Extractive Text Summarization with Quick Artificial Bee Colony
Authors: Jyotirmayee Rautaray  , Sangram Panigrahi  , Ajit Nayak  , Meenakshi Kandpal  , Pranati Mishra
Published: December 2024

Amidst the vast ocean of textual content saturating the digital realm, Automatic Text Summarization (ATS) emerges as a critical tool. Our paper introduces an innovative single-document extractive summarization approach, harnessing the Quick Artificial Bee Colony (qABC) optimization algorithm to shift through source documents and extract pivotal lines for a concise summary. Motivated by honey bee foraging practices, qABC navigates the search space adeptly, identifying optimal snippets to construct a summary which encapsulates the core concepts of the original work. We evaluate our model by using the ROUGE score calculation on the BBC News Summary dataset, achieving improved ROUGE-1, ROUGE-2, and ROUGE-L measure results. Comparative analysis against existing algorithms underscores our model's superiority, setting new standards in single-document summarization. With our revolutionary method, come along as we alter the text summarization landscape.
Enhancing Customer Service Efficiency: Automating Responses to Customer Queries using Natural Language Processing and Radial Basis Function Neural Network (RBFNN)
Authors: Sapanakolambe  , Dr. Parminder Kaur
Published: December 2024

The insurance sector is progressively embracing Artificial Intelligence (AI) and Natural Language Processing (NLP) to enhance customer service and streamline operations. This research offers a comprehensive framework that seamlessly integrates a chatbot system, empowered by advanced natural language processing techniques, with a Radial Basis Function Neural Network (RBFNN). In this approach, a chatbot serves as the primary user interface, facilitating easy and efficient communication regarding insurance policies and claims. Natural language processing algorithms are employed to interpret user queries, extract vital information, and generate structured responses. However, the key innovation lies in the application of the RBFNN, which is adapted to model and predict various aspects of insurance documents, including policy terms, premium calculations, and claim procedures. The RBFNN's ability to capture intricate data patterns significantly enhances the accuracy and efficiency of document generation. The combined approach accelerates document creation, reduces errors, and enhances the overall customer experience in the insurance domain. The performance of the proposed technique is evaluated in the Python platform and compared with existing approaches. Empirical results and comparisons with traditional methods illustrate the advantages in terms of accuracy and efficiency. The RBFNN achieved an average accuracyof99% across the five folds. This means that the RBFNN was able to correctly generate insurance documents for 99% of the customers in the test set, on average.
Securing Access: A Combined Approach of Cued Click Points and Text Passwords for Authentication
Authors: Vaishnavi Palkonda  , Viyyapu Lokeshwari Vinya  , Niveditha Kolluri  , Sahithi Godavarthi  , Ravula Arun Kumar  , Radha Mothukuri
Published: December 2024

Authentication is one of the most important security primitives. Alphabetic password authentication is the most used type of authentication. It has been shown that this method has several shortcomings and is susceptible to several attacks, such as dictionary, brute force, and shoulder surfing attacks. The project follows a combined approach using graphic passwords in order to get over the limitations of alphanumeric passwords. This is because visuals are easier for people to recall than text. Before developing a new graphical password system, this essay seeks to highlight the usability features of the existing graphical Pass face system. It uses both text and graphic passwords to strengthen the desktop authentication process. These days, computers play a significant role in everyone's lives. This study introduces a novel multi-factor authentication (MFA) framework that combines traditional text passwords with advanced visual recognition techniques. By integrating text passwords, image selections, and point selections, our approach offers enhanced security while maintaining user-friendliness. This MFA model aims to address current limitations of single-factor authentication systems and mitigate risks associated with modern cyber threats.
Contemporary Challenges and Adaptations in Work-Life Balance Among IT Professionals in Nanotechnology
Authors: Mahima Kapoor  , Dr. Vivekanand Pandey
Published: December 2024

In the rapidly evolving field of nanotechnology within the IT industry, professionals face unique challenges in maintaining a work-life balance. This study explores the contemporary variations in work-life balance experienced by IT professionals specializing in nanotechnology, with a focus on the specific pressures and adaptations required in this highly specialized domain. Through a combination of qualitative interviews and quantitative surveys, this research identifies key factors influencing work-life balance, including the high demands of innovation, long working hours, and the need for constant upskilling. The study also examines how these professionals adapt to these challenges through flexible working arrangements, remote work options, and personal time management strategies. The findings reveal a complex interplay between professional commitments and personal well-being, highlighting the need for organizational support systems that address the unique needs of nanotechnology professionals. This research contributes to the broader understanding of work-life balance in the IT industry and offers actionable insights for organizations aiming to enhance employee well-being in high-tech, fast-paced environments.
Measurement of the Quality of Service of the members of the Savings and Credit Cooperatives of Segment 1 of Ecuador
Authors: Darwin L. Pico Gordon
Published: December 2024

The purpose of this research is to determine the relationship between the main dimensions that comprise the quality of services in savings and credit cooperatives in Segment 1 Zone 3 of Ecuador (Empathy, Purpose, Security, Responsiveness, Tangibility) as well as member satisfaction. For the study, a methodology with a qualitative-quantitative approach was implemented, using the Servperf model as a study instrument to establish the dimensions of isolables as those that make up the quality of service and customer satisfaction. Once the model was established, the survey technique was implemented to measure each of the items. For the analysis of the results, factor analysis is implemented in the exploratory phase and structural equations in the confirmatory phase. It is concluded that there is a positive relationship between the dimensions of the quality of the service offered by Savings and Credit Cooperatives.
Simple Mathematical Modelling of Thai Glutinous Rice, Khao Tan Rice Cracker
Authors: Mali Sarobol  , Wanida  Pharanat  , Suminya Teeta  , Preedok Sarobol  , Chuchat Pharanat
Published: December 2024

Rice is a major cash crop which is important both for consumption and export, as a major agricultural export product of Thailand. This study aimed to examine the drying behavior of crackers using the thin-layer GIR-HAD method at temperatures of 60, 70, and 80oC. This article presents the mathematical modeling of drying curves, provides an initial way to determine moisture diffusivity values, and evaluates the activation energy of the cracker. This study explores the utilization of the GIR-HAD drying technique on crackers to enhance the efficiency of thermal processing. Non-linear regression techniques are employed to calculate drying coefficients using mathematical models. The findings suggest that the Midilli Kucuk model is the most optimal model for all GIR-HAD drying processes. The drying rate was positively correlated with the intensity of gas-fired infrared, resulting in a decrease in the overall drying time. The range of the total color difference (∆E) was between 21.38 and 25.89. As the gas-fired infrared intensity increased, the hardness, chewiness, and fracturability of the product decreased. The moisture diffusivity was significantly enhanced when using the GIR-HAD drying method, ranging from 1.37x10-11 to 3.64×10-10 m2/s. The link between the effective diffusivity coefficient and temperature was described using an Arrhenius-type equation. The activation energy for the diffusion of moisture was calculated to be 19.27 kJ/mol. The energy consumption per unit of water removed at a temperature of 80oC was 2.93 kWh/kg, which led to a 79% reduction in energy usage compared to a temperature of 60oC. This study presents a method for preparing rice crackers that could be an advantageous choice for the industry.
Image Based Malware Detection Using Transfer Learning and Hybrid Quantum Machine Learning Models
Authors: Soja Rani S  , Mouleeswaran
Published: December 2024

Image based malware classification is getting more popular in the field of malware detection as it frees the researcher from the tedious process disassembling and analyzing the code of the malicious software. Deep learning and transfer learning methods are efficient for extracting features from images. The accuracy of prediction will be better with the quality of the features extracted. This paper carries out experiments on a benchmark dataset, Malimg using the deep learning model CNN and transfer learning models VGG16 and ResNet. Within the broader paradigm of Quantum Computing, one of the primary research areas is Quantum Machine Learning (QML). Since applying QML algorithms to solve real-world problems might potentially save time and money when compared to more traditional (or digital) machine learning methods, researchers have become quite interested in QML in recent years. We also developed a HQCCNN which is a classical and quantum hybrid version of convolutional neural network for classifying the images that integrates classical and quantum counterparts. A parameterized quantum circuit is utilized in the design of the quantum convolutional layer. To retrieve hidden information from the quantum state, linear unitary transformation is applied. Additionally, pooling operations are carried out via the quantum pooling unit. Once the quantum system evolves, the measurement of quantum state is performed, and the resulting data is inputted into a fully connected traditional layer for additional processing.
Chronological Evolution: Development and Identification of a Marathi Handwritten Character Dataset Using Deep Learning
Authors: Shazid Wahid Khandakhani  , Sachikanta Dash  , Sasmita Padhy  , Rabinarayan panda
Published: December 2024

There is a level of saturation obtained about Online or Printed character recognition about Indian script. Comparing the research of different Indian languages obtained yet a few researches has been done for Marathi characters. There are still more research remains valid while recognizing handwritten Marathi script. To overcome these challenges a proper dataset is required for experimentation. The handwritten data collected from various Marathi writers. There are four process carried out to generate dataset i.e. data collection, Pre-processing, Segmentation and classification. Data is collected from different sources like school and college students, village people and small children from Indian Anganwadi centres etc.  After collection it is arranged them into different age groups, gender and education level. In pre-processing selection of image, noise removal, normalization, conversation of grey scale to binary image, then converted binary images to inverted images.  In this paper we have focused only handwritten simple characters that included vowels, consonants, numbers and special characters. We have created 100000 of character data of different varieties compared with other availability dataset and to find its better accuracy we have used `deep learning approach of keras like InceptionV3 and Resnet with different batch size and its epoch level and find its accuracy. We obtain an accuracy of 83.42% for the used model for our collected dataset. Our handwritten data is available publically and it will be useful for the researcher to continue their future research work and for the time being it can be available on request.
Coefficient Characterization For Some Spiral-Like Subclasses Of Generalized Rational Univalent Functions
Authors: S. Lalitha  , G. Padma  , K.V.Vidyasagar
Published: December 2024

The aim of this paper is to introduce Spiral-like subclasses SP_+^λ (b_1,α) and SP_+ (λ,ρ) of generalized rational functions and study the geometric properties like coefficient characterization, growth and distortion bounds.
Analytical Method Development And Validation For Determination Of Nitrosamine Impurities N-Nitrosodibutylamine (Ndba) & Nitroso Of N-Dimethyl Erythromycin In Sertraline Hcl Drug Substance By Lc-Ms/Ms
Authors: Sanjeev Kumar Giri  , Upama Misra
Published: December 2024

This work provides a thorough assessment of a method for measuring N-nitrosodibutylamine and N-nitroso of N-dimethyl erythromycin A, two substances with substantial regulatory interest because of their health hazards. The performance characteristics of the method were thoroughly investigated through a series of experiments, including standard solution analysis, quantitation and detection limit determination, linearity assessment, precision testing at various levels, and accuracy evaluation via recovery studies. The findings demonstrate that the technique performs exceptionally well in terms of linearity, accuracy, and precision over a broad range of concentrations. The results indicate that the suggested approach has the potential to accurately quantify these chemicals in a range of sample matrices, making it a useful tool for regulatory compliance and quality assurance in associated industries.
Effect Of Li-Co- Doping On The Structure And Optical Properties Of Sm3+ Doped PVP Capped Mgal2o4 Nanophosphors
Authors: Deepa Rani S
Published: December 2024

In this study, rare earth ion (Sm3+) doped and Li-co-doped magnesium aluminate (MgAl2O4) nanoparticles were synthesized using polymerised sol-gel combustion method. A series of trivalent Samarium (Sm3+) activated magnesium aluminate nanophosphors (Mg1-xSmxAl2O4 (x = 0.02, 0.04, 0.06, 0.08 and 0.10)) and Li-co-doped MgAl2O4: Sm3+nanophosphors were synthesized and their structural, optical and luminescence properties were studied using different characterization techniques such as X-ray diffraction (XRD), Fourier transform infrared spectra (FTIR), Diffuse reflectance spectra (DRS), X-ray photoelectron spectroscopy (XPS) and photoluminescence (PL) spectroscopy.The polymer used was polyvinylpyrrolidone(PVP) which acted as a reducing agent and stabilizing agent during synthesis. The average crystallite size,which was estimated from the x-ray diffraction analysis of all prepared nanophosphors,was around 10 nm. FTIR spectra showed various fingerprint bands in the range 400-700 cm-1. DRS spectra showed strong absorption in the UV region at 401 nm.XPS analysis confirmed the presence of different elements in the prepared nanophosphors. Photoluminescence emission spectra showed many characteristic peaks in the visible region for all samples upon 401 nm excitation. The most prominent peak was at 649 nm wavelength corresponding to the 4G5/2 – 6H9/2 transition. Li-co-doping enhances the emission intensity by almost 1.98 % without change in the peak positions. The results suggest that the synthesized nanophosphors could be an excellent choice for orange-red emitting luminescent materials for UV-excited phosphor-converted white light-emitting diodes and for various photonic applications.
Implementation of the BFTIRS Algorithm for Integrating Distributed Ledgers with Supply Chain Network
Authors: K. S. Chandrasekaran  , V. Mahalakshmi  , M. R. Anantha Padmanaban
Published: December 2024

Over the past ten years, blockchain technology has significantly captured interest in various application fields. Originally devised for the Bitcoin peer-to-peer cryptocurrency network, extensive research now explores integrating blockchain with various other service domains. The technology is celebrated for its decentralized structure, robust security, immutability, and transparency. In blockchain systems, consensus algorithms play a crucial role in establishing unanimous agreement among participants within a distributed computing environment, facilitating the addition of new blocks to the blockchain network. The effectiveness and security of the network largely hinge on the performance of these consensus algorithms. However, existing consensus algorithms face challenges with throughput, latency, and communication complexity. To address these issues, an enhanced consensus algorithm known as Intuitive Random Selection based Byzantine Fault Tolerant (BFTIRS) is introduced. This algorithm optimizes the consensus process by selecting a subset of nodes, thereby reducing network complexity and enhancing efficiency without sacrificing security. To tackle scalability issues in blockchains, a hierarchical BFTIRS algorithm that incorporates sharding is developed. This approach segments network participants into local and global consensus groups, each conducting the consensus process independently. Performance evaluations of this algorithm show improvements in both efficiency and security over existing solutions.
Secure Multi-Party Computation For Data Mining In Cryptographically Protected Environments
Authors: Ashish Jain  , Anjali Dixit  , Saish Pawar  , Amit Kumar Jain  , Atul kumar  , Yogesh Bhomia  , Jean George  , Heena Shrimali
Published: December 2024

A sophisticated cryptographic paradigm known as Secure Multi-Party Computation (SMPC) allows several parties to work together to calculate a function over their private inputs while maintaining the confidentiality of those inputs. SMPC makes it easier to share findings and insights about data mining in environments with cryptography protection while keeping individual data private. This research delves into the basic workings of SMPC, highlighting its potential uses in a range of industries, such as finance and healthcare, where sensitive data can obstruct data sharing. We discuss the difficulties in putting SMPC protocols into practice, like complexity and performance overhead, and we point out improvements in protocol efficiency that have made useful applications possible. Moreover, this study describes typical applications of SMPC, including secure auctions, cooperative machine learning, and privacy-preserving data processing. The goal of this study is to present a thorough overview of the function that SMPC plays in enabling safe and effective data mining operations by analysing current methods and investigating emerging trends. Our results highlight the need for continued investigation and improvement of SMPC techniques in order to promote broad use across many industries, eventually augmenting privacy protection and permitting beneficial data cooperation.
Stories in Preschool Education: A Bibliometric Analysis
Authors: Herrera Rojas Jessica Juliana  , Herrera Rojas Mirela Zuceti  , Ana Cecilia Sotelo Núñez  , Oscar López Regalado  , Barrueto Chunga Luis Enrique  , Mogollón Torres Daniel Alberto
Published: December 2024

The article "Stories in Preschool Education: A Bibliometric Analysis" explores how children's stories benefit the cognitive, linguistic and socio-emotional development of preschool children, with the Objective: Analyzing the conceptual contribution according to the categories under study of the scientific articles; Identify the evolution of reference spectroscopy, annual production, citations per year, the most relevant and cited sources; Identify the evolution of reference spectroscopy, annual production, citations per year, the most relevant and cited sources; Identify the annual scientific production and academic impact through the average number of citations per year, the most relevant and cited sources, the application of Bradford's Law; describe the influence and international distribution of the authors, highlighting major contributions by country and affiliations; analyze the influence and international distribution of authors, highlighting the main contributions by country and affiliations, word dynamics, visualization of word clouds, in addition to building a co-occurrence network to decode the intellectual structure and thematic relationships underlying the research. Methodology: Involves a systematic review of studies published between 2013 and 2023, selecting those that are empirically relevant and applying bibliometric analysis tools such as Biblioshiny. Conclusion: The importance of adjusting children's stories to the abilities and cultural environment of preschool children is highlighted, since they are fundamental for their comprehensive development. The effective use of children's stories as educational tools depends on their adaptation to the specific needs of each child.
A Comprehensive Review of Various Factors Impacting Human Health: Implications for Pharmaceutical Education
Authors: Kiran C. Mahajan  , Aditya Suryawanshi  , Sujata Shendage  , Shrikant Agawane  , Anushka Kurhade  , Himanshu Bhor
Published: December 2024

Aim: This review aims to explore the multifaceted factors influencing human healthspecifically lifestyle decisions, environmental effects, and genetic predispositions and their implications for pharmaceutical education. Purpose: The purpose is to equip aspiring pharmacists with a comprehensive understanding of how these factors interact to effectively address health issues in diverse populations. Discussion: The discussion highlights the critical role of pharmacogenomics in tailoring medications to individual genetic profiles, thereby enhancing treatment efficacy and minimizing adverse reactions. It also emphasizes the significant impact of environmental factors, such as living conditions, healthcare access, and socioeconomic status, on health outcomes, advocating for a focus on systemic health inequities to promote equitable healthcare access. Additionally, lifestyle choices, including nutrition and exercise, are discussed as key components that can be influenced through education, fostering a proactive approach to patient care. To prepare students for real-world challenges, the review underscores the necessity of an interdisciplinary educational approach that integrates knowledge from behavioral sciences, social work, and public health, thereby enhancing critical thinking and problem-solving skills. Conclusion: the paper emphasizes the need for an integrated educational framework that synthesizes recent research on health determinants, aiming to cultivate a new generation of pharmacists equipped to improve health outcomes for various groups by fostering a deeper understanding of these interconnected issues.
Blockchain Technology as a Decentralized Solution for Data Security and Privacy: Applications Beyond Cryptocurrencies in Supply Chain Management and Healthcare
Authors: M. Shakila  , Dr. S. Pandiaraj  , S. Leoni Sharmila  , Kumar T R K  , Vanaja Ramalingam  , Dr. M. Prakash
Published: December 2024

Decentralized and promising, blockchain technology brings enhanced security for data along with privacy issues particularly in health and supply chains. Here, we aim to show the usage of blockchain towards the secure data of devices that come under Internet of Medical Things or IoMT by providing data integrity and further sensitive medical information protection. A secure, transparent, and tamper-proof system in health care management can be supported by an IoT framework that uses blockchain. Four data security and privacy algorithms based on blockchain have been considered and tested with excellent results to show a clear indication of how much prevention in un-authorized access can be done in ensuring authenticity, tracking products in the medical supply chain. Some experiments were demonstrated where the blockchain-enabled system had reduced the data breach by 95% from traditional systems. Supply chain traceability had improved by 92%. The combination of machine learning and blockchain increased real-time threat detection by 88%. The outcome shows that blockchain can deal with problems like tampering, privacy violation, and counterfeiting in medical products. However, scalability of applications and adoption by regulators are two major challenges towards full integration. This paper aids in the development of a complete holistic framework that may make use of the usage of blockchain to secure health care and other supply chain systems to allow further avenues for digitizing data safety.
Applied Mathematics in the Design of Efficient Algorithms for Big Data Analytics
Authors: Dr Charudatta Dattatraya Bele  , Dr Basavaraj Patil  , Dr Dattatreya P. Mankame  , Baburao Gaddala  , Nidhi Jindal  , Gauri Jindal
Published: December 2024

This study explores the application of applied mathematics to big data analytics, exploring optimization through algorithms for effective processing in a computational efficiency with assurance of accuracy on any related domain. This paper further suggests and examines four types of algorithms, which include optimization-based algorithms, machine learning models, digital twin frameworks, and methods for the protection of privacy. The experimental results show that the optimization algorithm has reduced the processing time by 25%. On the other hand, the machine learning models enhanced the models predictive capabilities by 15 percent compared to conventional techniques. The major achievements of the proposed framework include; the Digital Twin framework enhanced the processing efficiency by 20% of the real time data simulation of manufacturing, and the Privacy Protection algorithm enhanced non-disclosure of data by 30%. Comparison with related work was also made to measure the performance of these algorithms and it was witnessed that the introduced techniques offer better accuracy, less time required and better scalability than existing models. The results will demonstrate the critical role of applied mathematics in addressing big data management and analytics issues across the sectors such as health care, manufacturing, and energy. Building on this previous work, it has set the groundwork for subsequent research to engage in the further development of more efficient and safer algorithms for the analysis of big data in real time, with strict regard to data privacy.
Artificial Intelligence in Supply Chain Management
Authors: Amar Jyoti Borah  , Bidyut Kumar Das  , Sanjose A Thomas  , R. Saravanan  , Dr. Syed Salim  , Dr. K K Dhande
Published: December 2024

This paper examines the use of AI in optimizing SCM, particularly efficiency, sustainability, and resilience. The study uses algorithms driven by AI, including machine learning, predictive analytics, and multi-objective optimization to enhance decision-making processes and streamline SCM operations. These algorithms are Genetic Algorithm (GA), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Particle Swarm Optimization (PSO). All of these were applied to real-world data related to the supply chain. The results showed 23% savings in operational costs by GA, 18% better demand forecasting accuracy by ANN, 15% decrease in lead times by SVM, and 20% better supply chain flexibility by PSO. It was also demonstrated that AI, when integrated with blockchain technology, increases the resilience of supply chains. The disruptions are decreased by a significant 30%. When compared to related work, it becomes evident that the optimization potential of AI for SCM has been overlooked; this study has well delineated the step towards improved operational efficiency, cost cuts, and sustainability in the global supply chain. It gives the present scenario, the key challenges of an industry, the role AI played in meeting those demands, and how it holds future scope for development in most diversified industries.
Development of Intrusion Detection & Prevention System (IDPS) Using SVM Model with New Kernel Function for Security Solutions
Authors: Itfaq Ahmad Mir  , Anwaar Ahmad Wani
Published: December 2024

In digital era, every aspect has an online presence. Almost all the activities whether communication, financial transactions, shopping, social networking etc are carried out digitally. All these activities involve sharing of sensitive data and information, hence securing information has become inevitable for almost all agencies whether public or private. Confidentiality of information must be maintained for integrity of data, various studies have been proposed, to prevent the intrusion and cyber-attacks. Despite this, the hackers succeed in breaking the barriers and can access the data unauthentically. The proposed research study has produced a versatile Intrusion Detection and Prevention System (IDPS) to detect and prevent the intrusion of hackers in the web. To further enhance the performance of the proposed IDPS, SVM (Support Vector Machine) classification model, along with a linear kernel is used to classify the intruders, by using probability concepts in the SVM kernel function, which uses probability concepts to classify intruders and authenticated web users, suggested IDS is tuned for accuracy by using SVM classification model. An innovative SVM classification algorithm along with a new kernel function is used to develop and implement the new IDPS. This research work provides the overview on current active cyber-attacks (intrusions) as well as available intrusion detection and prevention methods.
Enhancement Of Finger State Progress Model for Markerless Virtual Fine Motor Stroke Rehabilitation
Authors: Mohd Amir Idzham Iberahim  , Syadiah Nor Wan Shamsuddin  , Mokhairi Makhtar  , Yousef A.Baker El-Ebiary
Published: December 2024

The use of machine learning as a tool for analyzing and pattern extraction from the results is widely applied in various medical applications in stroke rehabilitation. It will help the therapist to make a consistent and precise evaluation for a viable recommendation for an optimal future exercise to improve the patient’s progress. The objective of this study is to produce a prediction model to analyze patient finger rehabilitation progress by comparing four regression classifiers' efficiency. In this study, we proposed an Enhancement of the Finger State Progress (E-FSP) model to produce prediction results of progress and performance which also consists of a markerless VR application using markerless motion sensors and can capture kinematic data through Time-based Simplified Denavit Heartenberg (TSDH) model and measure the results of rehabilitation exercises through the integration of Finger State Progress (FSP) model. 30 patients have undergone rehabilitation sessions using VR applications in the Kuala Nerus Rehabilitation and Hemodialysis Health Organization. The study shows the result of an optimum evaluation is the RandomForest classifier which has the lowest Mean Absolute Error (MAE) value of 8.26 and Root Mean Square Error (RMSE) value of 12.38. In conclusion, The VR application and machine learning can produce a very promising combination of attractive visual and viable prediction analysis for virtual fine motor stroke rehabilitation.
A Cluster-Based Dynamic Algorithm For Application Mapping On The Noc Architectures
Authors: Savita Gautam  , Abdus Samad  , M. Sarosh Umar
Published: December 2024

The effectiveness of Network on Chip (NoC) is highly dependent on how well the real-time tasks are mapped on NoC communication infrastructure. In this paper an efficient dynamic scheduling algorithm is proposed and implemented on standard Mesh topology as well as on similar recently proposed LECΔ topology. The performance of proposed algorithm is evaluated in terms of Load Imbalance Factor (LIF), makespan, communication cost and system throughput. The results are evaluated with different task distribution profile using Java-based simulator. The behavior of load distribution is estimated and compared with different performance metrices on two considered topologies. The simulation results show an improvement of 32.57% in LIF at lesser execution time. When comparing the performance of two networks in terms of makespan, the results show a reduction of 43.7% in case of 4 x 4 LECΔ topology as compared to 4 x 4 Mesh topology. The comparative study in terms of LIF, makespan and system throughput is carried out by implementing other existing techniques on both the considered NoC architectures which shows that the proposed approach is an optimized solution for task mapping at considerable reduced cost on considered NoC architectures. The analysis of simulation results shows that the proposed approach may be considered a better choice and implemented on similar NoC architectures as well as other than mesh-based topologies.
Balancing Ethical Imperatives: Examining The Role Of 'Responsible Ai' In International Humanitarian Law In Warfare
Authors: Priya Mondal  , Dr. Nagesh Sawant  , Dr. Ramratan Dhumal
Published: December 2024

For the foreseeable future , humans will continue to be engaged in the utilisation of all military systems. Commanders will retain absolute authority over certain critical variables that will be encrypted inside the systems. At minimum, they will include target attributes, geographical/spatial limits, and time constraints. The weapon system is tasked with selecting and engaging specific targets that adhere to these general constraints. Another facet of command and control is the ability to oversee the system, enabling the commander or weapons operator to detect and disable any weapons system displaying indications of malfunction.
Weapons systems provide significant military utility and enable commanders to maintain accountability for their actions and responsibility for the overall outcome of operations within certain limits. These criteria provide commanders with the ability to accomplish mission objectives while following legal standards and rules of engagement, retaining full knowledge of the situation, and working towards the overarching strategic and military goals of the operation.
While humans will retain strategic and operational authority, the weapon system will operate autonomously inside the tactical loop. Furthermore, several crucial decisions regarding targeting will be made during earlier stages of the targeting process and at locations that are farther away from the planned strike zone. Hence, a meticulous human examination of the functioning of an entirely autonomous weaponry system This may be accomplished in a mostly theoretical setting, with little awareness of physical hazards or specific vulnerabilities of civilians. Commanders should use more attention while deploying and using these technologies during operations .
Exploring The Interconnectedness Of Four Language Skills In Effective Lesson Planning
Authors: Habab Osman Hassan Ahmed  , Wafa Fadul Mohammed Ali  , Nahid Awad Kheri
Published: December 2024

The interrelationship among listening, speaking, reading, and writing skills plays a critical role in language acquisition and pedagogy. This study delves into the practical implications of integrating these four language skills within a single lesson hour. By examining how these skills reinforce each other, the research highlights the importance of a balanced approach to language teaching, where each skill complements the others to enhance overall language proficiency. The findings suggest that a well-structured lesson plan that simultaneously develops all four skills can lead to improved student engagement, comprehension, and linguistic competence. This study provides educators with practical strategies for designing lessons that maximize the synergistic benefits of these interconnected language skills.
OTP Generation Using LFSR To Validate Applicant Login Account
Authors: Manoj Kumar  , Karthik Shakyawar  , Navin
Published: December 2024

Most of the user login accounts used on web sites and mobile apps are authenticated by using a static password. Static password-based authentication systems are not very secure; they are easy to guess, and users also have to remember them. To avoid this problem, an OTP (one time password)-based password generation system for validating user login accounts is developed in this paper. Three types of 13-bit linear feedback shift register (LFSR) circuits are developed using XNOR gates, and they are synthesized and simulated using the Xilinx Vivado 2015.2 tool. Proposed LFSR circuits do not use any initial seed value, and they generate random numbers at a frequency of 200 MHz Random numbers generated by LFSR circuits are written to text files, and four characters of decimal digits are used to generate an OTP In this work, two forms are developed by the Anvil tool, where the user has to enter his email address, date of birth, and OTP to login to the system. The Anvil tool randomly selects the four-character OTP from the text file, and it is sent to the user's email address for validating their login accounts. Developed LFSR circuits consume area (LUTs) in the range of 1-2, and they consume 0.192W–0.193W of power.
Enhancing Salesforce with Machine Learning: Predictive Analytics for Optimized Workflow Automation
Authors: Shohoni Mahabub  , Israt Jahan  , Md Nakibul Islam
Published: December 2024

This study explores the integration of machine learning into Salesforce workflows to enhance automation and optimize operational efficiency. The research addresses the limitations of traditional Salesforce automation, which often falls short in managing the increasing complexity of data and workflows. The study employed a range of machine learning algorithms, including Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting, applied to Salesforce data to assess their impact on task completion times, error rates, and user satisfaction. The analysis revealed that machine learning models significantly reduced task completion times, lowered error rates, and improved user satisfaction by automating routine tasks and providing predictive insights. The findings indicate that integrating machine learning into Salesforce can lead to substantial improvements in workflow efficiency and decision-making capabilities. The study concludes that while the potential benefits are considerable, further research using real-world data and a broader range of algorithms is necessary to fully validate and extend these findings.
Significant Impact of Prognostic Molecular Markers in Papillary Thyroid Carcinoma
Authors: Samar Usama Hassan  , Sabah A.M. fadel  , Mahmoud Abdelhameid Mahmoud  , Asmaa M Ahmed
Published: December 2024

Papillary thyroid carcinoma (PTC) is the most common subtype of thyroid cancer and is the most prevalent one in the endocrine system. According to worldwide reports, its incidence has been increasing in recent decades. The discovery of DNA sequencing methods and molecular diagnostic techniques provides an insight into the understanding of PTC molecular biology which opens a new perspective in finding molecular markers. Expanded studies have been executed on identifying the genes involved in PTC development and their prognosis. Currently, clinical and pathological features of tumor (such as size, extra thyroid extension, lymph node invasion, and capsular invasion) are used to predict the prognosis of PTC. The present article provides an updated condensed overview of PTC molecular alterations involved and recent biomarker investigations.
Design and Fabrication of Lab Scale Model on Water Pump Operated by Wind Energy Source
Authors: K. Viswanath  , K.Anandavelu
Published: December 2024

The principle wellspring of energy has been coal, oil, flammable gas, atomic vitality, wood and coal. Be that as it may, every one of these sources are constrained and are the fundamental driver of contamination and this has prompted improvement and more spotlight on maintainable vitality gracefully with least contamination impacts. Consequently research and examination has demonstrated that breeze vitality, sun based vitality and biomass are the most noticeable answers for the above issues since they are eco-accommodating and promptly accessible in nature. Wind vitality can be created utilizing windmills that give mechanical vitality that is utilized legitimately on hardware for example water siphon and processor; or wind turbines that give electrical vitality. The primary goal of our venture was to plan a windmill and thusly our degree will be restricted to a windmill for water siphoning water. Windmills are grouped into two fundamental sorts dependent on the hub about which they pivot. Horizontal axis has the fundamental rotor shaft running on a level plane and if the rotor must be arranged toward the breeze, a breeze vane is combined with a servomotor. Vertical pivot has the principle rotor shaft running vertically. The rotor gathering can have at least two sharp edges relying upon the ideal robustness. In our structure, we utilized a level pivot windmill with 3 cutting edges, rigidity 31 N/mm², thickness 0.855 g/cm3, length of sharp edge 30 inch (2.5 feet) and quality of edge is 4500-6000 psi.
Development of AI Adaptive, and Recommendation Course on LMS for Optimization of Digital Learning Services
Authors: Ikhsan Ikhsan  , Muhammad Ashar  , Deni Darmawan
Published: December 2024

The Use of Adaptive Feedback using Artificial Intelligence (AI) in a Learning Management System (LMS) is an innovative way to provide automatically tailored feedback to users based on their behavior and performance. This important research process also considers feedback from AI tailored to each user's individual needs and level of understanding. Research methodology conducted by making prototype LMS platform model based on AI through creating personalization or learning style for each user, developing a feedback system for users, analyzing needs and designing user learning patterns, integration of learning resources, and data analysis and decision-making, also creating learning routes and monitoring user progress. Research process was designed by AI algorithms to systematically collect and analyse based on data, including course types, student performance, engagement metrics, and interaction patterns, teachers and score of assessment specific to the open and distance education context. The AI-driven adaptive feedback system provides real-time, personalized feedback, emphasizing strengths, pinpointing areas for leaning. Seamlessly integrated into the LMS, the user-friendly interface features a visual analytics dashboard, benchmarking capabilities, Research conclusion has represented a significant AI to elevate reflective feedback on course recommendation and materials personally of students before, ultimately enhancing the quality of education services.
Analysis of Harmonic Contamination at the Output of a 3kVA Hybrid Inverter
Authors: A. D. Soria-Soria  , Y. A. Urresty-Noguera  , F. H. Vásquez-Teneda  , M. R. Secundino
Published: December 2024

This study examines harmonic pollution in the output of a 3kVA hybrid inverter, identifying the main sources of distortion and proposing solutions to mitigate its effects on the power system. The objective is to evaluate the power quality and propose improvements for optimal and reliable operation. The importance of controlling and reducing harmonic pollution to maintain the efficiency and reliability of the inverter and connected devices is emphasized. The research combines a theoretical and experimental approach with quantitative analysis and simulations, employing the methods of "Wave Recording" and passive filters. The results identified the sources of harmonic distortion under different load conditions, evaluated their impact on system efficiency and analyzed effective solutions, such as the use of tuned filters to minimize losses by 5 % to 6 % and the implementation of "Balanced Load" according to IEE Std 519.
A Novel Data Locality-Aware Scheduler for Improved Cloud Performance
Authors: Tapankumar A. Kakani  , Rajkumar Muthusamy  , Anuteja Reddy Neravetla  , Karim Hudani  , Ketan Gupta  , Nasmin Jiwani
Published: December 2024

Scheduling tasks closer to stored data can significantly reduce network traffic. By optimizing for data locality, tasks can be matched with their associated data on the same node, minimizing the need for data transfer. However, many existing schedulers overlook the balance between task placement, data transfer overhead, and bandwidth consumption, focusing only on locality. We present a novel Genetic Algorithm-based Data Locality Scheduler (GADLS), which aims to balance time consumption and network bandwidth while improving data locality and throughput. GADLS employs a genetic algorithm to model data-task placement as a chromosome, optimizing for configurations that maximize locality and minimize bandwidth use. It integrates a multi-objective fitness function, balancing data movement, network traffic, and task runtime, with adaptive mutation and crossover mechanisms to explore a broad range of placement options. Through this approach, GADLS achieves an improvement of 18% in data locality rate and a 27% increase in throughput, demonstrating its effectiveness in maximizing resource utilization and enhancing performance in distributed environments.