bluuebunny/crossref_metadata_2025 huggingface dataset:
Three Modified Three-Term Conjugate Gradient Method in Non- linear Optimization
Authors: Farhan Khalaf Muord
Published: November 2024

In this work several a Tree –Term Conjugate -Gradient  (CG) – algorithms are  Modified; and  that satisfies  the sufficient descent condition and a global convergence . primariliy, us- have derive three new methods of this type algorithms . and we compared with (HS) , (PR) and (LS). Algorithms by using (15) Well-Known test functions. And by using Wolfe and Strong Wolfe condition on every  iteration. These new algorithms are used different memory less (BFGS) algorithms. Our new  numerical results are proved very robust and efficient than the same classes of algorithms.
Implementation and Mathematical Modelling of Data Sharing and Privacy Preserving Technique in a Blockchain Network by Using Machine Learning Algorithms
Authors: Madhuri Vishwas Shinde
Published: November 2024

Blockchain technology has emerged as a reliable and secure decentralized network with multifaceted applications in banking, finance, insurance, healthcare, and business domains. Recent trends within blockchain communities indicate a growing interest in deploying machine learning models to extract valuable insights from extensive, geographically dispersed datasets owned by individual participants. To enable learning models without centralized data repositories, extensive research has focused on developing machine learning algorithms tailored for blockchain networks. However, despite numerous proposals, privacy and security concerns remain inadequately addressed, revealing vulnerabilities in architecture and operational efficiency limitations. The proposed ensemble machine learning model presents a pioneering solution, aiming to systematically resolve privacy, security, and performance issues within blockchain systems. The novelty of this approach lies in its targeted resolution of critical challenges at the intersection of blockchain technology and machine learning. While prior research has delved into integrating machine learning into blockchain networks, this model stands out by introducing a privacy-centric methodology that systematically addresses the core issues of privacy, security, and performance. Moreover, the proposed model promises enhanced resilience against adversarial attacks compared to other aggregation rules within a differentially private scenario. The innovative ensemble machine learning model for blockchain exhibits a significant 20% improvement in data privacy, a 15% boost in security, and a notable 25% enhancement in operational performance.
RSM and ANN Modeling Techniques to Forecast How Various Parameters will Affect the Improvement of Electronics Cooling using Radial Heat Sink.
Authors: Mangesh Dadarao Shende
Published: November 2024

This research provides the mathematical modeling for temperature difference for natural convection using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) based modelling. Length of fin (L), height of fin (H), number of fins (N) and the heat input (Q) for the Radial heat sink are the parameters selected under natural convection heat transfer. Looking at the pattern of the data, feed forward back propagation type neural network is chosen. The RSM mathematical model of temperature difference is used to compare the performance of the created ANN models. ANN Simulations proved to be successful in terms of agreement with actual values of experimentation. ANN simulations perform accurate to validate the experimental results and the results obtained from the RSM for the output under natural convection. The optimum values for the dimensional parameters namely  length of fin, height of the fin, number of fins are obtained by RSM method. Also the optimum operating parameter that is heat input for minimum temperature difference is obtained.
Biomedical ECG Signal Compression by Combining Wavelet Transform and Unsupervised Autoencoder Techniques
Authors: Abhijeet. Y. Men
Published: November 2024

Abstract: Storing biomedical signal data is a significant concern in the field of biomedical signal processing because it requires a substantial amount of storage space. These biomedical signals often contain hours of information, necessitating extensive storage capacity. Researchers employ Electrocardiogram (ECG) signal compression techniques to address this issue. However, compressing the ECG signal could potentially lead to the loss of important features or data. This loss may adversely impact the accurate analysis of heart patient conditions. This paper introduces a novel approach for compressing ECG signals by the combined power of Wavelet Transform (WT) and unsupervised Autoencoder (AE) techniques. ECG signal compression plays a crucial role in reducing data storage and transmission requirements without compromising diagnostic accuracy. The proposed methodology, termed WT-AutoEncoder, integrates Wavelet Transform for signal decomposition and feature extraction, followed by an Autoencoder for efficient encoding and reconstruction of the original signal. Unlike conventional methods, this approach utilizes the strengths of both WT and AE to achieve high compression ratios while preserving signal accuracy. The study conducts comprehensive experiments and evaluations using benchmark MIT-BIH datasets to assess the performance of the WT-AutoEncoder on specific records (101-109) in terms of compression ratio of 32.31, PRDN of 33.26, PRD of 3.06, and QS of 13.49. The final results demonstrate the effectiveness of the proposed model compared with existing methods in compressing ECG signals while maintaining high accuracy, suggesting its potential for secure transmission and storage in various healthcare applications.
Mathematical Approach towards Adaptive Machine Learning Models for Dynamic Security Threats in Industrial IoT
Authors: Kapil Keshao Wankhade
Published: November 2024

The always changing nature of security dangers within the Industrialization Internet of Things (IIoT) makes it exceptionally difficult for standard security strategies to work. Since of this, versatile machine learning (ML) models got to be made that can respond to unused dangers in genuine time. This study proposes the utilize of versatile machine learning procedures as a better approach to form IIoT situations more secure. To begin with, the paper addresses the most security issues that IIoT frameworks have, cantering on how the frameworks must be able to alter to address unused dangers. Cutting edge online dangers are exceedingly cleverly and alter quickly, frequently resisting conventional security measures. This appears how vital it is to discover arrangements that can alter rapidly. The proposed customizable machine learning show employments real-time information streams sent by IIoT gadgets to keep risk recognizable proof and defence strategies up to date. These models utilize strategies such as design acknowledgment, peculiarity discovery, and prescient analytics to distinguish bizarre behavior which will indicate a security breach. By learning from past information and adjusting to modern designs and patterns, the models can distinguish known and obscure dangers more precisely and rapidly. A key angle that creates this customizable machine learning models work so well is the truth that they can operate autonomously in IIoT situations. They complement standard rule-based strategies with energetic data-driven bits of knowledge and work consistently with current security frameworks. This combination permits you to halt dangers some time recently they happen and react rapidly, decreasing the probability of a security occurrence and the harm it may cause. The study also covers the technical pieces and strategies required to convey adaptable machine learning in an IIoT environment. She talks approximately the challenges that emerge in collecting and planning information, preparing models, and drawing conclusions in genuine time. She emphasizes the significance of having a high-performance, versatile framework that can handle huge sums of information of different sorts. The study talks about important issues like privacy, data security, and following the rules when using flexible machine learning models in IIoT ecosystems that are sensitive. It supports open and responsible methods to make sure that release is done in an ethical way and that operations are safe from threats from enemies.
BRMECQN: Design of an efficient integration of Bioinspired Routing Model with Energy efficient Clustering for high QoS Wireless Networks
Authors: Pradnya A. Jadhav
Published: November 2024

In this dynamic domain of communication in wireless network technology, the need for advanced models that boost the Quality of Service (QoS) parameters concerning energy efficiency, speed, throughput, packet delivery ratio, and minimization of jitter is of prime concern. Traditional routing and clustering methods in wireless networks are often confronted with limitations such as suboptimal resource utilization, reduced energy efficiency, and lower QoS metrics. In this regard, the present work introduces an innovative integration of bioinspired routing models with energy-efficient clustering to enhance the performance of wireless networks. A novel Fan-Shaped Clustering approach is employed in the proposed model for a proper grouping of the network nodes, which can reduce the energy consumption and enhance the load balancing across the network. The model performs a further integration of Elephant Herding Optimization and Bat Optimization algorithms following the clustering. This integration has been done to develop an optimal route finding mechanism between the clustered nodes through its bioinspired approach, to utilize the global search capability of Elephant Herding Optimization, and the proficiency of Bat Optimizations in local searches. The proposed model, when tested empirically on different configurations of networks, presents significant improvements over methods in practice. The results suggest a 10.5% improvement in energy efficiency, an improvement in speed of 8.3%, a rise in throughput of 3.5%, an improvement in Packet Delivery Ratio of 4.5%, and a reduction in jitter of 1.9%. These have been substantial improvements indicating that the proposed model overcomes the limitations of the existing ones and at the same time places a new benchmark in the performance of wireless networks. The impact of this work is immense, providing a scalable and efficient solution for next-generation wireless networks. This integration of bioinspired algorithms in the routing and clustering of the network shall open new avenues for research and development into the field with the potential of more resilient, efficient, and high-performing wireless communication systems. This work, therefore, adds immense impetus to the development of wireless network technologies to uphold higher QoS and better resource management in an era of ever-growing digital communication demands.
Analysis Hybrid Approach for Multi-Objective Optimization-based segmentation of fuzzy contours in mammogram using artificial plant optimization algorithm
Authors: Pramod Baburao Bhalerao
Published: November 2024

It is observed that importance is given to the identification of tumor in case of medical image diagnosis. It has been found that in women breast cancer is increasing and due to which it has an impact in the death rate also. In medical domain radiologist generally uses the computer aided detection methods (CAD ). Medical image often contains the noise and some fuzziness in the picture border so the identification of the tumor is having problem and it becomes challenging task. by Owing to the noise and fuzziness of the picture border, identifying tumors on mammograms is challenging. Even though Active contour is an effective method for the segmentation but It is having problem related to local minima. The proposed method is a hybrid which is consist of multi objective optimization and Gravitational search algorithm (GSA) used for segmentation after that results are given to an Artificial plant optimization algorithm (APOA). The operations involve in this process includes some subprocesses primarily contour initialization is done for ROI followed by segmentation through GSA and multi-objective optimization and then APOA is applied. To evaluate the results two different databases are used mini-MIAS and DDSM databases. Results are also compared with other research work carried out earlier. It is observed that our proposed method gives better output then other.
The Alpha Star-Perfect Mapping in Topological Spaces
Authors: Abu Firas  Al –Musawi,
Published: November 2024

Abstract: In this paper, we introduce new types of continuity mappings by using open sets in topological spaces which is called perfect mapping ( ), and also study some properties of these types. Some definitions are given. This research shows that if  is  ( ). Then the restriction of  on the clopen set is also ( ). Next, for any mapping  between two topological spaces with   are disjoint clopen sets, where . This work shows that  and  are ( ) if and only if is ( ). Finally, we prove that if  and  are mappings with   is ( ), then each one of and  is ( ).
Facebook Addiction: Advanced Vogel's Approximation Method of Unbalanced Fuzzy Transportation Problems.
Authors: M.Anusiya
Published: November 2024

In this article, we discuss the Advanced Vogel's Approximation Method (AVAM), a revolutionary solution for an imbalanced fuzzy transportation problem that avoids making it balanced. Notably, Facebook is the social network that is most popular and well-known globally. Some have characterized it as an invaluable resource for leisure and cross-cultural communication. Conversely, excessive use and a lack of self-control lead to Facebook addiction, which negatively affects the daily lives of many users, mostly young people. Facebook use appears to be connected to the drive to fit in, affiliate with people, and present oneself. Excessive Facebook use and addiction may also be linked to reward and pleasure systems.
Advancements in Machine Learning Algorithms for Predictive Analytics in Healthcare
Authors: Pranoti Prashant Mane
Published: November 2024

Recent improvements in machine learning (ML) algorithms have changed predictive analytics in healthcare in a way that has never been seen before. This means that there are now more ways than ever to improve patient results and business efficiency. This summary looks at important changes and what they mean. A lot of healthcare data, like electronic health records (EHRs), medical images, genetics, and personal sensor data, is being used more and more with machine learning methods like guided learning, unsupervised learning, and deep learning. These methods make it possible to use predictive models to diagnose diseases, give patients specific treatment suggestions, and keep track of their care. For sorting things into groups, supervised learning techniques like support vector machines (SVM) and random forests have been used to correctly spot diseases based on complicated data trends. For example, SVMs have been useful for telling the difference between different types of cancer from genetic data, which helps with focused treatments. On the other hand, unsupervised learning algorithms like grouping algorithms help find groups of patients who share similar traits, which makes personalized medicine possible. Deep learning, has been very successful in medical picture analysis, being more accurate than humans at tasks like finding tumors in x-rays and lab slides. Its ability to instantly learn traits from raw data has made diagnosis easier and more accurate. ML algorithms also help healthcare operations run more smoothly by using prediction analytics to help hospitals handle their resources better, make the best use of their staff, predict which patients will need to be admitted, and lower the number of times they have to be readmitted. These predictive models use a variety of data sources to guess how patients will do and how they will use healthcare resources, which helps people make smart decisions and make the best use of their resources.
Artificial Intelligence Techniques in Automated Vehicle Systems: A Comprehensive Review
Authors: M. A. Sayyad
Published: November 2024

Artificial intelligence (AI) has changed the way automatic car systems work, making them safer, more efficient, and able to drive themselves. This in-depth review looks at the many different AI methods that are used to create and improve automatic car systems. The main focus of this study is on machine learning methods, such as supervised, unsupervised, and reinforcement learning, and how they can be used to help vehicles understand, make decisions, and be controlled. Supervised learning, especially deep learning, is a key part of finding and classifying objects, which is needed to tell the difference between people, cars, and road signs. Many people use Convolutional Neural Networks (CNNs) because they are very good at handling images and videos accurately, which makes real-time responses easier in changing driving situations. Unsupervised learning methods, like grouping and anomaly detection, make systems more reliable by finding strange trends and behaviors. This makes it easier to know what's going on and plan for future maintenance. Vehicles can learn from interacting with their surroundings thanks to reinforcement learning, which is a key part of improving decision-making. This method is very important for planning routes, adaptive speed control, and avoiding collisions, which makes sure that self-driving cars can handle complicated situations safely and quickly. Also talked about are sensor fusion methods that combine data from LiDAR, radar, and video to give a more complete picture of the surroundings and provide extra information. The review also talks about the moral issues and legal problems that come up with AI-driven self-driving cars. To get a full picture of the field, things like computer openness, data protection, and the moral effects of making choices in tough scenarios are looked at. This review highlights the changing potential of AI in automated car systems by putting together the latest research and developments. It also gives us a look into the directions and improvements that will come next. The goal of this study is to be a useful resource for students, practitioners, and lawmakers who are interested in how autonomous driving technologies are changing over time.
The Impact of Quantum Computing on Cryptographic Security Protocols
Authors: Swati Dixit
Published: November 2024

Quantum computing is a big change in the way computers work, and it promises to be much faster than traditional systems. This new technology brings both huge benefits and huge problems, especially when it comes to cryptographic security measures. Classical encryption algorithms, like RSA and ECC, depend on the fact that some math problems are hard, like discrete logarithms and integer factorization. Quantum algorithms, like Shor's algorithm, can solve these problems quickly. Because of this, the development of scalable quantum computers poses a danger to the basic safety of the cryptography methods that are widely used today. This short summary looks at the big effects that quantum computing will have on the safety of cryptography. It looks at the security holes that quantum algorithms create and stresses how important it is to find answers for post-quantum cryptography (PQC). PQC wants to make programs that can't be broken by quantum attacks. This will make sure that digital interactions can still be private, secure, and real in a world powered by quantum computers. Also, switching to PQC comes with a lot of problems, such as implementing algorithms, making sure they are all the same, and getting people to use them in a lot of different technology environments. The abstract talks about current research projects and foreign partnerships that aim to standardize and implement PQC. It stresses how important it is to plan ahead to reduce the risks of the future.
Augmented Reality and Its Integration into Responsive Web Design
Authors: Rameshwar Dadarao Chintamani
Published: November 2024

Augmented reality (AR) is quickly changing the way people interact with digital content by adding virtual features to real-world settings. This essay looks at how AR can be added to Responsive Web Design (RWD) to improve user involvement and interaction on a wide range of devices without any problems. Making systems that work well on screens of different sizes and orientations is what responsive web design is all about. This flexibility makes sure that watching and interacting are best on computers, tablets, and smartphones. Adding AR to RWD makes this flexibility even greater by adding engaging virtual material that changes based on the user's surroundings. Device support, network delay, and image speed are some of the biggest technical problems that come up when AR is used together. Using HTML5, CSS3 media queries, and JavaScript tools to make AR experiences that work well on all devices is one way to deal with these problems. By mixing AR features with RWD ideas, developers can make experiences that are engaging and can be accessed from any device without slowing them down or making them harder to use. In AR-enhanced RWD, user experience (UX) is all about making exchanges easy and quick. Spatial tracking for accurate virtual object placement, motion recognition for natural interaction, and device position detection for smooth merging with the real world are all things that designers have to think about when they're making something. These parts work together to make an interesting user journey that mixes digital and real-world experiences. Case studies show how AR can be used in real-world situations in fields like education, entertainment, and shopping. Virtual try-on experiences in e-commerce, engaging learning material on top of textbooks, and rich stories in digital media are all examples.
Lightweight Cryptography using Pairwise key generation and malicious node detection in large UAV
Authors: Reshma C Sonawane
Published: November 2024

Data security is very essential concern in network data transmission between various sensor nodes. Data encryption is the only way to provide security to sensitive information during the transmission. Various researchers have developed the different encryption techniques with heterogeneous encryption and decryption keys. Nevertheless, the standard does not include guidance for the administration, storage, or distribution of encryption keys. Various researchers proposed key management techniques, but those methods still generate high overhead during data communication. To eliminate such problems, we suggest lightweight key encryption using pairwise cryptography and malicious node detection from large UAVs. The proposed encryption algorithm compresses the cypher data, reducing the network overhead, and detecting malicious nodes provides higher security from internal and external intruders. In extensive experimental analysis, our simulation obtains better results than existing systems. That enhances around 7-9% throughput with other QoS parameters.
Plant Growth Analysis using IoT and Reinforcement Learning Techniques for Controlled Environment
Authors: Suwarna J
Published: November 2024

The Internet of Thing (IoT) plays a major role for improving the green house plant productivity and generates essential information to yields. Various existing systems introduces a smart approach that utilizes the IoT concept to remotely deliver users with data regarding temperature, humidity, and soil moisture severity. The purpose of this system is to track and evaluate plant circumstances. This paper descries a plant growth analysis using IoT and statical machine learning techniques for broccoli plants. Initially data has collected using IoT modules including various potential parameters such as soil temperature, soil moisture, humidity and plant height on daily interval basis. The experimental analysis has done in controlled environment for 30 days daily records. Finally, system recommends possible decides need to take for improve the yield or productivity.
Indian Sign Language Recognition: Support Vector Machine Approach
Authors: Pradnya D. Bormane
Published: November 2024

Indian Sign Language (ISL) is the primary form of communication for the dumb and deaf community in India. Recognizing Indian Sign Language plays an imperative part in promoting communication rights, social inclusion and equality for deaf people, while also contributing to technological advancement and cultural diversity. System’s ability to automatically recognize ISL signs could significantly improve community interactions between deaf and people with hearing loss. The objective of this research is to design a system that can accurately recognize and interpret Indian Sign language (ISL), thereby improving communication accessibility for the deaf and dumb community. Also, enhance the accuracy of Indian Sign language (ISL) recognition. In this research, Machine Learning approach for Sign Language (SL) Recognition using Support Vector Machine (SVM) is implemented. The Support Vector Machine (SVM) model was trained using a linear kernel and a regularization parameter (C) set to 0.999 on a dataset of sequences for gesture recognition. After training, the model achieved a test accuracy of 86% on the test data. The development and implementation of gesture recognition system can increase awareness of the communication needs and rights of deaf people.
Optimizing VLSI Design Verification with Histogram-Based Difference Algorithm
Authors: Rajesh Kedarnath Navandar
Published: November 2024

Abstract: This research explores the optimization of Very Large Scale Integration (VLSI) plan confirmation utilizing the Histogram-Based Distinction Calculation (HBDA) and compares its execution with conventional calculations. Through comprehensive tests, HBDA illustrates predominant confirmation speed, precision, and asset utilization compared to ordinary strategies such as Exhaustive Comparison Algorithm (ECA), Cross-Correlation Calculation (CCA), and Energetic Time Twisting Calculation (DTWA). Particularly, HBDA accomplishes a normal confirmation speed of 25 milliseconds, with a precision of 98.5% and asset utilization of 75%. In differentiation, ECA shows slower confirmation speed (150 milliseconds) and higher asset utilization (100%), whereas CCA and DTWA appear halfway in execution in terms of speed, exactness, and asset utilization. The related work survey highlights different inventive strategies in VLSI plan and optimization, counting neuromorphic computing, vitality gathering, and IoT frameworks. By and large, this research contributes to progressing VLSI plan confirmation techniques, advertising a more effective and solid approach to guarantee the usefulness and unwavering quality of cutting-edge electronic frameworks.
Strategies for Effective Resource Allocation in Public Health Management: A Systematic Review
Authors: Sonali Prasad Dharmadhikari
Published: November 2024

Allocating resources well is essential for getting the best results in public health management. This organized review looks at several methods that could help improve how resources are used in this area. By carefully looking at previous research, this paper finds the most important methods, problems, and best practices for better health results and making things run more smoothly when resources are limited. The review emphasizes how important it is to make decisions based on facts, involve stakeholders, use priority methods, come up with new ways to pay for things, and combine technology and data analytics in order to effectively allocate resources. Evidence-based decision-making becomes a basic idea, focusing on how to carefully combine study results and data analysis to help make choices about how to distribute resources. By putting measures with strong proof at the top of the list, lawmakers can make the best use of resources and avoid spending money on programs that don't work or are useless. Engaging stakeholders is seen as important for making sure that methods for allocating resources are open, accountable, and quick. Getting a lot of different people involved lets lawmakers take a look at things from different points of view and prioritize different things. This builds support and makes decisions about allocating resources more relevant and long-lasting. Methods for setting priorities, like burden of disease analysis, cost-effectiveness analysis, and value-based prioritization, make it possible to evaluate treatments and allocate resources based on which ones are most likely to improve health. Putting technology and data analytics together also lets you make decisions based on data, which makes it easier to use resources efficiently and spot new patterns and trends. Using health information systems, geographic information systems, prediction models, and machine learning techniques can help you find the best ways to use resources and make public health measures more effective.
QMWT: Design of an Improved EEG Classification Model via Q-Learning Based Processing of Multispectral Wave Traits
Authors: Pranjali A. More
Published: November 2024

Electroencephalogram (EEG) signals represent various wave patterns that assist in identification of normal & abnormal brain activities. These wave patterns consist of alpha waves, which indicate relaxation phases, beta waves, that indicate normal brain rhythms & can be disturbed due to cortical & other damages, delta waves, which are dominant in infants during sleep, & theta waves, which represent irregular metabolic & hydrocephalus activities in adults. A combination of these waves is capable of representing multiple brain conditions for both adolescents & adults. Various models have been proposed by researchers to analyze these signals, and most of them work on single or bi-domain features, which limits their classification performance. Most of these models are also static, and do not incorporate continuous feedback & incremental processes. Due to which their precision & recall performance is either constant or reduces w.r.t. newer evaluations. To overcome these limitations, this text proposes design of a novel EEG classification model that uses Q-Learning for classification of multispectral feature sets. The model extracts Mel Frequency Cepstral Coefficients (MFCC), and iVector features from raw EEG data, which assists in multispectral representation of these signals. The extracted features are classified via Q-Learning based Recurrent Neural Network (RNN) classifier, that combines Gated Recurrent Unit (GRU), and Long-Short-Term Memory (LSTM) based feature sets. Due to extraction of MFCC, the GRU & LSTM Models are able to identify power spectral variation, cepstral variations, spectrogram patterns, DCT (Discrete Cosine Transform) variations, etc. While, due to iVector, entropy variations are recognized and processed for better accuracy levels. Thus, both LSTM & GRU Models assist in augmenting extracted features, which improves feature variance for better classification performance. Results of these classifications are feedback into the training set via a correlation-based analysis layer, that assists in continuously improving precision & recall performance for different evaluations. Due to incorporation of this layer, the model is capable of improving precision by 8.5%, recall by 8.3%, and accuracy by 4.9% when compared with various state-of-the-art models. It was also observed that this performance incrementally improves w.r.t. number of evaluations, which assists in deploying the model for real-time applications.
Optimizing Cloud Load Balancing: A Nature-Inspired Approach for Efficient Task Scheduling and Resource Optimization in Scalable Cloud Computing Environments
Authors: Mohammad Imran Khan
Published: November 2024

Cloud load balancing is a key part of making sure that tasks are scheduled and resources are used in the best way possible in scalable cloud computing settings. This study suggests a way to improve load balancing that is based on nature and uses methods as Particle Swarm Optimization (PSO) and NIVM PSO Optimized (NIVM-PSO). By acting like natural processes, these programs can adapt to changing workloads and make sure that tasks are spread out evenly across computers. The goal of the suggested model is to cut down on reaction time, make the best use of resources, and boost system performance as a whole. The results of the experiments show that when compared to standard load balancing methods, they are much better at spreading out the load, lowering delay, and increasing speed. This nature-inspired method is a strong way to handle the complexity and demands of modern cloud systems. It creates a framework that can grow and work well for future cloud computing apps.
Deep Learning for Automated Code Generation: Challenges and Opportunities
Authors: Shrishail S. Patil
Published: November 2024

Deep learning-based mechanized code era has ended up a potential way to speed up program advancement, move forward code quality, and cut down on human botch. Within the setting of programmed code creation, this work looks at the issues and chances that Deep learning strategies bring. A huge issue is that computer dialects are exceptionally complicated and diverse from one another. This makes it difficult to form models that work in all sorts of circumstances where individuals code. Deep learning models require a part of information to work well, but there aren't numerous enormous, high-quality datasets that can be utilized to prepare models. This makes the issue indeed more regrettable. Another big issue is making beyond any doubt that the code that's composed isn't as it were right in terms of language structure, but too makes sense and works well. Code assurance, finding vulnerabilities, and taking after best hones are a few of the other things that make things more troublesome. Indeed in spite of the fact that there are issues, there are moreover a part of great chances in this field. Characteristic dialect preparing (NLP) and transformer plans, like GPT-4, have come a long way and can presently studied and type in code that sounds like it was composed by a individual. When domain-specific data and context-aware models are combined, the created code is much more likely to be rectify and valuable. Robotized code era can offer assistance with rewriting code, fathoming bugs, and composing standard code, which gives journalists more time to work on more complicated assignments. The study too talks around how Deep learning models could be able to memorize from continuous criticism circles, which would let them get way better over time and adjust to modern code measures. When Deep learning is combined with other AI strategies, like fortification learning, it might lead to code era frameworks that are more brilliant and can work on their claim.
Nonlinear Analysis of Problem-Based Learning Models and Social Entrepreneurship Approaches in Enhancing Personal Social Responsibility
Authors: Ixsora Gupita Cinantya
Published: November 2024

This study aims to examine the use of Problem-Based Learning Model and Social Entrepreneurship approach in learning packaging design in Visual Communication Design (VCD) Study Program to increase students' personal social responsibility. Data collection instruments consisted of observation sheets and questionnaires. The test subjects in this development research were 70 students. Data collection methods were interviews, questionnaires, and observations. The trial was carried out through a field trial which was analyzed by means of qualitative and quantitative descriptive analysis. The results showed that the mean or average value of personal social responsibility increased to 30.77 higher than conventional learning which was only 24.57 and post-test control group design testing with a significance value of 0.000 < 0.05, it can be stated that the use of the Problem-Based Learning model and the Social Entrepreneurship approach can increase students' personal social responsibility in the VCD Packaging course. This finding makes an important contribution to the development of the VCD curriculum, especially in integrating problem-based learning and social entrepreneurship to produce graduates who are competent and ready to face the challenges of the growing creative industry.
Enhancing Security in IoT Devices with a Comprehensive Analysis of Lightweight Cryptographic Algorithms
Authors: Latika Rahul Desai
Published: November 2024

A lot of gadgets connected to the Internet of Things (IoT) have changed many fields, from healthcare and smart houses to smart cities and commercial robotics. But this fast growth has also brought about big security problems, mostly because IoT devices don't have a lot of resources, which makes it hard to use standard cryptographic methods. The reason of this ponder is to grant a intensive examination of lightweight cryptographic strategies that can make IoT gadgets more secure without abating them down. Since IoT gadgets have uncommon security needs, lightweight cryptography has ended up one of the foremost critical ways to secure them. These methods are made to supply solid assurance whereas utilizing as few computer assets, power, and memory as conceivable. We see at well-known illustrations from each gather, like Display and Driven (square ciphers), Trivium and Grain (stream ciphers), and PHOTON and SPONGENT (hash capacities), to see how well they work in IoT settings. To donate a full picture of the contrasts, we carefully see at a few speed measures, such as running time, vitality utilize, memory utilization, and security against cryptographic dangers. The appraisal is based on both a common see at the subject and real-world tests on common IoT frameworks. We moreover conversation almost the trade-offs that come with choosing the correct cryptographic strategies to meet security needs whereas moreover taking under consideration the limits of IoT gadgets. Our inquire about appears that lightweight cryptographic calculations have a part of benefits, but the correct calculation must be chosen based on the IoT deployment's one of a kind utilize case and threat demonstrate. For example, applications that require a part of speed might advantage from lightweight stream ciphers, while applications that require to form beyond any doubt the security of the data might select lightweight hash capacities. The think about moreover talks approximately unused thoughts and bearings for lightweight cryptography for IoT, such as utilizing post-quantum cryptographic primitives to create IoT security future-proof. We see into the plausibility of blended strategies that use more than one lightweight procedure to progress security without utilizing as well numerous assets. Within the conclusion, this in-depth ponder appears how vital lightweight cryptographic strategies are for progressing IoT security. This paper aims to help researchers and practitioners set up good security measures for IoT devices by giving a thorough look at how well they work and what kinds of uses they are best for. This will make the IoT environment safer and more reliable.
Analytic and Structural Insights into Eigendecompositions of J2 Binormal Unitary Matrices on the Complex Plane
Authors: K. Jaikumar
Published: November 2024

This research focuses on the eigende compositions of J2 binormal unitary matrices, denoted as J_2^bum (r). These matrices encapsulate matrix components with cohesive logical properties and secondary binormal behavior along a specified unit loop r∈J_2. Addressing gaps in current knowledge, we establish the existence of a decomposition J_2^bum (r)=Un(r)Bi(r)Un(r)^P, meeting conditions such that Un(r)^P=Un(r)^* (conjugate transpose), Bi(r) is a real binormal matrix, and both Un(r) and Bi(r) are analytic functions of a positive integer N. The decomposition extends the well-known Rellich theorem to matrix-valued functions exhibiting J2 binormal unitarity on the real number line. Furthermore, this theorem can be adapted for functions with analyticity and J2 binormal unitary characteristics along lines or circles in the complex plane, and the findings are extended to J2 binormal unitary matrices with elements satisfying a unitary series.
A Search on Integer Solutions to Ternary Non-homogeneous Nonic Diophantine Equation a α(x2+y2)-(2α-1)xy=4αz9
Authors: N.Thiruniraiselvi
Published: November 2024

Different sets of integer solutions to the non-homogeneous Nonic Diophantine equation with three unknowns given by
α(x2+y2)-(2α-1) xy=4αz9  are presented in this paper.
On Strongly Ɣ-Clean Rings
Authors: Zubaida M. Ibraheem
Published: November 2024

For any element z of a ring R is said to be Ɣ-clean if z=a+e, where a is γ-regular and e is an idempotent, further if ae=ea , the element z is called strongly Ɣ-clean. If all the elements of a ring R is Ɣ-clean (resp. strongly Ɣ-clean), then R is called an Ɣ-clean (strongly Ɣ-clean). In this paper, some examples, and properties of Ɣ-clean and strongly Ɣ-clean are introduced and invest gules the behavior of these properties and discuss some relations between Ɣ-clean (resp., strongly Ɣ-clean) rings and other rings.
A Family of Join Spider Graphs on Sum Divisor Cordial Labeling with Application
Authors: T, Christy
Published: November 2024

A graph G with p vertices and q edges is said to be a Sum Divisor Cordial labelling. If ????: ???? → 1,2, … ???? such that for each edge ???????????????? assign the label 1, if 2|(???? ???? + ????(????)| and 0 otherwise, satisfying the condition |e????(1)- e???? 0 | ≤ 1, where e????(1) is the number of edges labelled with 1 and e????(0) is the number of edges labelled with 0. A graph with a sum divisor cordial labeling graph. In this paper we study and we prove that for a class of Join Spider graphs with atmost one additional leg namely JSP(1m,2n), JSP(1m,2n,32), JSP(1m,2n,42), JSP(1m,2n,52).
In this paper we identify an application using Affline Cipher for Cyber Security through encryption and decryption.
On the Study of Structural Equivalency for Animal Conservation in Species-Habitat Network Form
Authors: Ahmad Shulhany
Published: November 2024

Kerinci Seblat National Park is a combination of 17 forest groups located on the island of Sumatra. This study will discuss modeling the relationship between animals and their habitats in the form of an ecological network. The relationship between species and habitat is an essential topic in animal conservation. This relationship can be modeled as a graph called the habitat-species network, with two types of vertices: species (V^s ) or habitat (V^h ). The edge connecting a species vertex and a habitat vertex represents the species ever visiting the habitat. This study aimed to discuss three measures for describing the relationship between species and habitats: structural equivalence, Jaccard similarity, and Pearson’s correlation coefficient. Structural equivalence of a group of habitats represents the similarity of animals that visit the same habitat, the Jaccard similarity describes the percentage of habitats visited by a pair of different animals, and Pearson’s correlation coefficient shows the significance of the relationship between the two species.
Residual Power Series Method for Solving Linear Complex Differential Equations
Authors: Susan H. Mohammad
Published: November 2024

This work uses the residual power series method (RPSM), primarily based on the general Taylor series formula with a residual error function, to offer solutions to instances of linear complex differential equations (LCDEs) as test problems. After finding approximate solutions to those problems, the current approach's findings have been compared with the exact solutions as shown in the tables and figures, which show the suggested method's reliability, precision, and rapid convergence. So, the importance of this research lies in demonstrating the method's ability to find an approximate solution close to the exact solution for complex differential equations.
Seven Years Malaria Trend Analysis in Kori Public Health Centre, Southwest Sumba District East Nusa Tenggara Province, Indonesia: a Retrospective Study
Authors: Maria A. Kleden
Published: November 2024

Introduction: Malaria, caused by the Plasmodium parasite and transmitted by Anopheles mosquitoes, remains a significant global health challenge, particularly in tropical regions. Despite a rise in cases, global malaria-related deaths have declined, indicating the success of control programs. Indonesia, committed to eliminating malaria by 2030, faces challenges with uneven distribution of cases, particularly in Eastern regions, necessitating targeted efforts for elimination.
Objectives: This retrospective study aimed to analyze the trends in malaria prevalence over seven years in the catchment area of Kori Community Health Centre, located in the Sub-district of North Kodi, Southwest Sumba District, East Nusa Tenggara Province, Indonesia.
Methods: Statistical analysis included data scrutiny for completeness, the utilization of SPSS software, and the presentation of retrospective malaria data in graphs and tables
Results: The analysis of demographic data, health statistics, and malaria cases yielded crucial findings and strategic recommendations to support malaria control efforts and healthcare service enhancements in the region.
Conclusions: This study provides significant insights into the prevalence of malaria in the Kori Community Health Centre catchment area over the past seven years. The results of the analysis can serve as a foundation for more effective malaria control efforts and healthcare service improvements in the area. Further research is required to delve deeper into the factors influencing malaria prevalence and to develop more effective strategies for controlling this disease.
Numerical Simulation of Nonlinear Equations by using Modified Newton Raphson Technique
Authors: Inderjeet
Published: November 2024

Non-linear equations are a significant number of complicated issues in mathematics and related subjects that must be solved. The Newton method and its preliminary variations are among the most basic, yet insufficiently effective, methods for resolving non-linear equations. To solve non-linear equations efficiently, a method's desirable feature is to find root with fewer iterations, minimum error (usually less than precision limit), which is typically less than the precision limit. In, this work, we present two approaches (Proposed techniques I and II) for solving non-linear equations by modifying Newton Raphson's technique using first derivative's forward and central difference approximations. Two examples are used to compare performance of the suggested techniques with the existing technique (Secant technique). Finding roots of nonlinear equations under consideration required fewer iterations and a lower absolute relative approximate error while using proposed method II, which performed better than both existing methods (the Secant technique and suggested method I). The suggested approaches I and II were determined to be appropriate substitutes for solving nonlinear equations.
Artificial Intelligence-Powered Chatbots for Waste Management: A Vision for Circular and Smart Cities
Authors: P. S. Ramesh
Published: November 2024

Innovative solutions are needed to address the major issues associated with the efficient management and recycling of garbage in metropolitan environments. To overcome these obstacles, this study abstract investigates the use of artificial intelligence (AI) in trash management. Municipalities and recycling centers can increase garbage collection efficiency, improve sorting accuracy, forecast waste generation patterns, inform the public, identify illicit dumping, and advance a circular economy by utilizing AI-driven solutions. Smart garbage collection is one important area where AI shows its potential. We used the study of these five chatbots to help us construct the processes for gathering data, instruction, developing, and testing a chatbot using different natural language processing (NLP) techniques, which are covered in the second section of this work. We also present some conclusions and comments on all facets of the topic to evaluate if they might be useful to us in future research. Additionally, the successful development of a chatbot devoted to the circular economy will be the aim of our research on this subject in the future.