bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Complex Dynamics of Julia and Mandelbrot Sets for the Complex-valued Mapping sin(zk) + az + c Using Four-Step Iterative Scheme with s-Convexity
Authors: Nabaraj Adhikari
Published: November 2024

In this paper, we investigate unique variations of the Julia and Mandelbrot sets, establishing criteria for the escape of a function Tcz = sin(zk) + az + c for all z ∈ C, where k ∈ N \ {1} and a, c ∈ C with a̸ = 0 by utilizing a four-step iterative scheme extended with s-convexity, we analyze the dynamics of these fractals by formulating and implementing the algorithms. Our exploration focuses on understanding how distinct parameters impact these fractals’ color, dynamics, and overall visual characteristics. Our findings reveal that specific fractal patterns resemble exquisite natural objects like flowers and spiders. These fractal designs are also employed as visually striking artistic motifs on garments and textile materials. The captivating and complex properties of fractal patterns within dynamic systems make them an enticing and visually appealing area of research.
Implementing Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for Risk Assessment of Drug Interactions
Authors: Ashu Nayak
Published: November 2024

Novel drug development is time-consuming, difficult, and often unsuccessful. This has made combining therapies increasingly common and profitable in recent years. Healthcare professionals in the pharmaceutical sector are interested in the combination, but we must instantly solve drug-drug interactions. In few cases, single-perspective DDI evaluations are insufficient. Pharmacological therapy and patient safety depend on accurate drug interaction risk assessments. This work uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) to assess and predict medication interaction hazards. The proposed method uses neural networks' generative skills and fuzzy logic to construct a robust decision-making framework that can handle drug interaction scenarios and their unpredictability. Fuzzy rules provide all the necessary risk assessment components. This includes dose, patient health issues, drug interaction profiles, and pharmacological properties. Accuracy, specificity, and Mean Squared Error evaluate the model after training with known drug interactions. These metrics evaluate model performance. These measures are essential for machine learning evaluation. The ANFIS-based model outperforms previous risk assessment methods in risk classification and prediction. This study found that the ANFIS architecture may improve medication management security and prevent dangerous drug interactions.
Hybrid Blockchain Network for Drug Traceability in Secure Pharmaceutical Supply Chain
Authors: Uruj Jaleel
Published: November 2024

Several stakeholders are involved in the pharmaceutical supply chain (PSC), including those who provide raw materials, make and sell drugs, and oversee the industry, hospitals, pharmacies, and patients. An efficient traceability system is necessary to ascertain the present and all prior ownership of products because of the intricate nature of product and transaction flows in PSC. Furthermore, regulatory supervision and product safety can be significantly improved by digitizing the track and trace procedure. A permanent, trustworthy, responsible, and transparent system in the PSC can be achieved using blockchain-based medication tracing, which provides a distributed shared data platform. A hybrid blockchain network (HBN) based on secure pharmaceutical supply chain architecture (SPSCA) has been proposed for this research. The suggested system relies on smart contracts to record significant events and track how all the parties engaged in the supply chain perform their respective tasks. In this way, everyone concerned can remain informed of developments and protect the supply against manipulations of supply records and counterfeit pharmaceuticals. All parties involved benefit from a secure, immutable record of transactions due to the smart contract, which additionally guarantees the provenance of data and deals with mediators. Detailed methods and the system architecture that enable the solution's functioning are laid out here. We perform testing and validation to assess the system's efficacy in improving pharmaceutical supply chain traceability and provide cost and security evaluations.
High-Order Multimodal Interaction Network for Efficient Prediction of Drug-Drug Interaction
Authors: Rajeev Kumar Bhaskar
Published: November 2024

The pharmaceutical industry places a lot of focus on preventing drug-drug interactions (DDIs). Medication interaction detection has been the primary emphasis of machine learning-based DDI prediction methods. Since studies have shown that DDIs can cause different future occurrences, it is more beneficial to predict DDI connected events while examining the mechanism behind combination medicine consumption or adverse reactions. The areas of medication development and illness diagnostics are seeing increased usage of a developing approach that predicts DDIs-associated occurrences. We examine potential interactions between the two medications as well as the kinds of interactions that can occur in this research. Additionally, we provide a strategy that relies on learning and use High-Order Multimodal Interaction Network (HOMIN) to forecast DDIs by learning feature representations. The purpose of this work is to provide HOMIN-DDI, a new method for drug-drug interaction predictions that we have developed utilizing a HOMIN architecture. First, we develop feature vectors from medication categories, targets, paths, and digestive enzymes, and we use the Jaccard similarity to evaluate how similar drugs appear. After that, we build a new HOMIN using the feature representation to predict DDIs. The results of the experiments show that the drug categories feature type, when used to the HOMIN -DDI approach, is effective. Furthermore, compared to employing only one feature, combining numerous features yields more useful and informative results. Compared to other algorithms, HOMIN-DDI performs better when it comes to predicting DDIs.
Big Data in Pharmacy: Transforming Patient Care with Analytics
Authors: Abhijeet Madhukar Haval
Published: November 2024

The integration of big data analytics in pharmacy is revolutionizing patient care by providing insights that enhance medication management and therapeutic outcomes. By leveraging vast amounts of health data, pharmacists can deliver more personalized treatment and optimize drug therapy. Despite the potential benefits, existing methods in pharmacy often suffer from data silos, lack of interoperability, and insufficient analytical capabilities, leading to suboptimal patient outcomes and inefficient medication management. These limitations hinder the effective utilization of data for real-time decision-making in patient care. To address these challenges, it propose the Patient Care Transformation using Big Data Analytics (PCT-BDA) framework, which facilitates a holistic approach to patient data integration and analysis within smart grid systems. This framework enables seamless data flow between various healthcare entities, improving collaboration and fostering a comprehensive view of patient health profiles. The proposed method emphasizes real-time analytics, predictive modeling, and machine learning algorithms to enhance decision-making processes in medication management. By utilizing big data analytics, pharmacists can better predict patient responses, identify potential drug interactions, and tailor therapy plans to individual needs. Preliminary findings from the implementation of the PCT-BDA framework indicate significant improvements in patient outcomes, including reduced medication errors, enhanced adherence to treatment protocols, and overall increased satisfaction with pharmacy services. This innovative approach highlights the transformative potential of big data analytics in optimizing patient care in the pharmacy sector.
Pharma in The Digital Era: The Role of Artificial Intelligence in Drug Development
Authors: Priya Vij
Published: November 2024

The integration of Artificial Intelligence (AI) in the pharmaceutical sector marks a transformative era, where technology reshapes traditional drug development processes. This paper delves into the pivotal role AI plays in enhancing efficiency and precision in drug discovery, testing, and approval. Existing methods in drug development often suffer from high costs, extended timelines, and high attrition rates in clinical trials, posing significant barriers to timely patient care. To address these issues, we propose the Drug Development based on Artificial Intelligence (DD-AI) framework, leveraging AI-driven algorithms and machine learning models. The DD-AI framework enhances the identification and optimization of drug candidates, predicts clinical trial outcomes, and personalizes patient treatment plans. AI algorithms analyze vast datasets to discover potential drug molecules, simulate their interactions, and streamline the clinical trial process by identifying the most promising candidates. Implementing the DD-AI framework has demonstrated substantial improvements in reducing drug development costs and timelines while increasing the accuracy of trial outcomes. AI's predictive capabilities also personalize treatments, optimizing efficacy and minimizing adverse effects for patients. The DD-AI framework offers a robust solution to existing challenges in drug development, fostering a more efficient, cost-effective, and patient-centered approach. The findings underscore AI's transformative potential in revolutionizing pharmaceutical practices, ultimately benefiting patients with faster and more precise medical solutions.
Investigations of Acute Ischemic Stroke Detection Techniques from CT Images Using Machine Learning Approaches
Authors: Divya
Published: November 2024

Stroke is a kind of cerebrovascular accident (CVA) that primarily affects individuals over the age of 50, while it can afflict people of all ages. Stroke patients suffer from a chronic condition that leaves them physically unable till their death. To identify the onset of this illness, numerous studies have been carried out. Common signs of a stroke include stiffness, a shift in posture, and tremors in the arms and legs. Functional and emotional mediation are linked to a significant area of the brain. The symptoms of many brain disorders are similar in the early stages because of disruptions to dopamine and other regulating mechanisms. This is a crucial step in how stroke lesions develop. The CAD system is the most efficient method for identifying a stroke. By speeding up the analysis of the required aberration, the CAD system improves the process of illness diagnosis. The goal of the proposed study is to create a compact system that can lessen processing errors, false positive rates, and complexity—three major problems with the current system. The proposed study uses a machine learning model to interpret and classify a CT brain image that shows an Acute Ischaemic Stroke (AIS) lesion. To distinguish between normal and AIS stroke lesions, the pre-processed image is segmented and classed using a Random Forest classifier. Performance criteria like Peak signal ratio, average gradient, accuracy, specificity, sensitivity, and dice index are used to evaluate the experimental output of the proposed work. The suggested model has a straightforward processing structure and the highest efficiency.
Yara-Based Emotet Malware Scanner – A Factual Analysis
Authors: Priya P Sajan
Published: November 2024

Malware is malicious software which has the ability to compromise sensitive data and disrupt systems. At present malicious software is the most efficient tool used in compromising the security of computers or any other electronic devices connected to the internet. This has become a menace owing to the rapid progress in technologies such as encryption and data hiding techniques. A highly adaptable and persistent type of malware, Emotet is well-known for its capacity to propagate via phishing emails and send out further malicious payloads, by impairing network and data security. Emotet poses a special risk since it serves as a gateway for other viruses, such as ransomware and data thieves, which can cause serious data breaches, monetary losses, and interruptions to business operations. It poses a serious danger to both individual users and big enterprises due to its advanced evasion techniques and quick network spread. In this project, distinct file signatures and byte patterns that are distinctive of the malware are identified by YARA rules in order to detect Emotet. These patterns include specific sequences used in Emotet’s payload and obfuscation techniques, allowing for precise detection within scanned files.
Machine Learning and Computer Vision Driving Precision in Robotic Surgery
Authors: Ipseeta Nanda
Published: November 2024

The utilization of Artificial Intelligence (AI) in robotic surgery represents a significant advancement in healthcare, improving precision, effectiveness, and overall patient outcomes. The research studies the recent improvements in AI-powered robotic surgery, fastening on how artificial intelligence algorithms such as machine learning, computer vision, and natural language processing are transforming surgical processes. AI improves the functioning of robotic systems by employing data-driven algorithms, enabling independent or semi-autonomous tasks, and furnishing surgeons with exceptional delicacy through real-time analytics. AI-supported minimally invasive procedures and stoked reality interfaces have helped to drop complications, rehabilitation times, and surgical failures.  The exploration explores the impact of AI on surgical training and simulation, in which AI- AI-grounded virtual surroundings give surgeons realistic practice scripts before performing live operations.
Predictive Analysis System for National Ranking and Accreditation of HEIs
Authors: Amrita Sekhon
Published: November 2024

Predictive analytical models have been applied in context of the Indian higher educational institutions dataset. This study is aimed to compare the predictive accuracy level of various machine learning algorithms (multi-class classification prediction algorithms) to assess the grading system applicable to a college based on various parameters outlined by NAAC (National Assessment and Accreditation Council). NAAC is an apex body to assess and accredit HEIs of India. An ensemble classification method is proposed to best predict the academic grading of university-affiliated colleges of India for the NAAC accreditation system. This maiden attempt for using PA (Predictive Analysis) on such institutional credential data would be beneficial to colleges applying for NAAC accreditation to judge their grade prior to final assessment. Thus they would be able to identify their strengths and weaknesses and eventually work towards further improving upon their grade. Such a system would pave the way for designing such predictive systems to assess the institutional rankings for several other assessment systems.
Hybrid Machine Learning Approach for Early Stroke Prediction in Elderly People
Authors: P.Divya
Published: November 2024

Stroke represents a significant global health challenge, often leading to severe disability and mortality. The timely prediction and intervention of stroke are paramount in enhancing patient outcomes and reducing healthcare burdens. This project proposes a machine learning-based approach to predict stroke risk using multi-modal biosignals, specifically electrocardiogram (ECG) data. By leveraging advanced algorithms, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, the system aims to classify patient health data into critical risk categories such as Normal, Abnormal, Ischemic, and Hemorrhagic. The study utilizes a comprehensive dataset consisting of ECG signals and incorporates techniques for data preprocessing, class balancing, and feature extraction. The predictive model is trained and validated using robust evaluation metrics, including accuracy, precision, recall, and F1-score. The findings underscore the efficacy of the proposed system in providing real-time stroke risk assessments, offering a cost-effective alternative to traditional diagnostic methods. Furthermore, this research explores the integration of wearable technology with machine learning, highlighting its potential for continuous patient monitoring and early detection of stroke symptoms. By creating a user-friendly interface for healthcare professionals and patients, the system aims to facilitate prompt decision-making and intervention, ultimately improving the overall quality of care for individuals at risk of stroke
FEA of Bajaj Discover 100 cc Connecting Rod for Aluminum Material
Authors: Prathamesh S. Gorane
Published: November 2024

The connecting rod is a vital component of internal combustion engines, responsible for converting the piston's linear motion into rotational motion. Its design involves careful consideration of material selection, geometry, and load distribution to ensure it can withstand the dynamic forces and stresses encountered during engine operation. Finite element analysis plays a crucial role in evaluating the structural performance of the connecting rod, allowing engineers to optimize its design for maximum efficiency and reliability. By understanding the complexities of connecting rod design and the forces acting upon it, engineers can develop robust and high-performing engines that meet the demands of modern automotive applications.
The paper describes a case study that was crucial to the engine development program's evaluation of the connecting rod's strength and fatigue analysis for Bajaj Discover 100 cc engine. The basic model was prepared using appropriate modeling software and then it was analyzed in Ansys for strength and fatigue analysis. The compressive and tensile loading was applied on the both ends of the connecting rod and the results were found. The location of the maximum stress was found.
Finite Element Analysis (FEA) of Swift’s Connecting Rod Considering ‘I’ Cross Section
Authors: Prathamesh S. Gorane
Published: November 2024

The analysis of a connecting rod with an ‘I’ cross-section is a multifaceted process that requires careful consideration of material properties, stress and strain behavior, and potential failure modes. The ‘I’ cross-section offers a robust design that balances strength and weight, making it ideal for high-performance engines. Through the application of advanced techniques like FEA, engineers can optimize the design to ensure the reliability and efficiency of the connecting rod, ultimately enhancing the overall performance of the engine.
In this paper model of the Swift connecting rod was being created in modelling software, the model was imported into ANSYS Workbench and saved in IGES format. By using multiple ANSYS Workbench modules and appropriate boundary conditions, the model was examined for different types of stresses. An analysis is conducted on the Von Misses stresses, shear stresses, total deformation, and several fatigue characteristics such as life, damage, safety factor, etc.
Information-Theoretic Security in Wireless Sensor Networks: Mathematical Models for Secure and Energy-Efficient Communication
Authors: Kanchan Rahul Jamnik
Published: November 2024

Wireless Sensor Networks (WSNs) are used for a wide range of things these days, from watching the environment to improving healthcare systems. But because these networks are used everywhere, they are also open to security risks like spying, data corruption, and node capture. It is very important to address these issues in order to protect the accuracy, privacy, and access of data being sent. Information-theoretic security looks like a good way to deal with these problems because it uses mathematical models to make sure that transmission in WSNs is safe and uses little energy. This essay goes into detail about information-theoretic security in WSNs. It gives a full rundown of the mathematical basics and real-world implications for getting strong security in places with limited resources. The idea of "secrecy capacity" is at the heart of this method. It measures the highest rate at which authorized nodes can communicate privately while preventing spying foes. Utilizing the chance that exists in wireless channels, encrypted keys can be generated without using shared secrets, making them resistant to attempts that try to steal them. Because sensor nodes only have a short power life, energy economy is very important in WSNs. Information-theoretic security methods, like physical layer security and joint communication, are designed to use as little energy as possible while still providing strong security. Nodes can work together to improve signal strength and fight channel fading using cooperative methods. This makes the network last longer without compromising security. This article also talks about the trade-offs between security, energy savings, and communication performance in WSNs. This helps us figure out how to make the best transfer schemes.
Mathematical Models for Cyber-Physical System Security: Securing Critical Infrastructure Through Control-Theoretic Approaches
Authors: Sonam Rani
Published: November 2024

Cyber-Physical Systems (CPS) is now an important part of modern vital infrastructure. They combine physical and digital parts to make things more efficient and automated. But as they get more connected and complicated, they become more vulnerable to different online risks, which make security much harder. In this situation, control-theoretic methods look like they could help protect CPS by using ideas from control theory to create strong and flexible defenses. The main focus of this work is on control-theoretic methods to mathematical models for CPS security. We talk about the special problems that come up with CPS security, like making sure that the system is reliable, private, honest, and accessible even when working conditions change and online risks change. We talk about how control theory can help solve these problems by giving us a way to model, analyze, and lower the security risks in CPS. First, we look at how feedback control theory can be used to improve CPS security. In this case, control methods are used to change system settings and reactions on the fly to protect against cyber attacks and keep the system working. We look at several types of control-based defenses, such as breach detection and reaction systems, anomaly detection, and adaptable access control, to show how they can improve CPS's security while keeping vital processes running as smoothly as possible. Next, we talk about how to use cryptography and control theory together to make sure that communication is safe and data is kept safe in CPS. By using cryptographic primitives in control algorithms, CPS can make sure that components can safely share information with each other and protect against threats like listening in, changing data, and playing back information. We look at the problems that uncertainty and system dynamics can cause in CPS security models and suggest probabilistic and robust control methods that can effectively deal with uncertain settings and bad behavior. Lastly, we show case studies and real-world uses of control-theoretic security methods in key infrastructure areas like healthcare, transportation, and energy. This shows how well they work in the real world. This paper talks about how control theory helps keep key infrastructure systems safe and points out areas where more study is needed to make these systems even safer and more reliable.
Mathematical Modeling and Designing of a Novel Improvised Cooperative Balancing Routing (CBR) Protocol to Enhance the Lifetime of WSNS using Load Balancing Concepts
Authors: Swagat M Karve
Published: November 2024

In this paper, the design & development of a novel cooperative balancing routing (CBR) protocol to enhance the lifetime of WSNs using load balancing concepts by decreasing the energy consumption during the data exchanges is presented in the respective section. The main objective of the research work presented in this paper is to present an improvised version of the CBR that computes the routing path by using cooperative mechanisms using the concept of load balancing. This paper also demonstrates the various results obtained for all the test cases along with the necessary observations and explanations in the form of discussions and diagrammatic representations. The paper finally concludes with the overall conclusions of the CBR work.
Implementation of Industry 4.0 and Digital Manufacturing Practices in Indian Manufacturing Industries Using Discrete Event Simulation (DES)
Authors: Y. Y. More
Published: November 2024

Industry 4.0 and digital manufacturing have emerged as significant areas of research in recent years. According to several reputed research papers, Industry 4.0 is a new industrial revolution that involves the integration of cutting-edge technologies such as the Simulation (Discrete Event Simulation), Internet of Things (IoT), artificial intelligence (AI), and big data analytics into the manufacturing process. Smart manufacturing, on the other hand, is the implementation of these technologies in the manufacturing sector to increase efficiency, reduce waste, and improve product quality. The research suggests that Industry 4.0 and smart manufacturing have the potential to revolutionize the manufacturing industry by enabling companies to achieve higher levels of automation, productivity, and agility. The Indian manufacturing industry has witnessed substantial growth in recent years, becoming a significant contributor to the country's economic development. To maintain its competitive edge in the global market and meet the increasing demand for innovative and cost-effective products, the industry has been actively adopting digital manufacturing practices. In this context, Industry 4.0, characterized by the integration of cyber-physical systems, Simulation, the Internet of Things, and data analytics, offers new opportunities for enhancing efficiency and productivity. This research paper presents a comprehensive study on the successful implementation of digital manufacturing practices in the Indian automobile industry through the use of simulation approaches, in alignment with Industry 4.0 principles. The study aims to identify the key challenges faced by the industry and proposes viable solutions to harness the full potential of digitalization. The paper delves into the critical aspects of simulation techniques, such as discrete event simulation and virtual manufacturing, and their role in optimizing manufacturing processes, reducing lead times, and enhancing product quality. The results of this study indicate that the integration of digital manufacturing practices with Industry 4.0 principles has resulted in significant improvements across the entire value chain of the Indian automobile industry. Reduced production costs, increased production flexibility, and enhanced product quality have been observed as the key outcomes of this strategic alignment.
Analysis of a Hybrid Model of Automated Vehicle Safety System Consists of Rain Sensor-Controlled Wiper and Ultrasonic Sensor-Based Auto-Braking System
Authors: Sumit Kumar Rai
Published: November 2024

This paper presents an analysis of a novel hybrid model integrating two crucial safety features in modern vehicles: a Rain Sensor-Controlled Wiper (RSCW) and an Ultrasonic Sensor-Based Auto-Braking System (US-ABS) using Arduino UNO micro-controller. The integration of these systems aims to enhance vehicle safety by addressing two critical aspects: visibility during adverse weather conditions and collision avoidance through automated braking mechanisms. The Rain Sensor-Controlled Wiper (RSCW) component utilizes advanced sensor technology to detect precipitation on the vehicle's windshield accurately. Upon detection, the system automatically activates the wiper mechanism, tuning the wiper speed and frequency according to the amount of rainfall. By ensuring optimal visibility, the RSCW system significantly reduces the possibility of mishaps brought on by vision impairment due to rain, snow, or other weather phenomena. Complementing the RSCW, the Ultrasonic Sensor-Based Auto-Braking System (US-ABS) serves as a proactive safety measure to prevent collisions. Employing ultrasonic sensors strategically positioned around the vehicle, the US-ABS continuously monitors the surrounding environment for obstacles, vehicles, or pedestrians. When an imminent collision risk is detected, the system triggers automatic braking interventions to mitigate or entirely prevent potential accidents. In this analysis, we evaluate the effectiveness of the hybrid model by considering various factors, including accuracy, response time, and real-world performance. We have explored potential synergies between the RSCW and US-ABS components, assessing how their integration can further improve overall safety and reliability.
"Revolutionizing Agriculture: Mathematical Modelling for Plant Disease Diagnosis via Image Processing and CNNs"
Authors: Ajay Visave
Published: November 2024

Plant leaf diseases pose significant challenges to agricultural productivity, necessitating early detection and intervention strategies. Recent research has increasingly turned to artificial intelligence (AI) techniques, particularly image processing and deep learning, to address these challenges. The paper provides a technical view of the application of Artificial Intelligence in plant leaf disease detection. Key aspects covered include datasets, image processing methodologies, deep learning algorithms, and evaluation metrics commonly utilized in this domain. An emphasis is placed on the important role of annotated datasets in the training of AI models, facilitating the development of accurate classification systems. Image processing techniques, encompassing tasks such as enhancement, segmentation, and feature extraction, are discussed for their role in preprocessing leaf images to extract salient features. Deep learning algorithms, notably convolutional neural networks (CNNs), are highlighted for their effectiveness in automatically learning discriminative features from input images and achieving high accuracy rates in disease classification tasks. The paper illuminates the significance of employing evaluation metrics such as accuracy, precision, recall, and F1 score as quantitative benchmarks to gauge the efficacy of model performance. It highlights the triumphs observed across diverse studies harnessing AI methodologies for the detection of plant leaf diseases, emphasizing their transformative capacity in reshaping agricultural management paradigms. Future research directions are outlined, including the exploration of transfer learning methods to capitalize on pre-trained models and the integration of additional sensing technologies like hyperspectral imaging and drones for enhanced disease detection capabilities. These advancements are poised to further refine the accuracy and efficiency of plant leaf disease detection systems, thereby offering substantial benefits to agriculture and food production sectors.
"Smart Farming in the Indian Context: Fertilizer Prediction through Ensemble Machine Learning Based Soil Nutrient Analysis"
Authors: Bhagwan Dinkar Thorat
Published: November 2024

The agricultural landscape in India is undergoing a transformative shift with the integration of advanced technologies to enhance productivity and sustainability. Recognizing the critical role of soil health in agricultural outcomes, this research leverages advanced algorithms to analyze and interpret soil nutrient data, providing farmers with accurate and timely recommendations for optimal fertilizer application. The methodology involves the collection of comprehensive soil nutrient information from various regions, utilizing state-of-the-art sensing technologies. Relationships between soil nutrient levels and crop performance are established through the applying ML models, which include regression & classification methods.  The purpose of this study is to create a fertilizer prediction model capable of anticipating fertilizer requirements based on specific soil characteristics, crop types, and regional variations. The anticipated benefits of this research include improved resource utilization, enhanced crop yields, and reduced environmental impact through the targeted application of fertilizers. By providing farmers with precise recommendations tailored to their specific soil conditions, this approach seeks to contribute to sustainable agricultural practices, economic efficiency, and overall food security in the Indian context. This paper underscores the potential of machine learning applications in revolutionizing traditional farming practices by introducing data-driven decision-making processes.
Genetic Algorithms and Swarm Intelligence in Computational Mathematics for Predictive Modelling
Authors: Vaishali R Surjuse
Published: November 2024

The integration of genetic algorithms and swarm intelligence into computational mathematics has significantly advanced the field of predictive modeling. Genetic algorithms use the ideas behind natural selection and genetics to find the best answers to difficult problems over and over again. These algorithms are great at working with big, multidimensional search areas. This makes them perfect for many predictive modeling tasks, such as genomics, financial predictions, and climate modeling. The idea behind swarm intelligence comes from the way social animals like ants, bees, and birds act as a group. It uses autonomous, self-organized systems to improve predictive models. Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are two examples of techniques that use simple agents that follow basic rules to solve difficult optimization problems. In order to improve and confirm models, these programs often use computational mathematics methods like linear algebra, statistical analysis, and differential equations. When you combine genetic algorithms, swarm intelligence, and these mathematical methods, you get a strong framework for dealing with the computational problems that come up in predictive modeling. This combined method makes it easier to explore and use the search area effectively, raises the rate of convergence, and improves the quality of the solutions. This combination can be used in many areas, such as engineering to improve design parameters, economics to guess market trends, and healthcare to make diagnoses more accurate. In addition, the fact that these methods can be changed to include domain-specific knowledge adds to their usefulness and flexibility. Genetic algorithms and swarm intelligence are continuing to improve with the help of more complex mathematical and computational methods. This will lead to even better tools for making predictions, which will encourage new ideas in many scientific and economic fields.
Comprehensive Survey of Computer-Based Programs Impact on Adolescent Mental Health: Focusing on Depression and Anxiety
Authors: Devata Anekar
Published: November 2024

With computer-based programs, teen mental health, especially when it comes to sadness and worry, has become a major public health issue. This thorough study looks at the effects of these kinds of programs on teens, trying to figure out how well they work and what problems they might cause. In the first part, we look at previous research that has been done on how common sadness and anxiety are among teens. We also talk about how digital tools are becoming more important for mental health treatments. Key methods used to evaluate these programs, like randomized controlled trials and continuous studies, are looked at to get a full picture of how well they work. The next part talks about the different kinds of computer-based treatments that are out there, such as cognitive-behavioral therapy (CBT) apps, awareness programs, and virtual reality treatments. The exact ways each type of strategy works and the ideas behind them are talked about in terms of how they affect symptom decrease and mental health in general. The third part gives important information about the problems and restrictions of computer-based solutions. Things like obedience rates, privacy issues, and the digital gap are looked at to show what might get in the way of broad use and success. Finally, suggestions are made for future study and clinical practice, with a focus on how important it is to have strict evaluation methods and specific management strategies that are matched to the growth stages and cultural backgrounds of teens. As more people talk about how to use digital tools in mental health care, this poll adds to the conversation. It supports methods that are based on evidence and put both effectiveness and ethics first.
Optimizing Power Quality in Distribution Systems: A Three-Phase Three-Level Shunt Hybrid Active Filter with Multilevel Inverter Approach
Authors: Pranita Chavan
Published: November 2024

This paper presents an advanced solution for improving power quality in distribution systems through the implementation of a Three-Phase Three-Level Shunt Hybrid Active Filter (SHAF) with a Multilevel Inverter. The SHAF, utilizing a Three-Level Neutral Point Clamped Voltage Source Inverter actively mitigates harmonics, with a comprehensive performance analysis considering the impact of active and passive parameters on power quality improvement. The proposed system incorporates TPTL (Three phase 3-Level) NPC inverter technology to reduce switch ratings and enhance harmonic compensation. Various passive filters are integrated into the SHAF to assess performance. Simulation results, validated with MATLAB/Simulink, confirm the efficacy of the proposed system. Comparative analysis against conventional shunt passive filters, including Single Tuned, Double Tuned, High Pass, C-type High Pass, and a two-level shunt active filter with diverse passive filters, reveals a significant improvement of over 53% compared to two-level active filters and more than 16 times compared to shunt passive filters.
Industrial Product Surface Defect Detection Using CNN: A Deep Learning Approach
Authors: Deepali Ujalambkar
Published: November 2024

Defect identification on surfaces of industrial products is a thought-provoking issue that has garnered significant attention. Defect identification of industrial products plays a primordial role in ensuring quality and reliability of products at all levels of the manufacturing process. The traditional approach relies fundamentally on the human inspection, which was unreliable and inefficient. Moreover, human inspection cannot meet the high standards for real-time detection required in industrial production situations. While methods used in image processing can solve difficult modules, they are not meant to deal with complicated ambient textures, noise, or lighting fluctuations. Innovative techniques have so been used by researchers and practitioners to improve and expedite the defect diagnosis process. This study classifies the product as "defect or not defect." and provides an intelligent approach for surface defect identification using convolutional neural network (CNN). The model demonstrates strong performance in identifying many types of industrial product defects, such as "crack, patch, inclusion, rolled, pitting, and scratching," by utilizing deep learning approaches.  This involved training the model with several datasets featuring surface textures. The results were indicative of better performance, where the proposed method achieved outstanding precision in fault detection.
A Comparative Analysis of Violence Detection in Surveillance Video using TLD, GOTURN, YOLOv5, MDNet, KCF, DeepSort, and FairMOT
Authors: Vaishali M Bagade
Published: November 2024

The increasing deployment of surveillance systems has necessitated the development of robust algorithms for automatic violence detection. This paper presents a comparative study of various object detection and tracking algorithms—TLD, GOTURN, YOLOv5, MDNet, KCF, DeepSort, and FairMOT—for detecting violent activities in surveillance video footage. The evaluation focuses on accuracy, speed, and robustness under different environmental conditions. Our experiments demonstrate that while YOLOv5 combined with FairMOT provides superior accuracy, other algorithms offer competitive results in terms of processing speed and robustness, suggesting that the choice of algorithm should be based on specific application requirements.
A Statistical Analysis of Knowledge Graph and its Applications
Authors: Reshma G. Nehe
Published: November 2024

In today's fast-paced world driven by big data and artificial intelligence, organizing and representing vast amounts of knowledge is crucial. Knowledge graphs are dynamic structures serve as repositories for real-world knowledge, presented in the form of interconnected graph data. With their ability to encapsulate complex information, knowledge graphs have swiftly captured the attention of both academia and industry alike. In this paper, we dive deep into understanding knowledge graphs. In first Section, we start by looking at their origins and evolution. In second section describes knowledge graphs, compares them with other graph forms, and explores how academic societies have incorporated them. In conclusion, we discuss the various applications of knowledge graphs in various domains and highlight the software tools that are facilitating their advancement. By providing a systematic overview of knowledge graphs, we aim to illuminate new avenues for research and foster advancements in this burgeoning field.
Design and Implementation of Energy-Aware Integration Mechanisms for IoT-Cloud Architectures: Enhancing Efficiency in Resource-Constrained Environments
Authors: Deepak.D. Sapkal
Published: November 2024

This study paper suggests a way for the Internet of Things (IoT) and Cloud computing to work together that is smart about energy use. The goal is to solve the problem of IoT-Cloud integration's energy efficiency, which is important for making sensor nodes last longer and making sure that data transfer continues over time. The first step in the process is to plan the IoT nodes and give each sensor node a unique ID and a starting energy level. To make data safer, a homomorphic encryption method is used to make public and private keys. The integration process uses a good Cluster Head Selection Technique that has Random, Energy-Based, Distance-Based, and Density-Based methods to make sure that the sensor nodes are grouped together in the best way possible. The sensor nodes send data to the base station through cluster heads, which use homomorphic encryption to make the connection safe. The public key is used to hash each data packet before it is sent to the cluster head. The cluster head then sends the packets to the base station through the Cloud. At the base station, the data hash is checked one at a time, and then the data is decrypted. This makes sure that the data is correct and safe while it is being sent. Comparing time and energy use across different systems and node numbers is part of the performance assessment. The results show that System 4 did better than the others in terms of how much energy it used and how efficiently it transferred data. This shows that the proposed system can effectively handle IoT-Cloud integration. System 4 kept 28,848 units of energy after starting with 29,442 units, showing that it was much more energy efficient. The results show that using techniques that are aware of energy in IoT-Cloud systems can greatly improve performance. This study adds to the growing body of research on integrating IoT and the cloud by showing a safe, effective, and energy-conscious method that sends data while using the least amount of energy possible.
Linearithmic Mean and Standard Deviation Sorting Method (LMDSM): Efficient and Stable Algorithm
Authors: Haribhau Bhapkar
Published: November 2024

Sorting algorithms have significance in the field of big data and high-performance computing, where effective data processing is of major significance. This paper presents the Linearithmic Mean and Standard Deviation Sorting Method (LMDSM), a new sorting method designed to handle enormous datasets and complex computational environments. Further this paper present a comprehensive comparative analysis of LMDSM with conventional sorting algorithms like Merge Sort and Quick Sort, emphasizing its efficiency and stability in different scenarios. LMDSM consistently outperforms other sorting algorithms in terms of sorting time when tested on arrays of 850,292 data in various orders. Furthermore, paper provide a rigorous theoretical analysis of LMDSM, encompassing its recurrence relation and time complexity derivation. Our research highlights the importance of LMDSM as an effective option for organizing jobs in current computing environments, providing significant importance in speed and reliability.
HAECNN: An Intensity Analysis and Classification of Natural Disasters Using Hybrid Auto-Encoder - Convolutional Neural Network Model for Unprecedented Accuracy
Authors: Gyanendra Singh Tomar
Published: November 2024

Natural catastrophes are a serious threat to the environment, human lives, and property. Effective disaster management and response depend on the precise and timely determination of these occurrences' intensity and classification. In this work, we present a unique method for the analysis and categorization of natural disasters: the Hybrid Auto-Encoder - Convolutional Neural Network (HAECNN). By combining the strengths of convolutional neural networks with auto-encoders, HAECNN is able to classify and measure disaster intensity with previously unheard-of accuracy.HAECNN's auto-encoder part is in charge of autonomously extracting key features from unprocessed inputs like pictures, sensor signals, or satellite imagery. By capturing important details about the calamity, these extracted features strengthen and improve the model's ability to handle a variety of data sources. After the features are recovered, the convolutional neural network component analyses the data to identify spatial patterns and relationships.We carried out in-depth tests on a broad dataset comprising many kinds of natural catastrophes, such as hurricanes, earthquakes, floods, and wildfires, in order to validate the performance of HAECNN. With accuracy rate of over 95% in disaster classification and accurate intensity estimation, our model surpassed other available techniques. Because it can successfully adapt and generalise, HAECNN's outstanding accuracy is ascribed to its ability to utilise the intrinsic data structures and relationships present in many disaster types.For the analysis and categorization of natural disasters, the HAECNN model is a viable option that enhances early warning systems, timely reaction plans, and disaster planning. It is an invaluable tool for governments, first responders, and researchers trying to lessen the effects of natural disasters on the environment and society because of its unmatched accuracy and adaptability.
Enhancing Text-To-Image Translation with Stack-Generative Adversarial Networks
Authors: Parinita Chate
Published: November 2024

Text-to-image translation is the process of generating visually coherent images from textual descriptions. Traditional GANs suffer from inadequacy in capturing intricate details and therefore pose an obstacle in achieving this task. Hence, in this paper, we propose an improved approach based on the concept of Stack Generative Adversarial Networks, in which the generative process is applied in two stages. Stage I produces a 64x64 low-resolution image that offers solid shapes and colors. Then, in Stage II, this is further intensified into a resolution of 256x256 such that it closely resembles the text input.
The architecture of StackGAN surpasses the earlier variant GANs and other derivatives for it produces high-resolution images with semantic correctness closely following the description in the given text. The staged nature of this approach improves image quality while ensuring that a stronger relationship lies between the produced image and the input text. Future works will attempt to enhance the model's capabilities while applying it in other areas of synthesis in images.