bluuebunny/crossref_metadata_2025 huggingface dataset:
Screening and Formulation of Hand Sanitizer with Herbal Extracts of Aloe Vera and Cinnamon
Authors: Sridhar Shruthi
Published: November 2024

Sanitizers are liquid solutions formulated to reduce microbial growth on the hands, typically featuring alcohol as a primary component. However, various plant-derived bioactive compounds exhibit antimicrobial properties that can benefit the skin without adverse effects, enhancing its softness and overall health while providing a natural glow. This study explores the antimicrobial potential of plants such as tulsi, neem, lemon, and eucalyptus. An herbal sanitizer was developed using Aloe Vera and cinnamon extract, with the Microtiter Plate Technique employed to identify the optimal concentrations for effective use. Results revealed that both Aloe Vera and cinnamon extracts were effective in inhibiting microbial growth at a concentration of 1 mg. Notably, the combination of these extracts showed enhanced effectiveness, suppressing microbial growth at 0.5 mg. The formulated sanitizer using this combination inhibited specific microorganisms at 0.75 mg, while demonstrating a remarkable inhibitory effect on hand microbes at just 0.09 mg. These findings underscore the effectiveness of combining Aloe Vera and cinnamon in sanitizers. Additionally, the natural extracts incorporated into the formulation offer prolonged antimicrobial activity and contribute to maintaining skin health.

Keywords: Sanitizer, herbal extract, Aloe Vera, cinnamon, Microbes.
High-Performance Work Systems, Work Intensification, Health Harm and Well-Being: Mediation and Moderation Models
Authors: Firdausari Vega Andi , Triatmono Boge , Khouroh Umu
Published: November 2024

This study aims to examine the impact of work intensification, high-performance work systems (HPWS), and workplace well-being on health harm at Bentoel Group Cigarette Factory, Malang. Using an explanatory research approach, the study explores the mediating role of HPWS as a variable that explains the relationship between work intensification and health harm, as well as workplace well-being as a moderating variable influencing the relationships between variables. Data were collected through questionnaires distributed to 128 production employees in the tobacco processing section using a saturated sampling method. The analysis was conducted using SEM techniques with the SMARTPLS 4.0 software. The results show that the descriptive values of the variables of work intensification, HPWS, health harm, and workplace well-being each have good average scores. Work intensification has a significant positive effect on health harm. Additionally, work intensification has a significant negative effect on the implementation of HPWS. HPWS has a significant negative effect on health harm, indicating that HPWS can reduce health harm. HPWS also mediates the effect of work intensification on health harm with a significant positive influence. Workplace well-being was found to have a significant moderating effect, showing that increased workplace well-being can reduce the negative impact of HPWS on health harm.

Keywords: High-Performance Work Systems (HPWS), Work Intensification (WI), Workplace Well-Being (WWB), Health Harm (HH)
Analysis of the Impact of Fiscal Decentralization on Per Capita GDRP Across Provinces in Indonesia
Authors: Haikal Muhammad , . Sirojuzilam , . Sukardi
Published: November 2024

This study aims to investigate and analyze the impact of fiscal decentralization on per capita GRDP across provinces in Indonesia. Additionally, the research examines the influence of Regional Own-Source Revenue (PAD) on per capita GRDP across provinces in Indonesia, the effect of the General Allocation Fund (DAU) on per capita GRDP, the effect of Revenue Sharing Fund (DBH) on per capita GRDP, and the overall impact of the Specific Allocation Fund (DAK) on per capita GRDP, and the overall impact of fiscal decentralization on per capita GRDP. The study also seeks to analyze to combined effects of PAD, DAU, DBH and DAK on per capita GRDP across provinces in Indonesia. The data analysis technique employed in this research is panel data analysis. The data used are secondary data, which have been collected by data collection institutions and made available to the public. The data for this study were sourced from the Central Bureau of Statistics, the Directorate General of Fiscal Balance, Ministry of Finance of the Republic of Indonesia (DJPK Kemenkeu), as well as books, journals, and website relevant to this research. The variables include per capita GRDP, Regional Own-Source Revenue (PAD), the General Allocation Fund (DAU), the Revenue Sharing Fund (DBH), and the Spesific Allocation Fund (DAK). The result of the study indicate that during the administration of Susilo Bambang Yudhoyono (SBY), the variable PAD had no effect on per capita GRDP, the variable DBH had no effect on per capita GRDP, and the variable DAK also had no effect on per capita GRDP. Meanwhile, during the administration of Joko Widodo (Jokowi), DBH had no effect, and DAK similarly had no effect on per capita GRDP. Furthemore, the R-square value during SBY’s administration was 11,12%, which is higher compared to the 8,7% during Jokowi’s administration. This indicates that the role of PAD, DAU, DBH and DAK in influencing per capita GRDP was more pronounced during SBY’s administration.

Keywords: GRDP, PAD DAU, DBH, DAK
Djenkolism: Acute Kidney Injury Unresponsive to Conservative Therapy - A Case Report
Authors: Ridho Bilhaq Muhammad , Myh Etriyel
Published: November 2024

Djenkol beans (Archidendron jiringa), a popular food in Southeast Asia, can cause djenkolism—a condition leading to acute kidney injury due to the presence of djenkol acid. This case report details a 26-year-old male who presented with three days of anuria following the consumption of half a kilogram of djenkol beans. Upon admission, he exhibited severe abdominal pain, costovertebral tenderness, and elevated renal markers, leading to a diagnosis of obstructive uropathy caused by djenkolic acid crystals. Initial conservative treatment was ineffective, prompting the need for emergency ureteroscopy and placement of bilateral JJ stents to relieve the obstruction. Post-procedure, the patient experienced significant improvement, with a marked increase in urine output; post-obstructive diuresis in the first 24 hours, and resolution of abdominal pain, alongside improved renal function. This case underscores the importance of recognizing djenkolism as a potential cause of acute kidney injury, and advocating for timely diagnosis and intervention to mitigate kidney damage. Public awareness regarding the risks associated with djenkol consumption is also essential.

Keywords: Djenkolism, Obstructive Uropathy, Post-Obstructive Diuresis, Acute Kidney Injury
Morphosyntactic Errors in Students’ Written Narrative Text
Authors: Masriani Mery Rosmida Silalahi  , Rakasiwi Rani , Tampubolon Marlina , Siahaan Ricki Asi Erwindo
Published: November 2024

The objective of this study are to (1) investigate  types of errors in students’ Written Narrative Text made by students grade IX, (2) to find out the causes of errors on the students’ writing. Based on the analysis by Dulay’s theory, there are four kinds of errors: 1. Error of Omission, 2. Error of Addition, 3. Error of Misformation, 4. Error of Misorder. The dominant types of errors found in the students writings is Error of Misformation. The source of errors is analysed based on Brown’s Theory. The source of the errors are: 1) Interlingual Transfer, 2) Intralingual Transfer, 3) Context of Learning, and 4) Communication Strategy. Based on the analysis, the source of errors found are Interlingual Transfer and Intralingual Transfer. This study uses descriptive qualitative research. The subjects of this study are the students of grade IX SMK Satria Binjai. In conclusion, students make some morphosyntactic errors in their writing an d the potential cause of the errors is intralingual Transfer and Interlingual Transfer. In this case, the conclusion and the  suggestions are directed to those who would teach and learn english writing.
Keywords : Error Analysis, Narrative Text, Morphosintactic
Mathematical Modelling for Neural Network-Based Textural Feature Extraction from Normalized Ocular Images for Improved Biometric Authentication
Authors: Kanchan K. Doke
Published: November 2024

Biometric authentication systems have become increasingly prevalent in security and identity verification applications. Among various biometric traits, the ocular region, specifically the iris, offers a unique and stable source for individual identification. The intricate patterns within the iris are highly distinctive and remain consistent over a person's lifetime, making them ideal for biometric applications. In the field of biometric authentication, the ocular region has emerged as a robust and reliable source for identity verification due to its unique and stable features. This study focuses on enhancing the accuracy of biometric systems through neural network-based textural feature extraction from normalized ocular images. The proposed approach utilizes Mask Image Extraction (Hough circle) and an efficient U-Net model to achieve superior performance in biometric authentication. Initially, the ocular images are pre-processed to normalize lighting and orientation, ensuring consistency across the dataset. Mask Image Extraction, particularly using the Hough circle transform, is employed to isolate the region of interest (ROI) within the ocular images. This method accurately detects and segments the circular iris region, which is crucial for subsequent feature extraction. The core of the proposed system is the Efficient U-Net model, a variant of the standard U-Net architecture designed for enhanced efficiency and accuracy. The U-Net model is trained to extract intricate textural features from the segmented ocular regions. By leveraging its encoder-decoder structure, the U-Net captures both local and global features, resulting in a detailed and discriminative representation of the ocular texture. Experimental results demonstrate that the integration of Mask Image Extraction and the Efficient U-Net model significantly improves the accuracy of biometric authentication systems. The proposed method outperforms traditional approaches by providing more precise and reliable feature extraction, thereby enhancing the overall robustness and reliability of ocular-based biometric systems.
Rule-Based Threat Analysis Methodology for Cyber-Physical Systems in Healthcare
Authors: Piyush P. Gawali
Published: November 2024

Cyber-physical system (CPS) integration in the field of contemporary healthcare has completely changed patient monitoring and management. But because these systems are linked, they are vulnerable to different kinds of cyber attacks, which calls for strong security protocols. This work aims to proactively identify and reduce possible security risks by proposing a Rule- Based Threat Analysis Methodology (RBTAM) specifically designed for CPS in healthcare environments. RBTAM uses formalized rules to methodically evaluate threats in the physical and cyberspaces. Its foundations are in discrete mathematics. The process starts with identifying the parts and vulnerabilities of the system, then rules capturing possible threat scenarios are formulated. Among the many things covered by these regulations are denial of service, hardware malfunctions, data tampering, and unauthorized access. By means of an extensive analysis process, RBTAM assesses the probability and consequences of threats found on the availability, confidentiality, and integrity of healthcare systems. Through the facilitation of the prioritization of mitigation measures, this analysis helps healthcare professionals to efficiently distribute resources and reduce possible hazards to data security and patient safety. Important elements of RBTAM are the creation of threat scenarios specific to healthcare settings, the development of security rules based on system architecture, and the incorporation of real-time monitoring systems to identify and address new threats. Furthermore, the approach stresses feedback loops for ongoing improvement, which guarantees flexibility to changing cyberthreats and technology developments. We provide a case study of RBTAM application in a simulated CPS healthcare setting to illustrate its effectiveness. Results show that by efficiently identifying and reducing possible security risks, RBTAM improves the general resistance of healthcare systems to cyberattacks. In the ever-connected healthcare environment, RBTAM provides healthcare professionals and system administrators the tools and insights they need to protect patient well-being and data integrity through a proactive and methodical approach to threat analysis in CPS healthcare.
Fuzzy Similarity Fusion based Feature Selection and Optimization Enabled Deep Learning for Intrusion Detection in WSN
Authors: Aman B Kamble
Published: November 2024

An essential component of data protection is intrusion detection. With the widespread use of wireless sensor networks in military and environmental monitoring, security concerns have grown. The lack of physical defence devices makes data transferred via wireless sensor networks susceptible to attackers. To protect against such threats, appropriate intrusion detection techniques are essential. In this paper, we will construct and explore the suggested fuzzy similarity fusion approach, as well as student psychology Earthworm Optimization Algorithm (SPEWA)-based Deep Q network for intrusion detection. The WSN network will be simulated first, followed by data packet transmission in WSN. The intrusion detection process will then be carried out at the base station. In the base station, the input data will be transmitted to the feature selection module, which will use a newly invented fuzzy similarity fusion approach that combines wrapper, Canberra distance, and fuzzy logic. Once the relevant features have been chosen, intrusion detection will be performed by the Deep Q network, which will be trained using the Student Psychology Earthworm Optimization Algorithm (SPEWA).The SPEWA will be redesigned to incorporate the Student Psychology Based Optimization Algorithm (SPBO) and the Earthworm Optimization Algorithm (EWA). The experiment will be conducted out using the Python programmed BoT-IoT and the MQTT-IoT-IDS2020: MQTT Internet of Things Intrusion Detection Dataset. Furthermore, the performance analysis of the created fuzzy similarity fusion approach will be assessed using four metrics: energy, accuracy, recall, and f-measure.
Adaptive Data Sampling and Feature Optimization with LSH-IS and Pearson Correlation in Big Data Contexts
Authors: Archana S. Banait
Published: November 2024

A huge amount of data is produced with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). Storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. Instance and Feature selection has been a key research area in data mining, which chooses a subset of relevant features for use in model building. This paper aims to provide an overview of instance and feature selection methods for big data mining. It first discusses the current challenges and difficulties faced when mining valuable information from big data.Instance and feature selection has become an effective approach due to enormous data which is continuously being produced in the field of research. It is difficult to process such large datasets by many systems. Though the traditional techniques are useful for large datasets, the numbers when in hundreds, thousands or millions face scaling problems. The proposed work focuses on, scalable instance and feature selection in big data environment. Locality-sensitive hashing instance selection (LSH-IS) is a two pass method used to find similar instances along with Pearson correlation coefficient for feature selection. Hash function family is used which is a general method of reducing the size of a set; this is achieved by re- indexing the elements into buckets. This process find similar instance in same bucket, hence instance can be reduced. The work aims at improving the performance of locality sensitive hashing by storing additional information of the instances and features assigned of each class in the bucket and also to improve accuracy of instance and feature selection algorithm.
Advanced Deep Learning Techniques for Indoor-Outdoor Scene Recognition Integrating CNN and Edge Detection for Enhanced Classification Accuracy in Dynamic Environments
Authors: Pandit T. Nagrale
Published: November 2024

Recognizing indoor-outdoor scenes is an vital portion of computer vision that has an impact on numerous zones, counting driverless driving, virtual reality, and natural following. This article talks approximately a more progressed profound learning framework that employments Convolutional Neural Systems (CNNs) and edge acknowledgment strategies to create classification more precise in settings that alter over time. Distinctive lighting, surfaces, and structure highlights can make it difficult for conventional scene acknowledgment strategies to tell the contrast between complex scenes. We recommend a way to solve these issues that employments the leading parts of both CNNs and edge acknowledgment to urge exact pictures of geometric shapes. The system begins by altering the photographs it gets by utilizing edge acknowledgment strategies, like Canny and Sobel, to discover critical structure edges and lower the clamor. The edge-enhanced pictures are at that point sent to a CNN engineering that was made to recognize scenes. The CNN show is learned on a huge dataset that incorporates a wide run of indoor and open air settings. This makes beyond any doubt that it works well in all sorts of circumstances. A modern layer integration strategy is utilized to connect the CNN's learned highlights with the edge-based highlights. This makes a difference demonstrate tell the contrast between complex scene points of interest superior. It comes about of our tests appear that our combined strategy makes classification much more exact than conventional CNN models. The edge acknowledgment portion truly makes strides the model's capacity to choose up on little changes in picture structures, which leads to more exact forecasts. Moreover, the framework's capacity to adjust to changing situations has been demonstrated by a parcel of testing in a part of distinctive circumstances. Combining profound learning with edge discovery might make scene acknowledgment way better, and the proposed strategy shows how this may work. This may lead to more advanced and viable computer vision frameworks in real-world circumstances that are complicated.
Design and Development of Intrusive Detection Model based on Sequential Deep Recurrent Neural Network (SDRNN) Classifier
Authors: Nekita Chavhan
Published: November 2024

Intrusion detection systems (IDS) must defend advanced communication networks. The systems' major goal was to identify broken patterns, signatures, and rules. IDS is crucial to network cyber defense. IDS can now detect both regular and atypical patterns thanks to advances in artificial intelligence, notably machine learning and deep learning. This research discusses a revolutionary deep learning and machine learning approach to intelligent categorization in IDS model development. The main objective is to detect network traffic irregularities utilizing HIKARI-2021. With an optimized feature set for the dataset, a deep learning model achieved 99.78% accuracy for binary and multi-class classification tasks.
Statistical Prediction of Liquefaction Potential in Mining Dump Areas of the Nagpur Region Using Standard Penetration Test (SPT) Data
Authors: Manish V. Bawankule
Published: November 2024

Liquefaction could be a complicated and questionable but critical subject in ground building, particularly in places that are inclined to seismic tremors. Since of the visit seismic tremors in Nagpur, the developing mining industry makes it difficult to construct successful strategies for getting freed of mine squander. Seismic tremors are a major reason why structures fall flat, for the most part since of liquefaction, which does a parcel of harm to framework. This consider is for the most part approximately Nagpur, which is the third-largest city in Maharashtra and is growing very rapidly. The most objective is to form a arrange of the Nagpur mine dump ranges, figure out how likely it is that the dumps will liquefy, and make the information accessible for future investigate. The think about looks at soil liquefaction within the mine zone utilizing an SPT-N-based strategy. The liquefaction potential is measured utilizing the factor of security (FS) at distinctive soil levels that were found amid the think about. Standard Entrance Testing (SPT) was exhausted 11 test spots around the mine dumping range, and the comes about were composed down in boreholes. Modern investigate shows that the affectability to liquefaction is influenced by the number of SPT blows and the most extreme ground movement. The SPT-N esteem is balanced for the Cyclic Resistance Ratio (CRR), which appears that the soil's qualities and water table levels alter, and higher N values are ordinarily display. This implies that the soil contains a much lower chance of liquefying.
Durability Analysis and Cost Efficiency of RCC Beams Using Conventional Concrete Versus Bubble Deck Technique: A Statistical Investigation
Authors: Sanjay Bhadke
Published: November 2024

A crucial tectonic component in the building of houses and other structures are beams. The structural dead load is substantially reduced by virtually removing the non-structural concrete from the beam's core. A bubble beam is one in which various sized and shaped spheres are used in place of the beam's core. There are benefits to factory-made materials and components, regulated environments, on- and off-site finishing, hollow Bubble Deck technology, and regulated settings. Numerous advantages are offered, such as reduced total expenses, reduced material usage, enhanced structural effectiveness, expedited building, and eco-friendliness. Instead of using concrete, the midsection of the beam in this project is constructed from high density polyethylene balls the content that appears most regularly.
Comparative Analysis of Materials for Chassis Design in a Three-Wheeled Electric Vehicle
Authors: Pranav Charkha
Published: November 2024

With the growing interest in sustainable transportation, three-wheeled electric vehicles have gained popularity as an eco-friendly mode of transportation. The design of the chassis is a critical aspect of ensuring the structural integrity and performance of these vehicles. This research paper presents a comparative evaluation of different materials for chassis design in three-wheeled electric vehicles. The objective of this study is to assess the suitability of various materials, including steel, aluminum, and carbon fiber composites, for chassis construction. The evaluation criteria encompass factors such as strength, weight, stiffness, durability, and cost. A systematic methodology is employed, including theoretical analysis, computer simulations, and practical testing, to evaluate and compare the performance of each material. The findings of this research provide valuable insights into the advantages and limitations of each material in the context of three-wheeled electric vehicle chassis design. The results assist in the selection of the most suitable material based on performance requirements, cost considerations, and sustainability goals. Furthermore, the research identifies potential areas for further improvement and innovation in chassis materials for future electric vehicle designs. The outcomes of this study aim to guide manufacturers, designers, and engineers in making informed decisions when selecting materials for chassis design, ultimately enhancing the safety, efficiency, and overall performance of three-wheeled electric vehicles. By contributing to the development of sustainable transportation solutions, this research promotes the transition towards a greener and more environmentally friendly mobility landscape.
Determination of Uncertainty in calibration of External Micrometer
Authors: Sandip Nehe
Published: November 2024

In recent years there has been a growing awareness of measurement uncertainty, mainly for two reasons. First, the ever- increasing number of laboratory accreditations requires the evaluation of measurement uncertainty, especially in the area of calibration. Second, quality system protocols get mature with process and time. This is because manufacturers ensure the reliability of their measurements through correct calibration of their test and measuring equipment. International and national regulations require calibration and testing laboratories to provide result of measurement uncertainty. Calibration of the measuring instrument is the process in which the readings obtained from the instrument are compared with the standard values in the laboratory at several points along the scale of the instruments i.e. micrometer, gauges, calipers etc. In general, a calibration without information about the uncertainty of these estimates is incomplete. Calculating measurement uncertainty is an attempt to reasonably limit the results of measurements as referring to standard operating procedures. Since each measurement only provides a result of the outcome, the main requirement of the uncertainty estimation is to make reader confident that reading is within stated range. Proper calibration involves more than just estimating measurement uncertainty. It also requires that the estimated value is small compared to the stated accuracy of the meter
Optimal Feature Picking for Intrusion Detection on The Basis of Explainable Artificial Intelligence
Authors: Thamraj Narendra Ghorsad
Published: November 2024

Traditional security mechanisms for intrusion detection are strong, and the use of machine learning algorithms to detect intrusion into IoT networks has increased. As a result, the efficiency of the IoT network has increased and an effective model can be developed to detect attacks on the network. UNSW-NB15 is an IoT-based publicly available dataset for traffic data that contains general activity and malicious activity. Using this dataset, the key features were selected and these features fed different classifiers for training and classifying the attack in the network. Ann model performance levels of accuracy, the ML classifier, and accompanying algorithms for constructing a digital security system based on packet flow in-network, and features that really can monitor anomalous botnet behaviors proven to be highly efficient. The decision-making model for the classification was visualized using proven Explainable AI (XAI) approaches utilizing Scikit-Learn, LIME, ELI5, and SHAP libraries to boost explainability in classification predictions. The findings showed that XAI is realistic and profitable since security expertise and specialists stand to benefit greatly from combining conventional machine learning methods with Explainable AI (XAI) methodologies.
Quotient Permutation BF-Algebras and Quotient Maps
Authors: Qasim Mohsin Luhaib
Published: November 2024

The permutation BF-algebras was given as a new class of BF-algebras and their basic features in this work were studied. We considered and discussed some new notions in permutation BF-algebras, like permutation BF1/BF2-algebras, permutation BF-ideals, permutation BF-subalgebras, normal permutation BF-subalgebras. We further looked at the homomorphism of BF-algebras, congruence relation, quotient permutation BF-algebras, and Quotient map.
Analytical Solution of the Klein-Gordon coupled Equations via the Differential Transform Method
Authors: Asal B. Saleh
Published: November 2024

The basic equations in physical applications are the Klein-Gordon equation, as it gives a description of numerical particles in the fields of quantum and quantum mechanics, through the use of the differential transformation method and combining it with the Idomian method, in order to provide an integrated method for solving this equation, and it has been widely used to solve differential equations. Partial, to get rid of nonlinear terms and complex terms by integrating these two methods, which led to reducing the error and thus increasing the efficiency and accuracy of the solution using these numerical methods and the theoretical analysis of the method.. Researchers can learn more about quantum field dynamics using our method, which makes it easier to study a wide range of physical phenomena including scalar particles. In addition to improving the computational toolset in relativistic quantum mechanics, this work offers a useful foundation for investigating basic interactions in the field of particle physics and other fields.
Conclusions: Adomain decomposition method (ADM) and the Differential Transform Method (DTM) together provide a powerful approach to solving the Klein-Gordon system equation. The effectiveness of this integrated method in efficiently and precisely solving complex differential equations controlling scalar particle dynamics has been proven by our research. The nonlinearities and boundaries of the Klein-Gordon system equation have proven to be formidable obstacles, but we have overcome them by combining the two approaches, and the findings are solid and trustworthy. Scalar particle behaviour in relativity quantum mechanics can be better understood with the help of this comprehensive methodology, which also improves computational efficiency. The DTM technique is a powerful and flexible tool for academics in many fields, including applied mathematics, particle physics, and quantum field theory, as our results show. The future looks bright for tackling   ever more complex systems and improving our understanding of basic physical processes as we continue to investigate and enhance this integrated approach.
Changing the Dimension of Points Using a Polynomial in PG(2,11)
Authors: Thakreen Faisal Sultan
Published: November 2024

The main goal of this research is to change the dimensions of the points PG(2,11) by changing the formula of the points, the projective plane, into a polynomial and then multiplying them by the polynomial , which is not analyzable. Through this, points with dimensions different from the dimensions of the original points are obtained.
Graphical and Matrix Coding Methods for Enhanced Message Security Using Star Graphs
Authors: V. Sudhakar
Published: November 2024

This article presents a new method for encoding secret messages using super-intermediate symbols in star charts. The system includes two super average images and two formats, as well as graphics and matrix encoding methods for added security. The authors provide a method to adjust the average of the three star maps to ensure the stability of the encoded message. This research provides a non-programming method for developing matrix graphics coding techniques and has implications for fields such as telecommunications and cryptography. The use of super-average symbols and star charts provides a secure method of communication with applications in data encryption, network security, and confidential communication. The results of this study show that the effectiveness of the proposed system in improving the security and privacy of encrypted messages makes it useful in business.
Sustainable Plastic Waste Management in Thanjavur District: An Evaluation Using Fuzzy AHP and Neutrosophic TOPSIS
Authors: V. Usha
Published: November 2024

Introduction: The amount of plastic garbage has increased in tandem with the steady growth in the use of plastic products worldwide. Since plastics are lightweight, inexpensive, and durable, they are highly appreciated in a variety of industries, including electronics, building, packaging, healthcare, and agriculture). Plastic's cost, adaptability, resistance to corrosion, durability, and ease of production all contribute to its rising demand.With changes to the Plastic Waste Management Rules, India has made managing single-use plastics a top priority.
 Objectives: The research provides an extensive examination of the methods used to manage plastic garbage in Thanjavur's rural villages. Examining household behaviors and waste production trends, it reveals a wide range of plastic kinds, including polystyrene (PS), polypropylene (PP), high-density polyethylene (HDPE), low-density polyethylene (LDPE), and polyethylene terephthalate (PET).
Methods: The research uses a Multi-Criteria Decision Making (MCDM) approach that combines the Neutrosophic Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) for alternative ranking with the Fuzzy Analytic Hierarchy Process (FAHP) for criteria weighting. Although it uses fuzzy logic to address ambiguity and uncertainty in decision-making processes, the Fuzzy AHP approach is applied in a manner similar to that of AHP.
Results: Egestas diam in arcu cursus euismod quis viverra nibh. Convallis aenean et tortor at risus viverra. Sit amet justo donec enim diam. Sem et tortor consequat id. Purus gravida quis blandit turpis. Consectetur adipiscing elit duis tristique sollicitudin nibh sit amet commodo. Eget duis at tellus at urna condimentum mattis pellentesque. Auctor elit sed vulputate mi sit amet.
Conclusions: This study determines that, among the available options, Polyethylene Terephthalate (PET) is the best option by applying the fuzzy analytic hierarchy process (Fuzzy AHP) to balance these criteria. PET exhibits potential because of its economic and environmental advantages, but its effective application necessitates customized strategies that take into account the infrastructure constraints and socioeconomic characteristics of the local area. Programs for raising community knowledge and participation are crucial in promoting sustainable waste management techniques, as the study emphasizes.
Semi-Invertible Fuzzy Topological Space
Authors: Anjaly Jose
Published: November 2024

This paper fuzzifies the concept of semi-invertibility in a topological space, also it portrays the properties of such spaces. The structure of fuzzy semi inverting pairs is brought forward. Additionally, the correlation between semi-invertible and invertible fuzzy topological spaces is investigated. The transfer of fuzzy first category property from a local to a global context within a semi-invertible fuzzy topological space is explored.
Development and Evaluation of a New Algorithm for Real-Time Image Processing
Authors: Shubhangi Milind Joshi
Published: November 2024

When working with real-time images, creating and testing new methods is very important for making computers work faster and more accurately. This study describes a new method that is meant to solve problems in real-time picture processing applications by making them faster and better at what they do. It was created because of the need to deal with picture data that was getting more complicated in many areas, including autonomous systems, medical imaging, monitoring, and multimedia apps. Several new methods are built into the suggested program to help it reach its goals. To begin, it uses advanced parallel processing designs to speed up calculations without lowering their quality. The method cuts down on processing delays by making the best use of resources and job ordering. This makes it ideal for time-sensitive apps that need to make quick decisions based on real-time picture data. The algorithm also uses strong feature extraction techniques that are designed to work well with a wide range of picture traits and noise levels. This improves the algorithm's ability to work in a variety of settings, making sure it always works well in different working situations. Adaptive filtering mechanisms and advanced edge recognition algorithms are two important parts of the algorithm that work together to make it quick and easy to get useful information. A lot of testing was done to see how well the algorithm worked against the best available methods, using normal image processing datasets and real-life situations. A lot of attention was paid to performance measures like processing speed, accuracy in feature recognition, and computer economy. When compared to traditional methods, comparative research showed big gains in processing speed without sacrificing accuracy.
Trust Computation Model for IoT devices using Soft Computing and Machine Learning Techniques for Health Care Applications
Authors: Bhawana Atul Ahire
Published: November 2024

The calculation of trust for online web services is of utmost importance due to the security risks associated with network devices. various cloud platforms offer diverse services for communicating with various IoT devices and generating predictions based on the collected data. Occasionally, data may be compromised by either internal or external groups through low-impact hacking techniques. Investigating assaults and ensuring the elimination of connections can be a laborious task in a sensitive setting. Despite some progress, there are still many unresolved security issues that provide barriers to addressing security problems. This article seeks to conduct a comprehensive examination of existing security solutions for the lowest layer of the Internet of Things (IoT) and promote the development of more inventive IoT designs for those who invest in them. This paper proposes a system for calculating trust in IoT devices using both supervised and unsupervised machine learning algorithms for non-invasive health care applications. Both strategies were assessed based on trust computation weights and compared with their respective machine learning algorithms. The K-means clustering technique is employed for one strategy, while the SVM algorithm is used for the other approach. The aim of this successful implementation is to identify and eradicate a minor threat that occurs during the trusted execution process. An experimental analysis was conducted using NS2 2.35 in combination with an open-source Java platform. At first, a total of 100 nodes are created in the simulation environment. These nodes are then assessed using a clustering method to determine if they are trustworthy or untrustworthy, resulting in the generation of labels. The experimental research demonstrated the superior efficacy of attack detection in comparison to traditional methods of attack detection.
Intelligence Model based Routing with Channel Assignment in Multichannel Mobile Ad-Hoc Networks
Authors: Vijay Omprakash Rathi
Published: November 2024

Multiple wireless channels in MANET promise to enhance QoS, and their well-coordinated use can lead to major operational enhancements. For coordination & synchronization across numerous channels, existing approaches for developing multi-channels in mobile ad hoc networks (MANETs) employ a control channel-based technique. Offering QoS via resource reservation, routing, mobility management, & access/admission control has become the most important problems recently in MANETs. Nevertheless, due to obvious interference caused by mobility, several existing solutions fail to offer good QoS. This paper intends to introduce a novel routing and channel assignment in multichannel MANET. Here, the optimal routing is performed via selecting the cluster head under the certain constraints like Delay, distance, QoS, RSSI, and security. For this, (Chaotic Functions influenced spider monkey optimization) CFISMO algorithm is used. The assignment of channels as the scheduling policy is introduced through senders while it has packets to transmit. In this work, the channel assignment will be initiated via machine learning model that predicts the availability of the channels, which is based on the paths (channels) generated under the selected cluster head. Here, Optimized Neural Network (NN) will be used. Thus, the final output shows the paths (channel) to be assigned for data transmission. At the end, the performance of the adopted routing approach is evaluated over other traditional schemes based on various metrics like distance, PDR, delay, energy, alive nodes, QoS, security, and trust, respectively.
IoT-Driven Healthcare an Ensemble Learning Approach for Early Detection and Prevention of Chronic Diseases
Authors: Gunjan Behl
Published: November 2024

This investigate presents an IoT-driven healthcare system leveraging outfit learning methods for early discovery and avoidance of unremitting illnesses. By joining information from wearable sensors, shrewd domestic gadgets, and electronic wellbeing records, the system offers a comprehensive wellbeing checking framework. Gathering learning calculations, counting Random Forest, Slope Boosting, Stacking, and Bolster Vector Machines, are utilized to combine numerous models' qualities, improving forecast exactness and robustness. Experimental comes about on an assorted dataset illustrate the viability of the proposed approach, accomplishing a precision of 85%, accuracy of 87%, review of 84%, F1-score of 85%, and AUC-ROC of 0.92. Comparative examinations with standard calculations and related work within the field emphasize the prevalence of the outfit learning approach in leveraging IoT information for healthcare applications. This investigation contributes to progressing IoT-driven healthcare by giving bits of knowledge into the potential of gathering learning procedures for illness expectation and avoidance. By making strides early location and personalized mediations, the system points to upgraded understanding results, diminishing healthcare costs, and progress in populace well-being management.
Enhancing Diagnostic Accuracy: A Hybrid Approach to Mitigate Speckle and Rician Noise in Medical Images
Authors: Naveenkumar Jayakumar
Published: November 2024

This research explores the detrimental effects of Rician and speckle noise in medical imaging, particularly within MRI and radiographic modalities. Such noise can significantly obscure critical details, potentially leading to misdiagnoses and inaccurate measurements. Rician noise candidly dangles on the signal strength. Removing this noise affects the origial information in image that makes this noise removal perplexing. On other hand speckle noise is visualised like granular clog in ultrasound imageries. It creates ambiguous details and edges in the images.   To address this challenge, the study introduces a novel hybrid method implemented in Java. The method employs a frequency-based decomposition approach for speckle noise and separates images into magnitude and phase components for Rician noise. This allows for targeted filtering, utilizing customized versions of median and Gaussian filters, to effectively remove noise while preserving crucial image features. The proposed method was rigorously assessed using 200 contaminated brain and lung MRIs and radiographs, demonstrating superior performance compared to traditional filters based on crucial system of measurement like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Squared Error (MSE). This research presents a promising solution for enhancing the quality and reliability of medical images, potentially leading to improved diagnostic accuracy and patient outcomes.
Centroid, Kalman Filtering and Neural Network based Approach for Mobile Target
Authors: Mahadev Mahajan
Published: November 2024

In recent years, the knowledge of location is of prime importance for success of many location-based services (LBS) applications such as indoor navigation, emergency management, elderly tracking, etc. There are many popular approaches in the literature which utilize received signal strengths (RSSs) in wireless sensor network (WSN) to track the moving target. Many times for the applications wherein localization accuracy to the scale of few centimetres is required, the application of one localization approach is not sufficient. The important reason is the highly fluctuating nature of RSS’s, and dynamicity in target motion and surrounding environment. Although generalized regression neural network (GRNN) has very nice estimation ability, we believe that the addition of the concept centroid to the GRNN architecture and fusing it with kalman filter (KF) can further improve its target localization performance. This paper proposes two robust range-free RSS based target localization algorithms namely, CGRNN+KF, and CGRNN+UKF, obtained by the fusion of fundamentals of centroid, GRNN, and KF. From the results obtained, it is observed that the proposed CGRNN+KF, and CGRNN+UKF algorithms yield positioning accuracy of few centimetres and effectively deal with the highly fluctuating nature of RSS’s, and dynamicity in target motion and surrounding environment.
Detection of Encroachment in Civil Structure Using Dynamic Learning Techniques
Authors: Rahul B. Diwate
Published: November 2024

Identifying cracks through visual inspection and automated surveys are both effective methods. While both approaches yield satisfactory distress analysis results, automated crack recognition technology stands out for its speed and cost-effectiveness compared to traditional human visual detection methods. This study introduces an innovative approach, namely "crack encroachment in concrete structures classified using DCNN" which employs a Deep Convolutional Neural Network. To enhance the precision of crack detection, the input image undergoes denoising through ADNLMF (Anisotropic Diffusion Non-Local Mean Filtering), preserving edges, textures, and features. Crack discrimination between crack and non-crack images was achieved using Yolo v3, and a deep convolutional neural network classifier was employed to identify specific crack types based on their widths, utilizing crack width transform. This method not only enhances accuracy but also reduces network complexity and processing time. A comprehensive performance comparison with existing crack identification techniques indicates that our proposed methodology for concrete structure crack recognition produces superior results.
Enhanced Hydroponic Farming with Crop Suitability Prediction
Authors: Ankita H. Harkare
Published: November 2024

Hydroponics, a soil-less cultivation technique, offers efficient resource utilization and enhanced crop yields. However, precise water quality control is essential to ensure optimal nutrient absorption and successful crop growth. This research paper presents a comprehensive IoT-based water quality monitoring system specifically designed for hydroponics, incorporating pH, turbidity, and temperature sensors. The system can acquire data from sensors, data processing, and data visualization in real time. The sensor data is seamlessly transmitted from Arduino to ESP8266 and securely stored on the ThingSpeak cloud platform. Utilizing web scraping techniques, the collected data is extracted from ThingSpeak and seamlessly integrated into a user-friendly website. On the website, a machine learning (ML) model, trained on required data, automatically processes the realtime sensor readings and performs crop suitability predictions. The system enables continuous and automated monitoring of key water quality parameters, ensuring optimal conditions for hydroponic crop growth. The deployed ML model demonstrates remarkable performance in predicting water suitability for different crops. The model provides real-time predictions on the website by utilizing the data, empowering hydroponic farmers with data-driven insights to optimize crop selection based on water quality. To evaluate the system’s efficacy, extensive experimentation, and validation have been conducted using hydroponic setups with various crop types.