bluuebunny/crossref_metadata_2025 huggingface dataset:
Endodontic treatment parameter optimization using central composite rotatable design
Authors:   School of Mechanical Engineering, Dr. Vishwanatah Karad MIT World Peace University, Pachpore S.S. , Jadhav Pradeep V. ,   Department of Mechanical Engineering Bharati Vidyapeeth
Published: December 2023

Endodontology referred to as Root Canal Treatment (RCT) is a field of science that deals with the entire chemo-mechanical cleaning and sealing of the root canal system by using restorative material used to rescue or repair severely decaying or diseased teeth. The success of the treatment depends on the seal formed between the filling material and the root cavity post-obturation. The crucial components include the energy used to clean and seal the root canal system, the material used to fill the root canal system, the process used to fill the root canal system, and the geometry of the root canal system. In the presented research, ideal operative parameters and their interaction were studied with 32 experimental trials for five operative parameters namely canal angle, canal operative length, compaction force, operative temperature, and operating frequency using central composite rotatable design (CCRD); a response optimization tool. The treatment was carried out on endo-training blocks prepared with a 6% uniform taper across the working length by using gutta-percha as filling material. A compaction force of 10N, operating temperature between 90-92° C, operating frequency in the range of 40-50 Hz, and operative length of 12mm results in maximum gutta percha compaction
Contextualization: A Path to Chinese Traditional News Media’s Integration Into Social Media
Authors: Guo Difan , Wang Haiyan , Xu Jinghong
Published: December 2023

Meyrowitz’s media context theory proposes that new media and their contexts will lead to new behaviors. This article adopts media context theory as a framework and utilizes a textual analysis approach to analyze what Meyrowitz termed middle region behaviors and the contextualization strategies of the traditional Chinese news media (People’s Daily) on the social media platform Weibo. The findings reveal three of People’s Daily’s Weibo news’ innovation strategies: the middle regionalization of news contexts (live news, vlog news, chatbox news); personalized production of important news (Weibo commentary, user-produced news); and equal dialogue with the public (daily greetings, holiday greetings, popularizing science). The study also indicates that traditional news media can utilize social media to consolidate communication effectiveness and reconstruct their credibility while actively participating in social governance. In light of these findings, we think that the “contextualization” strategies employed by People’s Daily on the Weibo platform offer meaningful possibilities for traditional news organizations’ integration into social media, such as exploring innovative approaches to news presentation, emphasizing audience interaction, appropriately providing “non-news content” for the audience, and maintaining a commitment to objectivity and fairness in news reporting.
A jó hely – egy turizmusorientált koncepcióvázlat
Authors: Michalkó Gábor
Published: December 2023

A „jó hely” szókapcsolat korunk mindennapi kommunikációjának szerves részévé vált, leginkább egy szabadidős szolgáltatás vagy egy település, térség vonatkozásában, az ott szerzett tapasztalatok közreadása során hangzik el. Az utazási döntést befolyásoló közösségi médiafelületek (pl. Instagram, TripAdvisor) posztjait, illetve topikjait vizsgálva százezres nagyságrendben találkozhatunk a „jó hely” szókapcsolat felhasználásával a turisztikai élmények megosztásakor. Mivel a „jó hely” szókapcsolatra fókuszáló elméleti megközelítés egyrészt hozzájárulhat a hellyel kapcsolatos kutatások szélesítéséhez, másrészt alkalmas lehet a turisztikai desztinációmenedzsment-szervezetek tevékenységének támogatására, szükséges egy hiánypótló koncepcióvázlat elkészítése. Ugyan Ray Oldenburg a nevével fémjelzett elméletben a „jó hely” kifejezést az úgynevezett harmadik hely szinonimájaként használta, a „jó hely” egyáltalán nem vált a társadalomtudományok terminológiájának szerves részévé. Jelen tanulmány elsőként tesz kísérletet arra, hogy a turizmuskontextusú kommunikációban elterjedt „jó hely” szókapcsolat jövőbeli transzdiszciplináris vizsgálataihoz és azok várható eredményeire épülő menedzsment-implikációkhoz elméleti keretet teremtsen.
A Web of Science adatbázist hasznosító kulcsszóelemzés alapján megállapítható, hogy a „jó hely” koncepció a helykötődés-elmélet bázisán a turisztikai desztináció iránti elköteleződés teljes folyamatának értelmezését és az abból fakadó akciók katalizálását segíti elő. A tanulmány a harmadik hely elméletben észlelt rés szűkítése érdekében vállalkozik a „jó hellyé” válás folyamatának modellezésére és a helyértés turisztikai kontextusú definiálására.
Brain Tumor Detection and Multi Classification Using GNB-Based Machine Learning Approach
Authors: Gujar Satish N. , Jha Jaimala , Pingat Sanjaykumar P. , Pandey Rashmi , Kumar Atul , Gupta Ashish , Pise Priya
Published: December 2023

In an abnormal tissue called a brain tumor, the cells of the tumor reproduce quickly. if no control over tumor cell growth. The difficulties involved in identifying and treating brain tumors Machine learning is the most technologically sophisticated tool for classification and detection, implementing reliable state-of-the-art A.I. as well as neural network classification techniques, the use of this technology in early diagnosis detection of brain tumors can be accomplished successfully. it is well known that the segmentation method is capable of helping simply destroy the brain's abnormal tumor regions In order to segment and categorize brain tumors, this study suggests a multimodal approach involving machine learning and medical assistance. Noise can be seen in MRI images. To make the method for eliminating noise from images easier, a geometric mean is used later. The algorithms used to segment an image into smaller pieces are fuzzy c-means algorithms. Detection of a specific area of interest is made simpler by segmentation. The dimension reduction procedure is carried out using the GLCM. Photographic features are extracted using the GLCM algorithm. Then, using a variety of ML techniques, like as CNN, ANN, SVM, Gaussian NB, and Adaptive Boosting, the photos are categorized. The Gaussian NB method performs more effectively with regard to the identification and classification of brain tumors. The plasterwork work achieved 98.80 percent accuracy using GNB, RBF SVM.
Precision Gas Leakage Detection System with Integrated Sensors
Authors: Vishnu Sakthi D Et al.
Published: December 2023

Gas leaks pose significant safety risks in both residential and industrial settings, making gas leakage detectors essential safety devices for many years. These detectors serve the crucial function of identifying the presence of hazardous gases, such as carbon monoxide, and activating an alarm system to promptly alert occupants of the building. This innovative gas leak detection system leverages mobile technology, integrating phone calls, SMS, WhatsApp, and GPS location services. Additionally, it features a unique capability to shut off the power supply in the affected area, providing a real-time and effective means of gas leak detection and containment. This gas leakage detection system prototype has been successfully developed and tested with gases such as BUTANE and PROPENE. The experimental results demonstrate that the system can detect gas leaks in less than a minute. The primary objective of this gas detection project is to implement a security system for identifying gas leaks in closed environments. It utilizes MQ-2 sensors designed to function effectively within enclosed spaces. Furthermore, this system can be seamlessly integrated with other security and safety systems for enhanced overall protection.
Yolov5 AI Deep Learning model driven Nuclear Pleomorphism Grading on Breast Cancer Pathology WSI for Nottingham Cancer Grading
Authors: Rajasekaran Subramanian Et al.
Published: December 2023

Breast cancer is the second largest cancer caused in the world due to the uncontrollable growth in breast cells. Nottingham Grading is the internationally acceptable system to grade breast cancer. Nuclear pleomorphism is one of the breast cancer biomarkers for computing Nottingham grading. Pathologists grade nuclear pleomorphism on breast cancer glass tissue slides using a conventional microscope which is time consuming and has considerable inter-observer variability between pathologists. The paper proposed an Artificial Intelligence (AI) deep learning model to grade grade1, grade2, grade3 nuclear pleomorphism on breast cancer whole slide images (WSI). The proposed Yolov5 model is trained and tested on 1,30,000 WSI tiles having around two lakh annotations. The accuracy of the model is mAP 0.89. The proposed model saves the time and reduces the workload of the pathologist and also helps them  to produce accurate results.
Image Enhancement of Colon Cancer Images using a Two-Stage Hybrid Approach of TV and Shift-Invariant Filtering
Authors: Jitendra Prakash Patil Et al.
Published: December 2023

Medical imaging holds a critical position in both disease diagnosis and treatment strategies, including colon cancer. However, the quality of medical images can often be compromised by noise and artifacts, making accurate interpretation challenging. Here, we suggest a innovative two-stage hybrid method aimed at enhancing colon cancer images, leveraging the strengths of Total Variation (TV) denoising and shift-invariant filtering techniques. The primary objective of this study is to increase visual superiority as well as diagnostic accurateness of colon cancer image while preserving crucial anatomical information.The first stage of our approach employs Total Variation (TV) denoising to reduce noise and enhance image contrast. TV regularization is known for its ability to preserve edges and fine details, making it well-suited for medical image enhancement. In the second stage, we apply shift-invariant filtering to further enhance the image quality. This technique is designed to address the limitations of traditional filtering methods and adapt to the specific characteristics of colon cancer images.

To evaluate the effectiveness of our hybrid approach, we conducted a comprehensive set of experiments using a relevant dataset. We employed a range of quantitative metrics, including the Global Relative Error (EGRAS), Root Mean Squared Error (RMSE), Universal Image Quality Index (UQI), and Pixel-Based Visual Information Fidelity (VIFP), to assess the quality and fidelity of enhanced images. Our results demonstrate that the hybrid combination consistently outperforms existing methods, yielding superior image quality and diagnostic potential. This study makes a valuable contribution to the realm of medical imaging by introducing a robust and effective method to improve the quality of colon cancer images. Findings suggest that the proposed two-stage hybrid method holds promise for improving the accuracy of diagnosis and treatment planning. Further research in this direction may lead to advancements in medical image enhancement techniques, ultimately benefiting patient care and medical research.
Improving Kidney Tumor Detection Accuracy Using Hybrid U-Net Segmentation
Authors: Anupkumar B Jayswal Et al.
Published: December 2023

Kidney cancer stands as a significant factor in cancer-related mortality, highlighting the critical importance of early and precise tumor detection This study introduces a computer-aided approach using the KiTS19 dataset and a hybrid U-Net architecture. Manual tumor segmentation is resource-intensive and prone to errors. Leveraging the hybrid U-Net, known for its proficiency in medical image analysis, we achieve precise tumor identification. Our method involves initial kidney and tumor segmentation in high-resolution CT images, followed by region of interest (ROI) generation and benign/malignant tumor classification. The assessment conducted on the KiTS19 dataset demonstrates encouraging outcomes, with Dice coefficients of 0.974 for kidney segmentation and 0.818 for tumor segmentation, accompanied by a tumor classification accuracy rate of 94.3%.The hybrid U-Net’s advanced feature extraction and spatial context awareness contribute to these outcomes. By streamlining diagnosis, our approach has the potential to significantly improve patient outcomes. The use of the KiTS19 dataset ensures robustness across various clinical cases and imaging modalities. This method represents a valuable advancement in computer-aided kidney tumor detection, promising to enhance patient care.
An Intensive Spectrum for Intention Mining Analysis
Authors: Varsha D. Jadhav Et al.
Published: December 2023

There is huge volume of data in the social networks. This data can be retrieved and integrated to extract useful meaning and come out with the insights which is called as intentions. This can be used in different fields like business, recommender systems, education, Scientific research, games, etc. Also, there are various intention mining techniques which can be applied to several fields as information retrieval, business, etc. There is no specific definition of intention mining and also there is very less existing literature present. Accordingly, there is need to conduct systematic literature review of the very recent research area. Understanding intention mining, purpose of intention mining, categories and techniques of intention mining is the need. The paper endorses a spectrum for intention mining so that further literature review of intention mining can be completed. We validate our work through dimensions, categories and techniques for intention mining.
Experimental Testing and Validation of Adaptive Equalizer Using Machine Learning Algorithm
Authors: Annapurna H. S Et al.
Published: December 2023

Due to the increasing demand for high-speed data transmission, wireless communication has become more advanced. Unfortunately, the various kinds of impairments that can occur when carrying data symbols through a wireless channel can affect the network performance. Some of the solutions that are proposed to address these issues include channel equalization, and that can be solved through machine learning techniques. In this paper, a hybrid approach is proposed that combines the features of tracking mode and training mode of adaptive equalizer. This method utilizes the concept of machine learning (ML) to classify different environments (highly, medium, low, open space cluttered) based on the measurements of their RF signal. The results of the study revealed that the proposed method can perform well in real-time deployments. The performance of ML algorithms namely Logistic Regression, KNN Classifier, SVM Classifier, Naive Bayes, Decision Tree classifier and Random Forest classifier is analyzed for different number of samples such as 10, 50 and 100. The performance of these algorithms is evaluated by comparing their accuracy, sensitivity, specificity, F1 score and Confusion Matrix. The objective of this study is to demonstrate that a single ML algorithm cannot perform well in all kinds of environments. In order to choose the best algorithm for a given environment, the decision device has to analyze the various factors that affect the performance of the system. For instance, the random forest classifier performed well in terms of accuracy (100 percent), specificity (100 percent), sensitivity (100 percent), and F1_score (100 percent). On the other hand, the logistic regression algorithm did not perform well in low cluttered environment.
A State-of-the-Art Survey for IoT Security and Energy Management based on Hashing Algorithms
Authors: Teba Mohammed Ghazi Sami Et al.
Published: December 2023

The Internet of Things (IoT) has developed as a disruptive technology with wide-ranging applications across several sectors, enabling the connecting of devices and the acquisition of substantial volumes of data. Nevertheless, the rapid expansion of networked gadgets has generated substantial apprehensions pertaining to security and energy administration. This survey paper offers a detailed examination of the present state of research and advancements in the field of Internet of Things (IoT) security and energy management. The work places special emphasis on the use of hashing algorithms in this context. The security of the Internet of Things (IoT) is a crucial element in safeguarding the confidentiality, integrity, and availability of data inside IoT environments. Hashing algorithms have gained prominence as a fundamental tool for enhancing IoT security. This survey reviews the state of the art in cryptographic hashing techniques and their application in securing IoT devices, data, and communication. Furthermore, the efficient management of energy resources is essential to prolong the operational lifespan of IoT devices and reduce their environmental impact. Hashing algorithms are also instrumental in optimizing energy consumption through data compression, encryption, and authentication. This survey explores the latest advancements in energy-efficient IoT systems and how hashing algorithms contribute to energy management strategies. Through a comprehensive analysis of recent research findings and technological advancements, this survey identifies key challenges and open research questions in the fields of IoT security and energy management based on hashing algorithms. It provides valuable insights for researchers, practitioners, and policymakers to further advance the state of the art in these critical IoT domains.
Reconfigurable Multiband Dynamic Range Compression-based FRM Filter for Hearing Aid
Authors: Anjali Shrivastav Et al.
Published: December 2023

In this research, we present an innovative method for enhancing the performance of hearing aids using a Multiband Dynamic Range Compression-based Reconfigurable Frequency Response Masking (FRM) Filterbank. First, a unform16-band reconfigurable filter bank, which is reconfigurable, is designed utilizing the FRM scheme. The strategic arrangement of each sub-band within the proposed filter bank is meticulously prepared to optimize the matching performance. Based on the hearing characteristics of patients, the sub-bands can be distributed in low, medium, and high-frequency regions. Also, the gain can be adjusted per the patient's hearing profile from their audiogram for better auditory compensation.

Further, the Multiband Dynamic Range Compression (MBDRC) technique is applied to address the specific needs of individuals with different frequency-dependent hearing impairments. It involves using dynamic range compression independently to different frequency sub-bands within a filter bank. In MBDRC, the compression parameters, such as compression threshold and ratio, can be adjusted independently for every subband. It allows for a more tailored approach to address the specific hearing needs of different frequency regions. If an individual has more severe hearing loss in high-frequency regions, higher compression ratios and lower compression thresholds can be applied to those subbands to amplify and improve audibility for high-frequency sounds. Once dynamic range compression is applied to each sub-band, the resultant sub-bands are reassembled to yield the ultimate output signal, which can subsequently be transmitted to the speaker or receiver of the hearing aid. A GUI can be helpful for better visualization and parameter control, including gain adjustment and compression parameters of this entire process. With this aim in mind, a GUI has been developed on MATLAB. Different audio files can be imported, and their frequency response can be generated and observed. Based on a person's audiogram, the control parameters can be set to low, medium, or high. Their sub-band distribution in low, medium, and high-frequency regions can be visualized. Further, the filter bank makes automatic gain adjustments, as seen in the GUI. The gain points for each band can also be manually adjusted according to users' hearing characteristics to minimize the error. Also, the compression parameters can be set separately for each subband as per the hearing requirement of the patient. Further, the processed output can be visualized in the output frequency response tab, and the input and output audio signals can be analyzed.
Multi Factor Authentication and IP Address Restriction based Question Paper Delivery System for Indian Universities
Authors: Chaitali Shewale Et al.
Published: December 2023

The traditional question paper delivery (QPD) procedure in Indian institutions is discussed in this paper with a focus on its downsides, including complexity, time requirements, security issues, environmental impact, and cost. This method is manual, which makes it prone to mistakes, rigid, and open to leaks and tampering, especially when it comes to the security of the exam questions. It also looks at security issues involving question paper leaks, highlighting ongoing difficulties in preserving the integrity and confidentiality of the Indian educational system. These occurrences highlight the requirement for a more reliable and secure QPD system. 

In this paper workflow of the cloud-based software system is described in detail, with examples showing how it makes it easier to distribute safe exam papers and assures stakeholder accountability. Barcodes are integrated into the system, and their improved tracking, processing, and distribution of exam papers is highlighted. A cutting-edge cloud-based Question Paper Delivery System (OQPDS), which automates and streamlines the process of generating, distributing, and giving exams for Indian colleges, is the suggested cure. Modern security elements are used by the system, including two-step authentication, barcode-based data storage, data encryption during data transfer, IP address-based access restrictions, and time-based content delivery. The incorporation of these security elements enhances the overall security and efficiency of the QPD system. The proposed system's workflow is discussed in detail along with examples that demonstrate how it facilitates the distribution of secure exam papers and ensures stakeholder responsibility. The system incorporates barcodes, and its better distribution, processing, and tracking of exam papers is praised. The system's capacity to limit access based on IP addresses is described, along with how it might help with traceability and prevent unwanted access.  Additionally, the paper proposes watermarking to label test papers with the IP address of the testing facility, offering benefits including improved security, tamper detection, and traceability. It is highlighted that encryption is a key security tool for preserving data integrity, maintaining secrecy, and protecting against online dangers. Finally, the cloud-based Question Paper Delivery System is a solution to the security issues and inefficiencies of the current QPD system. This system intends to transform the examination procedure in Indian institutions by offering a more safe, effective, and transparent approach to question paper administration. It does this through contemporary security provisions and cutting-edge technology. This paper also discusses comparative analysis between traditional and proposed automated Question paper delivery system with respect to efficiency, security, cost reduction and flexibility.
A New Way for Face Sketch Construction and Detection Using Deep CNN
Authors: Sonali Antad Et al.
Published: December 2023

Traditional hand-drawn face sketches have encountered speed and accuracy issues in the field of forensic science when used in conjunction with contemporary criminal identification technologies. To close this gap, we provide a ground-breaking research article that is built on a stand-alone program that aims to revolutionize the production and identification of composite face sketches. This ground-breaking approach does away with the requirement for forensic artists by enabling users to easily create composite sketches using a drag-and-drop interface. Utilizing the power of deep learning and cloud infrastructure, these generated sketches are seamlessly cross-referenced against an enormous police database to identify suspects quickly and precisely. Our research study offers a dual-pronged approach to combating the rise in criminal activity while using the quick breakthroughs in artificial intelligence. First, we demonstrate how a specific Deep Convolutional Neural Network model transforms sketches of faces into photorealistic photographs. Second, we employ transfer learning for precise suspect identification using the pre-trained VGG-Face model. Utilizing Convolutional Neural Networks, which are famous for their data processing powers and hierarchical feature extraction, is a key component of our strategy. This approach exceeds current methods and boasts an extraordinary average accuracy of 0.98 in identifying people from sketches, providing a crucial tool for strengthening and speeding up forensic investigations. A unique Convolutional Neural Network framework that demonstrates significant improvements over state-of-the-art techniques is also revealed as we dive into the challenging task of matching composite sketches with corresponding digital photos. Our thorough analysis shows the framework to be remarkably accurate, constituting a substantial advance in the field of forensic face sketch production and recognition.
An IoT based Tree Specific Soil Nutrient Management System Using Neural Network for Cashew Cultivation
Authors: C. Sudha Et al.
Published: December 2023

Anacardium occidentale commonly known as Cashew was introduced into India by Portuguese in 16th century. Farmers of Cuddalore District, Tamil Nadu in India, are interested in Cashew Cultivation due to its export market values. To achieve better yield from cashew cultivation, farmers need to supply balanced nutrition to the soil and by following intercropping method. In literature, Recent Technologies like Artificial Intelligence and IOT are widely used to predict dosage of the fertilizer to be applied. By examining the numerous connected ascribe success locations of Cashew orchards, it was possible to determine how much macronutrients like Nitrogen (N), Phosphorus (P), and Potassium (K) were present in the soil and it will be helpful for the understanding of soil fertility level of the area. In our work, using IOT set up which includes Arduino UNO, NPK sensor and OLED display, we identified NPK values of soil for various cashew trees in a particular area. Along with NPK values of soil, inputs include the local soil type, PH and Tree age. Based on the input factors, DL trained model will provide suggestions of fertilizer dose to be applied for a specific tree for the Cashew cultivating farmers for better yield.  In our proposed work, LSTM based Recurrent Neural Network (RNN) Algorithm is used to provide a better prediction of fertilizer. With the help of this research, we can be provided with what intercrop to plant and how much fertilizer to use, in what ratio to supply and fertilizer shop suggestion to enhance agricultural knowledge across the globe using the Internet of Things (IoT) and Artificial Intelligence. The result of RNN model was compared with Random Forest, Linear Regression, Linear Random Classifier, Decision Tree Classifier with proving accuracy of 98.7 %. Together with a decrease in the farmers' input efforts, these strategies will increase the productivity of the fields.
Convolutional Neural Network – Based Algorithm for Currency Exchange Rate Prediction
Authors: Manaswinee Madhumita Panda Et al.
Published: December 2023

The foreign exchange market is one of the complex monetary markets in the world. Each day trillions of dollars are traded in the FOREX market by banks, retail traders, corporations, and individuals. It is very challenging to predict the price in advance due to the complex, volatile and high fluctuation. Investors and traders are constantly searching for innovative ways to outperform the market and increase their profits. As an outcome, forecasting models are continually being developed by scholars around the globe to accurately predict the characteristics of this nascent market. This study intends to apply the Random Forest (RF) approach to Convolutional Neural Networks, which involves two key steps. The first step is starting with feature selection using Convolutional neural network.The attention layer is then employed to assign weight.The random forest strategy is designed in the second stage to generate high-quality feature subsets. Thus the better result generated by CNN-RF model. Actually, this strategy combines the advantages of two different strategies to produce an outcome that is more consistent with what exchange market decision-makers anticipate happening in the exchange market.The main currency pairs considered in this study's proposed model for predicting exchange rates five and ten minutes in advance are the British Pound Sterling (GBP) against the US Dollar (USD), the Australian Dollar (AUD) against the US Dollar (USD), and the European Euro (EUR) against the Canadian Dollar (CAD) are also used to evaluate the performance of the proposed model.   In compared to the other three models (Multi-Layer Perceptron, Autoregressive Integrated Moving Average, and Recurrent Neural Network), CNN-RF yields better results. This conclusion has been backed by a large body of empirical research, which also suggested that this methodology be regularly used due to its high efficacy.
Analytical Model for Assessing the Impact of MANETs' Bottleneck Nodes
Authors: Ijteba Sultana Et al.
Published: December 2023

Objectives: Due to few resources, the most challenging task in mobile ad hoc networks is determining a route with the highest probability of success between a source and a destination. The objectives of this assignment are as follows: The limited power provided by batteries is one of the most significant challenges in routing. A great number of routing protocols have been devised to deal with the problem of limited battery power. Reliability, low energy consumption, and preferential treatment of nodes with greater energy levels are the cornerstones of the routing path made possible by these protocols. On the other hand, these protocols result in the creation of a bottleneck intermediate node, which is a node that must forward packets from numerous sources. The loss of packets is the most significant issue that emerges with a bottleneck intermediate node.

Methods and Statistical Analysis: In this study, we employ a poison random process to assess an intermediate's performance of bottleneck node within a mathematical model we developed. Findings: The model that is being proposed calculates the typical length of the queue at the input buffer, the typical amount of time spent waiting in the buffer and the lifetime of the network.

Application and enhancements: This model is put to use to regulate the incoming traffic of an intermediate node and to determine whether or not that node will participate in the route.
A Diaspora of Humans to Technology: VEDA Net for Sentiments and their Technical Analysis
Authors: Kirti Sharma Et al.
Published: December 2023

Background: Human sentiments are the representation of one’s soul. Visual media has emerged as one of the most potent instruments for communicating thoughts and feelings in today's world. The area of visible emotion analysis is abstract due to the considerable amount of bias in the human cognitive process. Machines need to apprehend better and segment these for future AI advancements. A broad range of prior research has investigated only the emotion class identifier part of the whole process. In this work, we focus on proposing a better architecture to assess an emotion identifier and finding a better strategy to extract and process an input image for the architecture.

Objective: We investigate the subject of visual emotion detection and analysis using a connected Dense Blocked Network to propose an architecture VEDANet. We show that the proposed architecture performed extremely effectively across different datasets.

Method: Using CNN based pre-trained architectures, we would like to highlight the spatial hierarchies of visual features. Because the image's spatial regions communicate substantial feelings, we utilize a dense block-based model VEDANet that focuses on the image's relevant sentiment-rich regions for effective emotion extraction. This work makes a substantial addition by providing an in-depth investigation of the proposed architecture by carrying out extensive trials on popular benchmark datasets to assess accuracy gains over the comparable state-of-the-art. In terms of emotion detection, the outcomes of the study show that the proposed VED system outperforms the existing ones (accuracy). Further, we explore over the top optimization i.e. OTO layer to achieve higher efficiency.

Results: When compared to the recent past research works, the proposed model performs admirably and obtains accuracy of 87.30% on the AffectNet dataset, 92.76% on Google FEC, 95.23% on Yale Dataset, and 97.63% on FER2013 dataset. We successfully merged the model with a face detector to obtain 98.34 percent accuracy on Real-Time live frames, further encouraging real-time applications. In comparison to existing approaches, we achieve real-time performance with a minimum TAT (Turn-around-Time) trade-off by using an appropriate network size and fewer parameters.
An Innovative Method for Lung Cancer Identification Using Machine Learning Algorithms
Authors: Kavita Singh Et al.
Published: December 2023

Biological community and the healthcare sector have greatly benefited from technological advancements in biomedical imaging.  These advantages include early cancer identification and categorization, prognostication of patients' clinical outcomes following cancer surgery, and prognostication of survival for various cancer types. Medical professionals must spend a lot of time and effort gathering, analyzing, and evaluating enormous amounts of wellness data, such as scan results. Although radiologists spend a lot of time carefully reviewing several scans, tiny nodule diagnosis is incredibly prone to inaccuracy. Low dose computed tomography (LDCT) scans are used to categorize benign (Noncancerous) and malignant (Cancerous) nodules in order to study the issue of lung cancer (LC) diagnosis. Machine learning (ML), Deep learning (DL), and Artificial intelligence (AI) applications aid in the rapid identification of a number of infectious and malignant diseases, including lung cancer, using cutting-edge convolutional neural network (CNN) and Deep CNN architectures, we propose three unique detection models in this study: SEQUENTIAL 1 (Model-1), SEQUENTIAL 2 (Model-2), and transfer learning model Visual Geometry Group, VGG 16 (Model-3). The best accuracy model and methodology that are proposedas an effective and non-invasive diagnostic tool, outperforms other models trained with similar labels using lung CT scans to identify malignant nodules. Using a standard LIDC-IDRI data set that is freely available, the deep learning models are verified. The results of the experiment show a decrease in false positives while an increase in accuracy.
A Hybrid Optimization Algorithm for Efficient Virtual Machine Migration and Task Scheduling Using a Cloud-Based Adaptive Multi-Agent Deep Deterministic Policy Gradient Technique
Authors: Gurpreet Singh Panesar Et al.
Published: December 2023

This To achieve optimal system performance in the quickly developing field of cloud computing, efficient resource management—which includes accurate job scheduling and optimized Virtual Machine (VM) migration—is essential. The Adaptive Multi-Agent System with Deep Deterministic Policy Gradient (AMS-DDPG) Algorithm is used in this study to propose a cutting-edge hybrid optimization algorithm for effective virtual machine migration and task scheduling. An sophisticated combination of the War Strategy Optimization (WSO) and Rat Swarm Optimizer (RSO) algorithms, the Iterative Concept of War and Rat Swarm (ICWRS) algorithm is the foundation of this technique. Notably, ICWRS optimizes the system with an amazing 93% accuracy, especially for load balancing, job scheduling, and virtual machine migration. The VM migration and task scheduling flexibility and efficiency are greatly improved by the AMS-DDPG technology, which uses a powerful combination of deterministic policy gradient and deep reinforcement learning. By assuring the best possible resource allocation, the Adaptive Multi-Agent System method enhances decision-making even more. Performance in cloud-based virtualized systems is significantly enhanced by our hybrid method, which combines deep learning and multi-agent coordination. Extensive tests that include a detailed comparison with conventional techniques verify the effectiveness of the suggested strategy. As a consequence, our hybrid optimization approach is successful. The findings show significant improvements in system efficiency, shorter job completion times, and optimum resource utilization. Cloud-based systems have unrealized potential for synergistic optimization, as shown by the integration of ICWRS inside the AMS-DDPG framework. Enabling a high-performing and sustainable cloud computing infrastructure that can adapt to the changing needs of modern computing paradigms is made possible by this strategic resource allocation, which is attained via careful computational utilization.
Design An Intelligent System to Support Dental Cyst Detection Using Two Convolutional Neural Networks
Authors: Jinu Thomas Et al.
Published: December 2023

The aim of this paper is to develop a methodology, through studies on Computer Vision techniques, for the automatic identification of dental cysts in panoramic radiography images, providing Dental professionals with an alternative aid in the interpretation of these images. In addition segmentation techniques are applied in the inner region of the jaws, seeking to separate the regions with a greater possibility of cyst. The objective of this work is to design an intelligent system that supports the diagnosis of Dental Cyst using convolutional neural networks in order to help detect Dental Cyst at an early stage. The research method applied in this study consists of model design, where built and trained two convolutional neural network architectures, supporting 80% of the dataset with a total of 775 images with four image categories, and proposal validation, where we work with the remaining 20% of the dataset. Our results show that the ResNet50 architecture achieved the best classification with an accuracy of 98%.
A Reliable Network Routing Protocol Design with an Intelligent Mobile Sink and Energy Efficiency over Wireless Sensor Network
Authors: Sunita S S Patil Et al.
Published: December 2023

This is crucial for the successful operation of a Wireless Sensor Network, frequently called as WSN as each of the sensor nodes in the network's structure are in charge of transmitting the information from its original source to its final destination. Several researchers have developed a Mobile sink in an effort to enhance transmission quality. Although it has been shown to be beneficial, the network's overall reliability is compromised by the much more energy-intensive operation of the nodes. In this research, we offer a routing strategy that makes use of cluster and source portability to drastically reduce power usage. We have given this protocol its name: the Intelligent Mobile Sink Assisted Routing Protocol (IMSARP).  To get started, we divide an entire sensor environment into regions, and within each of them, members cast proportional votes to choose who will serve as the Cluster-Head (CH). To determine how to choose the most efficient choice, nodes in the network evaluate the power consumption of all feasible routes. The proposed IMSARP method uses a cluster-based paradigm to construct a mobile-sink routing protocol. The standard quantity of energy within all the clusters serves as what drives the sink's motion. The outcomes section of this research proves the legitimacy and maintains the credibility of the proposed system by presenting the results, which include throughput, delay reduction, energy efficiency, and data transfer rate.
Deep Learning-based Copy-Move and Spliced Image Forgery Detection
Authors: Divya Prathana Timothy Et al.
Published: December 2023

This paper proposes a Deep Learning (DL) based pre-trained AlexNet model for detecting and localizing copy-move and spliced forgery in photos. To localise forgeries in a photo, a binary mask is constructed using sobel operators. Further feature vectors are extracted patch wise from the input pictures. The Spatial Rich Model (SRM) is employed to address the generalisation issues in the DL model. There are three datasets used: Columbia Uncompressed Image Splicing Detection Evaluation (CUISDE), CASIA 1, and CASIA 2. The overall performance of the model has a 98.59 percent total accuracy as against 98.176% reported in the existing literature.
Trust Score based Optimized Cluster Routing (TSOCR) approach for Enhancing the Lifetime of Wireless Sensor Networks
Authors: Sri S. Suresh Babu Et al.
Published: December 2023

Energy efficiency is the most significant obstacle that Wireless Sensor Networks (WSN) must overcome, and the desire for solutions that maximize energy efficiency will never go away. There are a variety of methods that can be utilized to improve energy efficiency, with data transmission as the primary driver of maximum energy consumption. The transmission of data from the source to destination nodes uses more energy. When the transmission of data is handled better, the energy efficiency is improved and the lifetime of the network is increased. The purpose of this research is to propose an Trust Score based Optimized Cluster Routing (TSOCR)  scheme for WSNs, which is based on Whale Optimization Algorithm (WOA). A total trust score is derived by combining the results of computing three distinct trust scores, such as the direct, indirect, and the most recent trust score. The path that has the highest trust score is chosen as the route and employed for data transmission. The effectiveness of the work is evaluated by looking at factors such as the rate of packet delivery, the latency, the amount of energy consumed and the lifetime of the network.
Comprehensive Survey and Analysis of Techniques, Advancements, and Challenges in Video-Based Traffic Surveillance Systems
Authors: Joshi Rakhi Madhukaro Et al.
Published: December 2023

The challenges inherent in video surveillance are compounded by a several factors, like dynamic lighting conditions, the coordination of object matching, diverse environmental scenarios, the tracking of heterogeneous objects, and coping with fluctuations in object poses, occlusions, and motion blur. This research endeavor aims to undertake a rigorous and in-depth analysis of deep learning- oriented models utilized for object identification and tracking. Emphasizing the development of effective model design methodologies, this study intends to furnish a exhaustive and in-depth analysis of object tracking and identification models within the specific domain of video surveillance.
Advancing Chronic Respiratory Disease Care with Real-Time Vital Sign Prediction
Authors: A. Joshi Et al.
Published: December 2023

Cardiovascular and chronic respiratory diseases, being pervasive in nature, pose formidable challenges to the overall well-being of the global populace. With an alarming annual mortality rate of approximately 19 million individuals across the globe, these diseases have emerged as significant public health concerns warranting immediate attention and comprehensive understanding. The mitigation of this elevated mortality rate can be achieved through the application of cutting-edge technological innovations within the realm of medical science, which possess the capacity to enable the perpetual surveillance of various physiological indicators, including but not limited to blood pressure, cholesterol levels, and blood glucose concentrations. The forward-thinking implications of these pivotal physiological or vital sign parameters not only facilitate prompt intervention from medical professionals and carers, but also empower patients to effectively navigate their health status through the receipt of pertinent periodic notifications and guidance from healthcare practitioners. In this research endeavour, we present a novel framework that leverages the power of machine learning algorithms to forecast and categorise forthcoming values of pertinent physiological indicators in the context of cardiovascular and chronic respiratory ailments. Drawing upon prognostications of prospective values, the envisaged framework possesses the capacity to effectively categorise the health condition of individuals, thereby alerting both caretakers and medical professionals. In the present study, a machine-learning-driven prediction and classification framework has been employed, wherein a genuine dataset comprising vital signs has been utilised. In order to anticipate the forthcoming 1-3 minutes of vital sign values, a series of regression techniques, namely linear regression and polynomial regression of degrees 2, 3, and 4, have been subjected to rigorous examination and evaluation. In the realm of caregiving, a concise 60-second prognostication is employed to enable the expeditious provision of emergency medical aid. Additionally, a more comprehensive 3-minute prognostication of vital signs is utilised for the same purpose. The patient's overall health is evaluated based on the anticipated vital signs values through the utilisation of three machine learning classifiers, namely Support Vector Machine (SVM), Decision Tree and Random Forest. The findings of our study indicate that the implementation of a Decision Tree algorithm exhibits a high level of accuracy in accurately categorising a patient's health status by leveraging anomalous values of vital signs. This approach demonstrates its potential in facilitating prompt and effective medical interventions, thereby enhancing the overall quality of care provided to patients.
Ensemble Learning for fraud detection in Online Payment System
Authors: Kothapalli. Mandakini Et al.
Published: December 2023

The imbalanced problem in fraud detection systems refers to the unequal distribution of fraud cases and non-fraud cases in the information that is used to train machine learning models. This can make it difficult to accurately detect fraudulent activity. As a general rule, instances of fraud occur much less frequently than instances of other types of occurrences, which results in a dataset which is very unbalanced. This imbalance can present challenges for machine learning algorithms, as they may become biased towards the majority class (that is, non-fraud cases) and fail to accurately detect fraud. In situations like these, machine learning models may have a high accuracy overall, but a low recall for the minority class (i.e., fraud cases), which means that many instances of fraud will be misclassified as instances of something else and will not be found. In this study, Synthetic Minority Sampling Technique (SMOTE) is used for balancing the data set and the following machine learning algorithms such as decision trees, Enhanced logistic regression, Naive Bayes are used to classify the dataset.Majority Voting mechanism is used to ensemble the DT,NB, ELR methods and analyze the performance of the model. The performance of the Ensemble of various Machine Learning algorithms was superior to that of the other algorithms in terms of accuracy (98.62%), F1 score (95.21%), precision (98.02%), and recall (96.75%).