bluuebunny/crossref_metadata_2025 huggingface dataset:
Geo-Distance Based 2- Replica Maintaining Algorithm for Ensuring the Reliability forever Even During the Natural Disaster on Cloud Storage System
Authors: Selvi S.Annal Ezhil
Published: September 2023

In today's digitalized and globalized scenario, everyone has moved to cloud computing for storing their information on cloud storage to access their data from anywhere at any time. The most significant feature of cloud storage is its high availability and reliability then it has the capability of reducing management factors as well as incurred lower storage cost compared with some other storing methods, it is most suitable for a high volume of data storage. In order to meet the requirements of high availability and reliability, the system adopts a replication system concept. In replicating systems, the objects are replicated many times, with each copy residing in a different geographical location. Though it is beneficial to the users, it leads to some issues like security, integrity, consistency and hidden storage and maintenance cost, etc. Therefore, it is exposed to a few threats to the Cloud Storage System (CSS) user and the provider as well. So, this research seeks to explore the mechanisms to rectify the above-mentioned issues. Thus, the predecessor of the research work has proposed an algorithm named as 2-Replica Placing (2RP) algorithm which is used to reduce the storage cost, maintenance cost; and maintenance overheads as well as increase the available storage spaces for the providers by placing the data files on two locations based on Geo-Distance. But it fails to address the recovery mechanism when a natural disaster happens because providing reliability with less than 2 replicas is a challenging task for the providers. Thus, the research proposed Geo-distance based 2-Replica Maintaining (2RM) algorithm which is used to consider that issue for ensuring reliability forever even during natural disasters
Flood Prediction using MLP, CATBOOST and Extra-Tree Classifier
Authors: Kundra K Sandhya Rani , Lakshmi B. Jaya , Venugopal I V S , Guthula Venkatesh
Published: September 2023

Flooding can be one of the many devastating natural catastrophes, resulting in the annihilation of life and damaging property. Additionally, it can harm farmland and kill growing crops and trees. Nowadays, rivers and lakes are being destroyed, and the natural water reservoirs are converted into development sites and buildings. Due to this, even just a bit of rain can cause a flood. To minimize the number of fatalities, property losses, and other flood-related issues, an early flood forecast is necessary. Therefore, machine learning methods can be used for the prediction of floods.To forecast the frequency of floods brought on by rainfall, a forecasting system is built using rainfall data. The dataset is trained using various techniques like the MLP classifier, the CatBoost classifier, and the Extra-Tree classifier to predict the occurrence of floods. Finally, the three models' performances are compared and the best model for flood prediction is presented. The MLP, Extra-Tree, and CatBoost models achieved accuracy of 94.5%, 97.9%, and 98.34%, respectively, and it is observed that CatBoost performed well with high accuracy to predict the occurrence of floods.
Resource Allocation Challenges and Strategies for RF-Energy Harvesting Networks Supporting QoS
Authors: Magar Harshada B. , Sharma Pramod , Ubale Archana S. , Ahire Sandhya O. , Philip Vineeta
Published: September 2023

This paper specifically addresses the resource allocation challenges encountered in wireless sensor networks that incorporate RF energy harvesting capabilities, commonly referred to as RF-energy harvesting networks (RF-EHNs). RF energy harvesting and transmission techniques bring substantial advantages for applications requiring Quality of Service (QoS) support, as they enable proactive replenishment of  wireless devices. We commence by providing an overview of RF-EHNs, followed by an in-depth examination of the resource allocation challenges associated with this technology. In addition, we present a case study that focuses on the design of an efficient operating strategy for RF-EHN receivers. Our investigation highlights the critical aspects of service differentiation and QoS support, which have received limited attention in previous research. Besides, we explore previously unexplored areas within these domains.
Machine Learning Technique for Prediction of Breast Cancer
Authors: Narasimhaiah P. , Nagaraju C.
Published: September 2023

Breast cancer is one of the most prominent disease and the second foremost source of death among middle-aged women in the world. Removing of breast tumour by using a surgical treatment and chemotherapy could work excellently if it can be identified as a primary tumour or at an early stage of transmutation, however it is a costly process. The quick development of machine learning techniques continues to burn the medical tomography enthusiasm in implementing to improve the accurateness of tumour findings. To identify breast cancer in the area of machine learning lots of attempts were made, but these techniques are not too accurate. In the proposed Machine Learning Technique for Prediction of Breast Cancer (MLTPBC) is an automated system used to remove a label, pectoral muscles, noise, and identification of cancer. The experimental results of the proposed MLTPBC shows the preferable accuracy over the existing methods.
Classification of Cyberbullying Detection in Social Networking with Audio using Machine Learning Approach
Authors: Saichandana Bandari , Kamakshi Pille
Published: September 2023

Every day, more people use the internet and social media, which leads to an increase in cyberbullying vulnerabilities. By transmitting, posting, and spreading damaging, false, and bad stuff online, it is taking place. For those impacted, it causes psychological and emotional issues. Therefore, the development of automated tools for cyberbullying identification and prevention is essential. Recent research on identifying cyberbullying has largely focused on text-based analysis. The two most significant media in cases of cyberbullying are text and audio. In this paper, a machine learning model for detecting cyberbullying in two types of social data, namely text and audio is presented. This paper is focused on detecting majors form of Cyberbullying: cyberbullying dataset on Twitter and classify them as containing Cyberbullying or not. This paper used datasets, namely, ‘cyberbullying Dataset’. In this paper, the implementation can be done with machine learning algorithms such as logistic regression, naive bayesian classifier, and support vector machine. Also, these three algorithms were compared and evaluated with performance metrics like accuracy, precision, recall, and f1 score. The main aim is to detecting cyberbullying messages on any type of social media platform.
A Novel Detection Method for Grey Hole Attack in RPL
Authors: Shiranzaei Atena , Khoshbakht Farhad
Published: September 2023

The Internet of Things (IoT) is a type of network that involves the Internet and things. This network consists of constrained devices that are connected through an IP protocol. In the IoT, a network with constrained devices is called 6LowPAN. RPL is a routing protocol to address the constraints and specific properties of these networks; though RPL puts the networks at risk through a large variety of attacks. The urgent need to develop secure routing solutions is required. In this paper, we investigated grey hole attacks and presented a detection method to identify and isolate the malicious node. The experiments show the proposed detection method improves PDR, Throughput and reduces PLR and E2ED in comparison with other scenarios.
Estimation of State of Charge of Battery Used In Electric Vehicles With Wireless Battery Management System
Authors: Lohar Ganesh , Kumar M. Suresh
Published: September 2023

This research paper presents a comprehensive investigation into the development and analysis of a wireless battery management system (BMS) using MATLAB Simulink. The primary objective of this study is to create an efficient, reliable, and scalable BMS that caters to the demands of various applications, such as electric vehicles, grid energy storage, and portable electronics. Our methodology involves designing and simulating key BMS components, including state estimation algorithms, fault detection mechanisms, and communication protocols, within the MATLAB Simulink environment. The paper first elucidates the motivation for adopting wireless technology in BMS, emphasizing its advantages over traditional wired systems. Subsequently, we explore the intricacies of the proposed wireless BMS architecture, detailing the implementation of essential features such as state-of-charge estimation, fault diagnosis, and thermal management. We also address the challenges associated with wireless communication, including latency, security, and energy efficiency, by incorporating robust communication protocols and power management strategies. Through rigorous simulations, we demonstrate the efficacy of the proposed wireless BMS, showcasing its ability to ensure optimal performance, safety, and longevity of battery packs. The outcomes of this research not only contribute to the advancement of BMS technology but also pave the way for further improvements in battery-powered systems. In conclusion, this paper offers a holistic perspective on wireless BMS design, emphasizing its potential to revolutionize energy management and extend the applications of battery technology in various domains.
Real-Time Monitoring and Assessment System with Facial Landmark Estimation for Emotional Recognition in Work
Authors: Zhu Chaoyang
Published: September 2023

The Model for Monitoring and Regulating Emotional States in the Work Environment based on Neural Networks and Emotion Recognition Algorithms presents an innovative approach to enhancing employee well-being and productivity by leveraging advanced technologies. This paper on the development of a system that utilizes neural networks and emotion recognition algorithms to monitor and interpret emotional cues exhibited by individuals in real-time within the work environment. With the uses of novel Directional Marker Controlled Facial Landmark (DMCFL) Emotion recognition algorithms are employed to analyze facial expressions, speech patterns, physiological data, and text-based communication to infer the emotional state of employees. Neural networks are then utilized to process this data and provide more sophisticated emotion classification and prediction. The emotional states are classified with the integrated Regression Logistics Classifier (RLC) model for classification. The analysis of the findings expressed that the real-time monitoring enables employers and supervisors to gain insights into the emotional well-being of employees, identifying patterns and potential issues. The system facilitates feedback and regulation mechanisms, allowing for personalized interventions and support tailored to individual emotional needs.
An Optimized Approach for Maximizing Business Intelligence using Machine Learning
Authors: Afsaruddin Afsaruddin , Agrawal Devendra , Nayak Sandeep Kumar
Published: September 2023

The subject of study known as business intelligence is responsible for the development of techniques and tools for the analysis of business information with the goal of assisting in the management and decision-making processes of corporations. In the current climate, business intelligence is essential to the process of formulating a strategy and carrying out operations that are data-driven. Throughout the many stages of the company operation, an organization will need assistance evaluating data and making decisions; a decision support system may provide this assistance by including business intelligence as an essential component. The fact that this enormous quantity of data is distributed over a number of different types of platforms, however, makes it a difficult challenge, in particular to understand the information that is actually relevant and to make efficient use of it for business intelligence. One of the most important challenges facing modern society is maximizing business intelligence through the application of machine learning. It offers a full analysis that is based on predictions and is extracted for Business Intelligence techniques along with current application fields. This anomalous gap has been pointed up, and solutions and future research areas have been offered to overcome it in order to create effective business strategies.
5G Enabled Moving Robot Captured Image Encryption with Principal Component Analysis Method
Authors: Moorthy T. Venkata Krishna , Bachu Srinivas , Ramesh Gajula , Reddy Dasaratha Rami , Krishn S. Vamsee , Dharani M.
Published: September 2023

Estimating the captured image of moving robots is very difficult. These images are vital in analyzing earth's surface objects for many applications like studying environmental conditions, Land use and Land Cover changes, and change detection studies of worldwide change. Multispectral robot-captured images have a massive amount of low-resolution data, which is lost due to a lack of capture efficiency due to artificial and atmospheric reasons. The image transformation is required in a 5G network with effective transmission by reducing noise, inconsistent lighting, and low resolution, degrading image quality. In this paper, the authors proposed the machine learning dimensionality reduction technique i.e. Principle Component Analysis (PCA) and which is used for metastasizing the 5 G-enabled moving robot captured image to enrich the image's visual perception to analyze the exact information of global or local data. The encryption algorithm implanted for data reduction and transmission over the 5G network gives sophisticated results compared with other standard methods. This proposed algorithm gives better performance in developing data reduction, network convergence speed, reduces the training time for object classification, and improves accuracy for multispectral moving robot-captured images by the support of 5G network.
Detecting Sybil Attack in Blockchain and Preventing through Universal Unique Identifier in Health Care Sector for privacy preservation
Authors: Raghav Nidhi , Bhola Anoop Kumar
Published: September 2023

Health care data requires data secrecy, confidentiality, and distribution through public networks. Blockchain is the latest and most secure framework through which health care data can be transferred on the public network. Blockchain has gained attention in recent year’s due to its decentralized, distributed, and immutable ledger framework. However, Blockchain is also susceptible to many attacks in the permission less network, one such attack is known as Sybil attack, where several malicious nodes are created by the single node and gain multiple undue advantages over the network. In this research work, the Blockchain network is created using the smart contract method which gets hampered due to Sybil attack. Thus, a novel method is proposed to prevent Sybil attack in the network for privacy preservation. Universal Unique Identifier code is used for identification and prevention of the Sybil attack in the self-created networks. Results depict that proposed method correctly identifies the chances of attack and the prevention from the attack. The approach has been evaluated on performance metrics namely, true positive rate and accuracy which were attained as 87.5 % and 91% respectively, in the small network. This demonstrates that the proposed work attains improved results as compared to other latest available methods.
Epidemic Diseases Forestall Module using Data Science and SIR Algorithms
Authors: Shimbre Nivedita , Patil Tanvi , Late Rutuja , Jagtap Kshitij , Patil Amogh
Published: September 2023

This survey paper is intended to prevent epidemic diseases and pandemic diseases. According to the WHO every year in the world over 17 million people die due to this type of disease. Epidemic diseases have lower transmission rate than pandemic diseases and they spread in a bounded area. On the other hand, pandemic diseases have higher transmission rate and it can easily spread in an immense area. We can control this type of disease in its initial stages before it becomes a fatal disease like covid-19. Lack of knowledge in peoples and inefficient systems used by higher authorities in that region are the main reasons to spread diseases in larger areas. But using data science and the epidemic compartment models it’s possible to control infectious diseases in its initial stages. For different diseases there are different compartment algorithms that are able to estimate the number of cases in the future. These models often use ordinary differential equations for predicting things. Using data science, we are able to find what are key factors responsible for the spreading of that particular disease.
Enhancing Security in Internet of Healthcare Application using Secure Convolutional Neural Network
Authors: Singh Sanjeev , Singh Amrik , Limkar Suresh
Published: September 2023

The ubiquity of Internet of Things (IoT) devices has completely changed the healthcare industry by presenting previously unheard-of potential for remote patient monitoring and individualized care. In this regard, we suggest a unique method that makes use of Secure Convolutional Neural Networks (SCNNs) to improve security in Internet-of-Healthcare (IoH) applications. IoT-enabled healthcare has advanced as a result of the integration of IoT technologies, giving it impressive data processing powers and large data storage capacity. This synergy has led to the development of an intelligent healthcare system that is intended to remotely monitor a patient's medical well-being via a wearable device as a result of the ongoing advancement of the Industrial Internet of Things (IIoT). This paper focuses on safeguarding user privacy and easing data analysis. Sensitive data is carefully separated from user-generated data before being gathered. Convolutional neural network (CNN) technology is used to analyse health-related data thoroughly in the cloud while scrupulously protecting the privacy of the consumers.The paper provide a secure access control module that functions using user attributes within the IoT-Healthcare system to strengthen security. This module strengthens the system's overall security and privacy by ensuring that only authorised personnel may access and interact with the sensitive health data. The IoT-enabled healthcare system gets the capacity to offer seamless remote monitoring while ensuring the confidentiality and integrity of user information thanks to this integrated architecture.
Design and Implementation of Modified Zeta Converter for Solar Water Pumping Application
Authors: Kumar P K Vineeth , J J Jijesh , C Niranjana , Manasa B Lakshmi , M Divakara , D Manju
Published: September 2023

The linear increase in the growth of the population demands a requisite for energy resources. Knowing the loathsome truth that non-renewable sources will ultimately exhaust, the significance of renewable sources cannot be undervalued. Considering various factors, many work areas are reliant upon fossil fuels for the generation of electricity. The use of fossil fuels will increase the quality of power production but will drain one day, and industries must change to renewable sources. The earliest system that strikes a chord with regard to renewable energy is the photovoltaic (PV) energy system. In this specific circumstance, interest in solar systems is expanding step by step, and its installations are becoming broad. The implementation of the solar water pumping method used for irrigation purposes using a Zeta converter was best suited for small and minor farmers, but still, the efficiency of the system can be upgraded with the use of filters. The vantage of the ZETA converter has less result voltage ripple and smooth water pumping application. The PV-based system has reached the point where it is used in Electric vehicles by enhancing the standard operating condition of the converter under the steady and dynamic behavior of a PV system. Eventually, it can be worked considerably under minimum solar irradiance. Maximum power point tracking (MPPT) of the signal had dominant performance in a zeta converter circuit while sign levels ripple current, and voltage on the output side was compact.
Classification of Chest X-ray Images using CNN for Medical Decision Support System
Authors: Garud Pritam , Patel Gargi , Kadlag Nilkanth , Bhandari Sheetal , Patil Rachana Yogesh
Published: September 2023

X-rays are a crucial tool used by healthcare professionals to diagnose a range of medical conditions. However, it is important to keep in mind that a timely and accurate diagnosis is crucial for effective patient management and treatment. While chest X-rays can provide highly precise anatomical data, manual interpretation of the images can be time-consuming and prone to errors, which can lead to delays or incorrect diagnoses. To address these issues, healthcare systems have taken steps to improve diagnostic imaging services following the impact of the COVID-19 pandemic. While deep learning-based automated systems for classifying chest X-rays have shown promise, there are still several challenges that need to be addressed before they can be widely used in clinical settings, including the lack of comprehensive and high-quality datasets. To overcome these limitations, a real-time DICOM dataset, has been converted to JPEG format to increase processing speed and improve data control. Three pre-trained models and a convolutional neural network (CNN) model with low complexity and three convolutional layers for feature extraction, along with max pooling layers and ReLU and Softmax activation functions have been implemented. With an validation accuracy of 95.05% on their CNN model using the SGD optimizer, the result has been validated using a separate, real-time unlabeled DICOM dataset of 1000 X-ray images.
Breast Cancer Analytics Classification using MEFBUD DCNN Techniques
Authors: Priyadharsini .M Suriya , Sathiaseelan J.G.R.
Published: September 2023

Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. There are numerous procedures and approaches for detecting cancer in the tissues of the breast. This work presents the image processing, segmentation, and deep learning methodologies and approaches for the diagnosis of breast cancer. This research will help people make better decisions and use trustworthy techniques to find breast cancer early enough to save a woman's life. Pre-processing, segmentation, and classification are some of this system's steps. We've included a thorough study of several techniques or processes, along with information on how they're used and how performance is measured.  The stated results lead to the conclusion that, in order to increase the chances of surviving breast cancer, it is crucial to develop new procedures or techniques for early diagnosis. For researchers to effectively diagnose breast cancer, segmentation and classification phases are also difficult. Therefore, the precise diagnosis and categorization of breast cancer still require the use of more advanced equipment and techniques.
Image Tagging using Modified Association Rule based on Semantic Neighbors
Authors: Phakatkar Anupama Ganesh , Sonkamble Balwant A.
Published: September 2023

With the rapid development of the internet, mobiles, and social image-sharing websites, a large number of images are generated daily.  The huge repository of the images poses challenges for an image retrieval system. On image-sharing social websites such as Flickr, the users can assign keywords/tags to the images which can describe the content of the images. These tags play important role in an image retrieval system. However, the user-assigned tags are highly personalized which brings many challenges for retrieval of the images.  Thus, it is necessary to suggest appropriate tags to the images.

Existing methods for tag recommendation based on nearest neighbors ignore the relationship between tags. In this paper, the method is proposed for tag recommendations for the images based on semantic neighbors using modified association rule. Given an image, the method identifies the semantic neighbors using random forest based on the weight assigned to each category. The tags associated with the semantic neighbors are used as candidate tags. The candidate tags are expanded by mining tags using modified association rules where each semantic neighbor is considered a transaction. In modified association rules, the probability of each tag is calculated using TF-IDF and confidence value.

The experimentation is done on Flickr, NUS-WIDE, and Corel-5k datasets. The result obtained using the proposed method gives better performance as compared to the existing tag recommendation methods.
Enhancing Face Recognition Performance with Multispectral Imaging and Machine Learning: Comparison from Sift and Sift-Freak Feature Extraction
Authors: Mamadou Diarra , Ayikpa Kacoutchy Jean , Ballo Abou Bakary , Diabate Nagbégna , Akoguhi N’guessan Patrice
Published: September 2023

The development of modern societies faces many security and identification challenges. To meet this expectation, computer vision offers biometric solutions. Much research in recent years has focused on face recognition. Traditional facial recognition that uses color images has had many shortcomings, such as variation in illumination, smoke, rain, disguise, face concealment, makeup, etc. Light-insensitive infrared (IR) imaging is presented as an alternative to facial recognition in the visible to overcome the shortcomings of uncontrolled environments. However, IR also has weaknesses, such as facial occlusion by glasses, variation in body temperature, perfusion, etc. This paper proposes a new facial recognition architecture that uses several classification algorithms, detectors, and feature descriptors in multispectral imaging. A combination of SIFT and FREAK, feature extraction tools, was associated with classification algorithms such as SVM, logistic regression, and Random forest to conduct this study. Several experiments were made to evaluate the performance of the proposed recognition system. The validation process of the proposed multispectral face recognition method involved several important steps. First, experiments were carried out on visible and infrared spectrum images to measure the recognition system's performance. These experiments made it possible to compare the recognition performances between these two types of images. Then, fuse visible and infrared images were used to assess multispectral facial recognition. The goal was to maximize each spectrum's advantages while minimizing their disadvantages. Metrics were evaluated to measure the accuracy of the multispectral face recognition method. The performances were compared with classical facial recognition methods, such as facial recognition based on the visible spectrum or infrared imagery alone. The results showed that the proposed multispectral facial recognition method performed better than traditional methods, reaching a facial recognition score ranging from 76% to 95% in the IRIS database.
Machine Learning based Stress Detection using Keyboard Typing Behavior
Authors: Chunawale Abhishek , Bedekar Mangesh
Published: September 2023

Emotion detection is one of those areas where technological advances have brought about significant changesin the human lifestyle. During COVID-19 pandemic, due to the work from home culture, use of computers and laptop was suddenly increased. Introduction of digital environments gave it a whole new dimension. Emotion detection is a virtual or computerized way to detect stress. People suffer from various kinds of stress in day to day activities and it is directly connected to their performance. The stress factor can be expressed through a number of ways and human behavior.  The way in which humans interact with the computer can reveal the emotional state of the user, mainly the stress. Keyboard typing behavior or characteristics can be used for stress detection. This paper focuses on understanding typing behaviour of human and indicate their stress level. Relevant features are extracted from typing behavior of a user and used for training machine learning models for detection of stress. K-Nearest Neighbor algorithm gave highest accuracy of 84.21% with dimensionality reduction approach.
Strategies and Approaches for Generating Identical Extensive XML Tree Instances
Authors: Reddy Bhargavi Peddi , Bommala Harikrishna , Bhyrapuneni Srikanth
Published: September 2023

In recent years, XML has become the de facto internet wire language. Data may be organized and given context with the use of XML. A well-organized document facilitates the transformation of raw data into actionable intelligence. In B2B1 applications, the XML data is sent and created. This implies the need for fast query processing on XML data. The processing of XML tree sample queries (XTPQ) that provide an efficient response (also known as sample matching) is a topic of active study in the XML database field.DOM (Parser) may be used to transform an XML document into a tree representation. Extensible Markup Language (XML) query languages like XPath and XQuery use tree samples (twigs) to express query results.XML query processing focuses mostly on effectively locating all instances of twig 1 samples inside an XML database. Numerous techniques for matching such tree samples have been presented in recent years. In this study, we survey recent developments in XTPQ processing. This summary will begin by introducing several algorithms for twig sample matching and then go on to provide some background on holistic techniques to process XTPQ.
Covid-19 Detection For CT-scan Images Using Transfer Learning Models
Authors: Patil Pratham , K Rajani P. , Salunkhe Karishma , Patil Hitesh , Kulkarni Rugved
Published: September 2023

COVID-19 is a respiratory illness caused by a virus called SARS-CoV-2 which affected around 455 million people around the world. CT-scan is a medical imaging technique that uses X-rays to create detailed images of the body and which can be used to detect many respiratory diseases. Transfer learning models are a type of machine learning model that are trained on a large dataset of images and which can be used for their already trained ability to extract features from image in other tasks. They can then be used to classify new images with similar features.This paper presents a study of different transfer learning models for the task of classifying chest X-ray images into three classes: COVID-19, pneumonia, and normal. The study was implemented using Python and the dataset used was the COVID-19 Chest X-ray Dataset. The train-test split used was 0.2–0.8. The parameters used to test the models were the precision, recall, accuracy, F1 score, and Matthew’s correlation score. Other than these, different optimizers were also compared such as ADAM, SGD with different learning rates of 0.01, 0.001, and 0.0001.The models used in this study are EfficientNetB0, EfficientNetB7, VGG16, and InceptionV3. Out of these models, the most effective model was the EfficientNetB0 model, which achieved an accuracy of 98.6%. This study provides valuable insights into the use of transfer learning for medical image analysis. The results suggest that transfer learning can be used to develop accurate and efficient models that can be used as a secondary option for the diagnosis of COVID-19 using chest X-ray images.
A Probabilistic Approach for Item Based Collaborative Filtering
Authors: Devi D. Ganga , Sampath S.
Published: September 2023

In this era, it is essential to know the customer’s necessity before they know it themselves. The Recommendation system is a sub-class of machine learning which deals with the user data to offer relevant content or product to the user based on their taste. This paper aims to develop an integrated recommendation system using statistical theory and methods. Therefore, the conventional Item Based Collaborative filtering integrated the probabilistic approach and the pseudo-probabilistic approach is proposed to update the k-NN approach. Here we synthesize the data using the Monte-Carlo approach with the binomial and the multinomial distribution. Then we examine the performance of the proposed methodologies on the synthetic data using the RMSE calculation.
Implementation of Super Resolution Techniques in Geospatial Satellite Imagery
Authors: Baraskar Trupti , Raman Vignesh Charan , Panchal Poojan
Published: September 2023

The potential for more precise land cover classifications and pattern analysis is provided by technological advancements and the growing accessibility of high-resolution satellite images, which might significantly improve the detection and quantification of land cover change for conservation.  A group of methods known as "super-resolution imaging" use generative modelling to increase the resolution of an imaging system. Super-Resolution Imaging, which falls under the category of sophisticated computer vision and image processing, has a variety of practical uses, including astronomical imaging, surveillance and security, medical imaging, and satellite imaging. As computer vision is where deep learning algorithms for super-resolution first appeared, they were mostly created on RGB images in 8-bit colour depth, where the sensor and camera are separated by a few meters. But no evaluation of these methods has been done.
A Framework for Credit Risk Prediction Using the Optimized-FKSVR Machine Learning Classifier
Authors: Devi Usha , Batra Neera
Published: September 2023

Transparency is influenced by several crucial factors, such as credit risk (CR) predictions, model reliability, efficient loan processing, etc. The emergence of machine learning (ML) techniques provides a promising solution to address these challenges. However, it is the responsibility of banking or nonbanking organizations to control their approach to incorporate this innovative methodology to mitigate human preferences in loan decision-making. The research article presents the Optimized-Feature based Kernel Support Vector Regression (O-FKSVR) model which is an ML-based CR analysis model in the digital banking. This proposal aims to compare several ML methods to identify a precise model for CR assessment using real credit database information. The goal is to introduce a classification model that uses a hybrid of Stochastic Gradient Descent (SGD) and firefly optimization (FFO) methods with Support Vector Regression (SVR) to predict credit risks in the form of probability, loss given, and exposure at defaults. The proposed  O-FKSVR model extracts features and predicts outcomes based on data gathered from online credit analysis. The proposed O-FKSVR model has increased the accuracy rate and resolved the existing problems. The experimental study is conducted in Python, and the results demonstrate improvements in accuracy, precision, and reduced error rates compared to previous ML methods. The proposed O-FKSVR model has achieved a maximum accuracy rate value of 0.955%, precision value of 0.96%, and recall value of 0.952%, error rate value of 4.4 when compared with the existing models such as SVR, DT, RF, and AdaBoost.
Modelling of Hybrid Meta heuristic Based Parameter Optimizers with Deep Convolutional Neural Network for Mammogram Cancer Detection
Authors: Ashwini P. , Suguna N. , Vadivelan N.
Published: September 2023

Breast cancer (BC) is the common type of cancer among females. Mortality from BC could be decreased by identifying and diagnosing it atan earlierphase. Different imaging modalities are used to detect BC, like mammography. Even withproven records as a BC screening tool, mammography istime-consuming and hasconstraints, namely lower sensitivity in women with dense breast tissue. Computer-Aided Diagnosis or Detection (CAD) system assistsaproficient radiologist to identifyBC at an earlier stage. Recently, the advancementin deep learning (DL)methodsareemployed to mammography assist radiologists to increase accuracy and efficiency. Therefore, this study presents a metaheuristic-based hyperparameter optimization with deep learning-based breast cancer detection on mammogram images (MHODL-BCDMI) technique. The presented MHODL-BCDMI technique mainly focused on the recognition and classification of breast cancer on digital mammograms. To achieve this, the MHODL-BCDMI technique employs pre-processing in two stages: Wiener Filter (WF) based noise elimination and contrast enhancement. Besides, the MHODL-BCDMI technique exploits densely connected networks (DenseNet201) model for feature extraction purposes. For BC classification and detection, a hybrid convolutional neural network with a gated recurrent unit (HCNN-GRU) model is used. Furthermore, three hyperparameter optimizers are employed namely cat swarm optimization (CSO), harmony search algorithm (HSA), and hybrid grey wolf whale optimization algorithm (HGWWOA). Finally, the U2Net segmentation approach is used for the classification of benign and malignant types of cancer. The experimental analysis of the MHODL-BCDMI method is tested on a digital mammogram image dataset and the outcomes are assessed in terms of diverse metrics. The simulation results highlighted the enhanced cancer detection performance of the MHODL-BCDMI technique over other recent algorithms.
A Systematic Study of Stock Markets Using Analytical and AI Techniques
Authors: Patidar Neha , Shah Harshal
Published: September 2023

Predicting stock market patterns is seen as a crucial and highly productive activity. Therefore, if investors make wise choices, stock prices will result in significant gains. Investors face a lot of difficulty making predictions about the stock market because of the noisy and stagnating data. As a result, making accurate stock market predictions is difficult for investors who want to put their money to work for them. Predictions of the stock market are made using mathematical techniques and study aids. Out of 30 research papers advocating approaches, this study offers a thorough analysis of each, including computational methodologies, AI algorithms( machine learning and deep learning), performance evaluation parameters, and chosen publications. Research questions are used to choose studies.

As a result, these chosen studies contribute to the discovery of ML methods and their corresponding data set for predicting security markets. The majority of Artificial Neural Network and Neural Network techniques are employed for producing precise stock market forecasts. The most recent stock market-related prediction system has significant limitations despite the substantial amount of work that has gone into it. In this survey, one may infer that the stock price forecasting procedure is a comprehensive affair and it is very necessary to look more closely at the typical parameters for the stock market prediction.
En-PaFlower: An Ensemble Approach using PSO and Flower Pollination Algorithm for Cancer Diagnosis
Authors: Senapati Sudhir Kumar , Shrivastava Manish , Mahapatra Satyasundara
Published: September 2023

Machine learning now is used across many sectors and provides consistently precise predictions. The machine learning system is able to learn effectively because the training dataset contains examples of previously completed tasks. After learning how to process the necessary data, researchers have proven that machine learning algorithms can carry out the whole work autonomously. In recent years, cancer has become a major cause of the worldwide increase in mortality. Therefore, early detection of cancer improves the chance of a complete recovery, and Machine Learning (ML) plays a significant role in this perspective. Cancer diagnostic and prognosis microarray dataset is available with the biopsy dataset. Because of its importance in making diagnoses and classifying cancer diseases, the microarray data represents a massive amount. It may be challenging to do an analysis on a large number of datasets, though. As a result, feature selection is crucial, and machine learning provides classification techniques. These algorithms choose the relevant features that help build a more precise categorization model. Accurately classifying diseases is facilitated as a result, which aids in disease prevention. This work aims to synthesize existing knowledge on cancer diagnosis using machine learning techniques into a compact report.  Current research work aims to propose an ensemble-based machine learning model En-PaFlower using Particle Swarm Optimization (PSO) as the feature selection algorithm, Flower Pollination algorithm (FPA) as the optimization algorithm with the majority voting algorithm. Finally, the performance of the proposed algorithm is evaluated over three different types of cancer disease datasets with accuracy, precision, recall, specificity, and F-1 Score etc as the evaluation parameters. The empirical analysis shows that the proposed methodology shows highest accuracy as 95.65%.
Understanding First-Person and Third-Person Videos in Computer Vision
Authors: Girase Sheetal , Bedekar Mangesh
Published: September 2023

Due to advancements in technology and social media, a large amount of visual information is created. There is a lot of interesting research going on in Computer Vision that takes into consideration either visual information generated by first-person (egocentric) or third-person(exocentric) cameras. Video data generated by YouTubers, Surveillance cameras, and Drones which is referred to as third-person or exocentric video data. Whereas first-person or egocentric is the one which is generated by GoPro cameras and Google Glass. Exocentric view capture wide and global views whereas egocentric view capture activities an actor is involved in w.r.t. objects. These two perspectives seem to be independent yet related. In Computer Vision, these two perspectives have been studied by various domains like Activity Recognition, Object Detection, Action Recognition, and Summarization independently. Their relationship and comparison are less discussed in the literature. This paper tries to bridge this gap by presenting a systematic study of first-person and third-person videos. Further, we implemented an algorithm to classify videos as first-person/third-person with the validation accuracy of 88.4% and an F1-score of 86.10% using the Charades dataset..
Modelling Accessibility based on Urbanization using Artificial Neural Networks (ANN)
Authors: Kumar T. Pavan , Lakshmana Rao K. M.
Published: September 2023

The present study involves modelling the accessibility index with respect to the traffic volume, Right of Way width and Population density. It also involves the collection of number of different types of opportunities like schools, hospitals, ATMs, bus-stops and parks to determine the accessibility index. Two different methods are used in the study such as Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) to develop models in order to predict the accessibility index. Based on the R2 value obtained in the models, it is observed that ANN has better prediction capability than MLR model. The study acts as a guide to the urban transportation planners to understand the change in accessibility index when there is a change in urbanization variables.
A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications
Authors: V. Abilasha , A. Karthikeyan
Published: September 2023

The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime.
Requirement based Test Case Prioritization for System Testing
Authors: Kumar Harish , Vedpal Vedpal , Kumar Umesh , Chauhan Naresh
Published: September 2023

System Testing encompasses a large number of test cases, which may not be able to get executed due to constrained time, budget and limitation of the resources. Therefore, the test cases must be prioritized in some order such that the critical and most required functionality can be tested early. In this paper, a hierarchical approach for system test case prioritization based on requirements has been proposed that maps requirements on the system test cases. This approach analyzes and assigns value to each requirement based on a comprehensive set of twelve factors thereby prioritizing the requirements. Further, the prioritized requirement is mapped on the highly relevant module and then prioritized set of test cases. To analyze the effectiveness of this approach, a case study of income tax calculator software [1] has been taken. The existing as well as the proposed approach were applied and analyzed on this software. The results show the efficacy of the proposed approach in terms of fault detection and severity early.
Brain Tumor Prediction using Adaptive Connected Component based GLCM and SVM Method
Authors: Reddy A. Srinivasa , Raja R. , Satheesh N. , Muruganantham R.
Published: September 2023

A crucial stage in the diagnosis of brain disorders using magnetic resonance images is feature extraction. The feature extraction procedure is used to reduce the amount of the picture data by removing the necessary information from the segmented image. The segmentation strategy and features that are extracted have an impact on the classification algorithm's dependability. With the aid of a Support Vector Machine, texture features are retrieved in this study using a Grey Level Co-occurrence Matrix, while form features are extracted using connected areas. Images of benign tumours, malignant tumours, and a normal brain all exhibit distinctive features. The classification of MR images can benefit from this change in feature values. A SVM classifier will receive the features that were thusly obtained for training and testing and further able to classify the abnormalities in brain images.