bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Arsenate Removal Efficiency Using Sewater Bittern-Derived MgO Nanoparticles/PVDF-HFP Electrospun Nanofibre Composites
Authors: Asnan Rinovian Asnan Rinovian , Muhamad Nasir Muhamad Nasir , Muhammad Ali Zulfikar Muhammad Ali Zulfikar , Purwajanti Swasmi , Nugraha Nugraha , Handayani Nurrahmi , Suradharmika I Gusti Agung , Fitri Dara Fitri Dara
Published: October 2023

MgO nanoparticles (MgO NPs) incorporated PVDF-HFP nanofibers have been synthesized using the electrospinning method to remove arsenic from polluted water. MgO nanoparticles were synthesized from seawater bitterns and used as magnesium precursors. The synthesized materials were characterized using various techniques, and their adsorption capacities were evaluated against arsenic under different conditions. The results showed that the maximum adsorption for As(V) adsorption was 41.47 mg g-1 for PVDF-HFP/MgO 30% (w/w), which equals 179.69 mg g-1 based on the weight of bare MgO NPs and achieved at pH 11, a contact time of 420 minutes, and an adsorbent weight of 0.0125 g. Incorporating MgO NPs into the nanofiber matrix can enhance its stability, further increase the adsorption capacity. This study demonstrates the potential of using PVDF-HFP/MgO nanofiber composites to treat arsenic-containing wastewater and further provide commercial benefits for seawater bitterns by serving as a precursor for producing functional nanomaterials.
Forensic Dental Age Estimation Using Modified Deep Learning Neural Network
Authors: ATAŞ İsa , ÖZDEMİR Cüneyt , ATAŞ Musa , DOĞAN Yahya
Published: October 2023

Dental age is one of the most reliable methods to identify an individual’s age. By using dental panoramic radiography (DPR) images, physicians and pathologists in forensic sciences try to establish the chronological age of individuals with no valid legal records or registered patients. The current methods in practice demand intensive labor, time, and qualified experts. The development of deep learning algorithms in the field of medical image processing has improved the sensitivity of predicting truth values while reducing the processing speed of imaging time. This study proposed an automated approach to estimate the forensic ages of individuals ranging in age from 8 to 68 using 1332 DPR images. Initially, experimental analyses were performed with the transfer learning-based models, including InceptionV3, DenseNet201, EfficientNetB4, MobileNetV2, VGG16, and ResNet50V2; and accordingly, the best-performing model, InceptionV3, was modified, and a new neural network model was developed. Reducing the number of the parameters already available in the developed model architecture resulted in a faster and more accurate dental age estimation. The performance metrics of the results attained were as follows: mean absolute error (MAE) was 3.13, root mean square error (RMSE) was 4.77, and correlation coefficient R2 was 87%. It is conceivable to propose the new model as potentially dependable and practical ancillary equipment in forensic sciences and dental medicine.
Early Breast Cancer Prediction using Machine Learning and Deep Learning Techniques
Authors: Patil Swati B. , Patil Ritesh V. , Mahalle Parikshit N.
Published: October 2023

Breast Cancer (BC) is a considered as one of the utmost lethal diseases across the globe that has a very high morbidity and mortality rate. Accurate and early prediction along with diagnosis is one of the most crucial characteristics for the treatment of Breast Cancer. Doctors can have an edge over Breast cancer if they are able to predict it in its early stages using deep learning and machine learning techniques. This paper proposed consists of comparison between the and accuracy of various machine learning models like Support vector machine (SVM), K-Nearest Neighbours (KNN), Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), XGB Classifier and deep learning model of Artificial neural networks (ANN) for the precise detection of breast cancer.

The most crucial properties from the database have been chosen using one feature-selection technique. Correlation is also used to choose the most correlated features from the data. Implementing the ANN model consists of one input layer, two hidden layers, and one output layer. All Machine Learning models and ANN model are then applied to selected features. The results demonstrated that the SVM classifier achieved the highest performance with an accuracy of ~98.24%.
Numerical Simulation and Design of Ensemble Learning Based Improved Software Development Effort Estimation System
Authors: Gora Rajani Kumari , Sinha Ripu Ranjan
Published: October 2023

This research paper proposes a novel approach to improving software development effort estimation by integrating ensemble learning algorithms with numerical simulation techniques. The objective of this study is to design an ensemble learning-based software development effort estimation system that leverages the strengths of multiple algorithms to enhance accuracy and reliability. The proposed system combines the power of ensemble learning, which involves aggregating predictions from multiple models, with numerical simulation techniques that enable the modelling and analysis of complex software development processes. A diverse set of software development projects is collected, encompassing various domains, sizes, and complexities. Ensemble learning algorithms such as Random Forest, Gradient Boosting, Bagging, and AdaBoost are selected for their ability to capture different aspects of the data and produce robust predictions. The proposed system architecture is presented, illustrating the flow of data and components. A model training and evaluation pipeline is developed, enabling the integration of ensemble learning and numerical simulation modules. The system combines the predictions generated by the ensemble models with the simulation results to produce more accurate and reliable effort estimates. The experimental setup involves a comprehensive evaluation of the proposed system. A real-world dataset comprising historical project data is utilized, and various performance metrics, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), are employed to assess the effectiveness of the system. The results and analysis demonstrate that the ensemble learning-based effort estimation system outperforms traditional techniques, showcasing its potential to enhance project planning and resource allocation.
A Hybrid Approach using CNN and DQN Technique for Diagnosis Pneumonia in Chest X-Ray Images
Authors: Sabeena Jasmine , Kumar N. Chaitanya
Published: October 2023

Pneumonia poses a significant risk to life and well-being respiratory infection that requires accurate and timely diagnosis for effective treatment. In this research investigation, it proposes a hybrid approach for detecting pneumonia diagnosis in chest X-ray images by combining machine learning techniques with convolutional neural networks (CNN), and deep Q-network (DQN) reinforcement learning. The suggested approach holds promising prospects for enhancing the efficacy of pneumonia diagnosis. Especially in resource-limited settings where access to radiologists or specialized equipment is limited. The proposed hybrid approach involves multiple stages. Initially, an extensive collection  dataset of chest X-ray images, comprising both normal and pneumonia cases, is collected. The CNN model can be integrated into clinical decision support systems to provide accurate diagnosis of infection for pneumonia. Furthermore, the use of the rainbow method can be extended to other clinical imaging tasks to enhance  deep learning models performance Additionally, it  demonstrates that the use of the rainbow method improves the performance of the CNN, leading to a higher accuracy. We have introduced a novel hybrid deep learning framework called LIP-CDF Algorithm, which combines algorithms of Convolutional Neural Networks (CNN) and Deep Q-Network (DQN) techniques.  LIP-CDF (Lung Infection Prediction using CNN-DQN Fusion) Algorithm is a computational approach designed for the accurate and efficient lung infection prediction using images from chest X-rays. The implementation of this framework utilized popular tools such as Jupyter Notebook, TensorFlow, and Keras. To assess the effectiveness of our model, the NIH chest X-beam picture dataset gained from the Kaggle archive. To evaluate the effectiveness of our proposed approach, we conduct experiments on the publicly available Chest X-ray14 dataset. The results show that our approach achieves a high accuracy of 94.8% in detecting pneumonia cases.

The purpose of our framework is to streamline the detection of lung diseases, making it easier for both medical experts and doctors. By harnessing the power of CNN and DQN, our approach offers a simplified yet accurate method for identifying lung diseases from chest X-ray images. This advancement in deep learning technology has the potential to greatly assist healthcare professionals in diagnosing and treating patients effectively
Triple Layered Security for Data Hiding Using Steganography and Visual Cryptography
Authors: Bhawna Bhawna , Malik Sanjay Kumar
Published: October 2023

The proposed system makes use of steganographic method along with visual cryptography for hiding data in images and to provide better security.  Even though extensive research work has been done previously, but most of the research work gives no robust security for the image that has been encrypted. The method proposed in this paper is capable of hiding some secret message in least significant bits of the original image, thereby hiding the secret message in way to make its detection difficult. The secret image is then under went to visual cryptography algorithm. Thereby, creating shares of the secret image, which are noise like structures. Further, the method proposes to hide these shares in least significant bits of different images so as to create three levels of security for the message. The main objective of the proposed method is to combine the use of data hiding techniques, steganography and visual cryptography algorithms for designing a secure algorithm so that security, reliability and efficiency of secret message can be improved. The framework is implemented in matlab.
Securing the Biometric through ECG using Machine Learning Techniques
Authors: Gupta Praveen Kumar , Parihar Subhash Singh , Rastogi Ritesh , Vidushi Vidushi , Salgotra Komal
Published: October 2023

In the current era, biometrics is widely used for maintaining the security. To extract the information from the biomedical signals, biomedical signal processing is needed. One of the significant tools used for the diagnostic is electrocardiogram (ECG). The main reason behind this is the certain uniqueness in the ECG signals of the individual.  In this paper, the focus will be on distinguishing the individual on the basis of ECG signals using feature extraction approaches and the machine learning algorithms. Other than preprocessing approach, the discrete cosine transform is applied to perform the extraction. The classification between the signals of the individuals is carried out using the Support Vector Machine and K-Nearest Neighbor machine learning techniques.  The classification accuracy achieved through SVM is 87% and K-NN has achieved a classification accuracy of 96.6% with k=3. The work has shown how machine learning can be used to classify the ECG signal.
Detection of 3D Object in Point Cloud: Cloud Semantic Segmentation in Lane Marking
Authors: Arya Hemlata , Saxena Parul , Jha Jaimala
Published: October 2023

Managing a city efficiently and effectively is more important than ever as growing population and economic strain put a strain on infrastructure like transportation and public services like keeping urban green areas clean and maintained. For effective administration, knowledge of the urban setting is essential. Both portable and stationary laser scanners generate 3D point clouds that accurately depict the environment. These data points may be used to infer the state of the roads, buildings, trees, and other important elements involved in this decision-making process. Perhaps they would support "smart" or "smarter" cities in general. Unfortunately, the point clouds do not immediately supply this sort of data. It must be eliminated. This extraction is done either by human specialists or by sophisticated computer programmes that can identify objects. Because the point clouds might represent such large locations, relying on specialists to identify the things may be an unproductive use of time (streets or even whole cities). Automatic or nearly automatic discovery and recognition of essential objects is now possible with the help of object identification software. In this research, In this paper, we describe a unique approach to semantic segmentation of point clouds, based on the usage of contextual point representations to take use of both local and global features within the point cloud. We improve the accuracy of the point's representation by performing a single innovative gated fusion on the point and its neighbours, which incorporates the knowledge from both sets of data and enhances the representation of the point. Following this, we offer a new graph point net module that further develops the improved representation by composing and updating each point's representation inside the local point cloud structure using the graph attention block in real time. Finally, we make advantage of the global structure of the point cloud by using spatial- and channel-wise attention techniques to construct the ensuing semantic label for each point.
Health-Seeking Behaviour and the use of Artificial Intelligence-based Healthcare Chatbots among Indian Patients
Authors: Rangasamy Sangeetha , Nagarathinam Aishwarya , Chellasamy Aarthy , N. Elangovan
Published: October 2023

Artificial Intelligence (AI) based healthcare chatbots can scale up healthcare services in terms of diagnosis and treatment. However, the use of such chatbots may differ among the Indian population. This study investigates the influence of health-seeking behaviour and the availability of traditional, complementary and alternative medicine systems on healthcare chatbots. A quantitative study using a survey technique collects data from the Indian population. Items measuring the awareness of chatbot’s attributes and services, trust in the chatbots, health-seeking behaviour, traditional, complementary and alternative medicine, and use of chatbots are adapted from previous scales. A convenience sample is used to collect the data from the urban population. 397 samples were fetched, and statistical analysis was done. Awareness of the chatbot’s attributes and services impacted the trust in the chatbots. Health-seeking behaviour positively impacted the use of chatbots and enhanced the impact of trust of a chatbot on the use of a chatbot. Traditional, complementary and alternative medicine was not included in the chatbot, which negatively impacted the use of chatbots. At the same time, it dampened the impact of trust in chatbots on the use of chatbots. The study was limited to the urban population and a convenience sampling because of the need to use the Internet and a smart device for accessing the chatbots. The results of the study need to be used cautiously. The results can be inferred from the relationships’ existence rather than the impact’s magnitude. The study’s outcome encourages the availability of chatbots due to the health-seeking behaviour of the Indian urban population. The study also highlights the need for creating intelligent agents with knowledge of Traditional, complementary and alternative medicine. The study contributes to the knowledge of using chatbots in the Indian context. When earlier studies focus mainly on the chatbot features or user characteristics in the intention studies, this study looks at the healthcare system and the services unique to India.
Detection and Predicting Air Pollution Level in a Specific City using Deep Learning
Authors: Nandhan S. Harish , Zean J. Remoon , L. Akshara Sri , Subedha V. , P. C. Giftson
Published: October 2023

Air pollution affects millions of people worldwide, making it a growing issue. Deep learning can identify and forecast metropolitan air pollution. Deep learning needs a massive dataset of air quality measurements and meteorological factors to predict city air pollution levels. Government monitoring stations and citizen scientific programs collect this data. Once we have our dataset, we can apply deep learning to develop a model that predicts air pollution levels. Temperature, humidity, wind speed, and air quality data will be used to predict future air pollution levels. Predicting air pollution using the LSTM network is popular. This neural network works well with air quality time-series data. The LSTM network's long-term data learning is essential for accurate air pollution predictions. We would pre-process our data to prepare it for an LSTM network to predict air pollution. Scaling, splitting, and encoding data may be needed. Train the LSTM network using backpropagation and gradient descent on our dataset. Adjusting the network's weights and biases would lessen the air pollution gap. After training, the network can predict city air quality. Inputting current meteorological and environmental factors may help accomplish this aim and deliver timely predictions. Deep learning can detect and predict urban air pollution. LSTM neural network algorithms may accurately forecast complex air quality data patterns, providing vital information about our planet's health.
Machine Learning Algorithm to Identify Hesitancy Towards Covid-19 Vaccination Among the Rural Citizens Under E-Governance Initiatives
Authors: Bhuvana M. , Priya K. , V Suresh. , Palukiri Jyothi , B Premkumar.
Published: October 2023

The Indian Government has taken massive initiatives to control the pandemic virus of COVID-19. Recently, the Government has decided to begin the process of a large-scale vaccination programme to create an end to the crisis due to COVID-19. However, vaccination is found to be the major solution as suggested by various health care experts to stop the widespread of this deadly virus, hesitancy towards getting vaccinated is found to be more in the rural villages of India. The process of getting vaccinated was not made compulsory by the government, but still there exists skepticism in minds of rural citizens towards COVID-19 vaccination. Text Analytics has been implemented to identify the exact emotions among rural citizens towards COVID Vaccinations. Hence the present research study has identified the major reasons for hesitancy towards getting vaccinated for COVID-19 using machine learning techniques. The researchers of the current study have also measured the emotions and signs of sentiments of the rural citizens on COVID-19 vaccination.
Job Recommendation System Using Deep Reinforcement Learning (DRL)
Authors: Mandalapu Srinivasa Rao , Narayanan B. , Putheti Sudhakar
Published: October 2023

The rapid growth of online job portals and the increasing volume of job listings have made it challenging for job seekers to efficiently navigate through the vast number of available opportunities. Job recommendation systems play a crucial role in assisting users in finding relevant job opportunities based on their skills, preferences, and past experiences. This research paper proposes a job recommendation system that leverages deep learning techniques to enhance the accuracy and effectiveness of job recommendations. The system utilizes advanced algorithms to analyses user profiles, job descriptions, and historical data to generate personalized job recommendations. Experimental evaluations demonstrate the superiority of the proposed system compared to traditional recommendation methods, thereby improving the job search process for both job seekers and employers. This paper provides Job recommendation system using Deep Reinforcement Learning (DRL).
Automated Identification and Localization of Brain Tumor in MRI Using U-Net Segmentation and CNN-LSTM Classification
Authors: T S Chandrakantha , Jagadale Basavaraj N , T E Abhisheka , Farhan Alnaggar Omar Abdullah Murshed
Published: October 2023

Nowadays, the use of computers to evaluate medical images automatically is critical part of the life. Today's treatment method relies heavily on early diagnosis and accurate disease identification, which were formerly difficult for medical research to achieve. Brain Magnetic Resonance Imaging (MRI) is essential to the detection and treatment of brain tumor (BT). Tumor of the brain are the result of brain cell division that has gone awry or is otherwise out of control. The manual MRI segmentation of BT is a difficult and time-consuming process. The most critical factor in the effective treatment and identification of BT is the ability to accurately locate the tumor. The detection of BT is regarded as a difficult task in medical image processing. For analysing and interpreting MRI, there are semi-automatic and fully automated systems that require large-scale professional input and evaluation, with varying degrees of effectiveness. Automated identification and extraction of the tumor's localization from brain MRI will be proposed in this paper. To achieve this goal, the data collected from Kaggle and the collected data are processed. Then the U-Net is employed to segment the tumor region from the MRI. Next, the MRI is classified using DL models like Convolutional Neural Network (CNN), and the hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM). Both process segmentation and classification are evaluated using the metrics. From the evaluation, it is identified that CNN-LSTM outperforms the CNN model.
Digital Twins for Lithium-Ion Battery Health Monitoring with Linked Clustering Model using VGG 16 for Enhanced Security Levels
Authors: Padmapriya Valluri. , Srivenkatesh Muktevi
Published: October 2023

Digital Twin (DT) has only been widely used since the   early 2000s. The concept of DT refers to the act of creating a  computerized replica of a physical item or physical process. There is   the physical world, the cyber world, a bridge between them, and a portal from the cyber world to the physical world. The goal of DT is   to create an accurate digital replica of a previously existent physical object by combining AI, IoT, deep learning, and data analytics. Using   the virtual copy in real time, DTs attempt to describe the actions of the physical object. Battery based DT's viability as a solution to the   industry's growing problems of degradation evaluation, usage  optimization, manufacturing irregularities, and possible second-life  applications, among others, are of fundamental importance. Through       the integration of real-time checking and DT elaboration, data can be   collected that could be used to determine which sensors/data used in a batteries to analyze their performance. This research proposes a          Linked Clustering Model using VGG 16 for Lithium-ion batteries   health condition monitoring (LCM-VGG-Li-ion-BHM). This work           explored the use of deep learning to extract battery information by           selecting the most important features gathered from the sensors. Data           from a digital twin analyzed using deep learning allowed us to         anticipate both typical and abnormal conditions, as well as those that   required closer attention. The proposed model when contrasted with            the existing models performs better in health condition monitoring.
An Internet of Things based Solar Power Monitoring System using Node MCU
Authors: Thopate Kaushalya , Shilaskar Swati , Bhatlawande Shripad
Published: October 2023

Solar power systems are increasingly being adopted as a renewable energy solution worldwide. However, effective monitoring and management of these systems are crucial for optimizing their performance and ensuring their reliable operation. In this research paper, we propose a solar power monitoring system using NodeMCU, an open-source IoT platform. Our system collects, processes, and visualizes real-time data from solar panels, batteries, and other qsystem components, providing comprehensive monitoring capabilities for solar power system owners. The system is designed to be cost-effective and scalable, making it suitable for both residential and commercial applications. Through our research, we demonstrate the feasibility and effectiveness of our solar power monitoring system using NodeMCU. The findings contribute to the field of renewable energy management by offering an innovative approach to monitor and optimize solar power systems, promoting the wider adoption of renewable energy sources for a sustainable future.
Secure and Transparent Supply Chain Management using Blockchain and IoT
Authors: Farooqui Yassir , Parikh Swpanil M.
Published: October 2023

Blockchain technology has emerged as a disruptive force across various industries, and its integration with the Internet of Things (IoT) has unlocked new avenues for supply chain management. The conventional supply chain systems often encounter challenges related to privacy, security, and data integrity. In contrast, blockchain's decentralized and tamper-proof nature ensures a secure, auditable, and transparent record of product movement within the supply chain. By leveraging the immutable properties of blockchain, the system enhances product traceability, authenticity, and accountability while significantly reducing operational costs. IoT devices are vulnerable to attack as due to low processing power, storage limitations etc.  Blockchain integrated with IoT provides a solution faced by the several industries. Blockchains and smart contracts are technology that has gained massive attention. The integration of blockchain addresses these shortcomings by providing robust data security and integrity, minimizing the risk of unauthorized access or alteration. This paper presents a system that helps the industrialist to have an access to agricultural data and supply of crops data to farmer. As industries continue to embrace digitization and connectivity, the presented system offers a significant step towards a more streamlined and secure future for agricultural information sharing. This system will be effective for the supply chain management for the trusted delivery.
Handwritten Devanagari Text Recognition using Single Classifier Approach with VSPCA Scheme
Authors: More Vijay , Yawalkar Prashant , Kharat Madan
Published: October 2023

In this research paper we used individual classifier approach for Handwritten Devanagari text recognition. We experimented different categorical classifiers namely   Random Forest Classifier (RFC), Support Vector Machine (SVM), K Nearest Neighbor Classifier (KNN), Logistic Regression Classifier (LogRegr), Decision Tree Classifier (DTree). Seven different feature sets are used namely Eccentricity, Euler Number, Horizontal Histogram, Vertical Histogram, HOG Features, LBP Features, and Statistical Features. The experimentation is carried out on 9434 different characters whose features are extracted from 220 handwritten image documents from PHDIndic_11 dataset. We deduced and implemented a unique scheme namely VSPCA scheme. VSPCA is Vectorization, Scaling, and Principal Component Analysis carried out on all feature sets before being given for model training. We obtained varied accuracies using all these five classifiers on all these six feature sets in which 99.52% highest accuracy is observed.
A Real Time Employee Attendance Monitoring System using ANN
Authors: Jain Jaishree , Chauhan Jatin , Sharma Anushka , Sahu Shashank , Rani Ankita
Published: October 2023

Face recognition refers to the technology that examines and contrasts a person's face characteristics to recognise or verify their identity. Recently, this technology has drawn a lot of attention due to the potential uses it may have in security, marketing, and law enforcement. Face recognition involves studying a picture or video of a person's face to identify features like the space between their eyes, the contour of their nose, and the curve of their mouth. The person's identity is then established or verified by comparing these characteristics to a database of previously saved pictures. A series of techniques called facial recognition algorithms are used to identify and authenticate persons based on the features of their faces. These algorithms compare a person's facial attributes to those in a database of recognised faces by looking at things like the shape of their face, the distance between their eyes, and other distinctive facial features. There are many different types of face recognition algorithms, including geometric-based algorithms, appearance-based algorithms, and hybrid algorithms that combine both approaches. Geometric-based algorithms employ the geometry of face traits to identify and validate people, while appearance-based algorithms use image processing techniques to compare the patterns and textures of facial features. Recent advances in deep learning have significantly improved the accuracy of facial recognition algorithms. Artificial Neural Network (ANN) has shown to be highly effective and have been used in a range of applications, including mobile devices, security, and surveillance. Face recognition algorithms provide advantages, but there are also moral dilemmas with regard to its application, such as potential biases and privacy difficulties. As technology advances, it is imperative to address these problems and ensure that face recognition algorithms are used ethically and responsibly.
An Automatic Detection of Brain Tumor using CNN & VGG19
Authors: Mallick Shweta , Mishra S. P
Published: October 2023

According to the 2019 cancer statistics by WHO, brain tumors are considered the main cause of mortality related to cancer throughout the world and are known as one of the most common forms of cancer both in children as well as adults. Among the most common brain tumors, we have those that begin and tend to remain in the brain, which as meningioma with 34% of presence, another type of tumor is called glioma, arising from the surrounding tissue in the brain, it is part of 30% of all tumors in the brain, however, this glioma represents 80% of malignant tumors, making it the most common tumor common that causes death. However, this scheme depicts how convolutional neural networks using VGG19 model can provide an effective mechanism to detect brain tumors at an early stage using MRI images and can save the lives of mankind. Consequently, this research classified glioma brain tumor images using VGG-19 with HE preprocessing data. The model was tested to get a comparison of accuracy, precision, recall, and f1-score of the two test data, namely the original data and HE data. Based on the results of model testing, we can see in table2  that the original data produced the highest values of accuracy, precision, recall, and F1-score, with values of 97% accuracy, 100% precision, 97% recall, and 98% f1 score. While data using HE preprocessing has an accuracy value of 92%, precision of 100%, recall of 92%, and f1 score of 96%t..
Emotional Tendency Analysis of Twitter Data Streams
Authors: Ranganathan Gunasundari , Ganesan Clara Barathi Priyadharshini , Chellamuthu Balakumar
Published: October 2023

The web now seems to be an alive and dynamic arena in which billions of people across the globe connect, share, publish, and engage in a broad range of everyday activities. Using social media, individuals may connect and communicate with each other at any time and from any location. More than 500 million individuals across the globe post their thoughts and opinions on the internet every day. There is a huge amount of information created from a variety of social media platforms in a variety of formats and languages throughout the globe. Individuals define emotions as powerful feelings directed toward something or someone as a result of internal or external events that have a personal meaning. Emotional recognition in text has several applications in human-computer interface and natural language processing (NLP). Emotion classification has previously been studied using bag-of words classifiers or deep learning methods on static Twitter data. For real-time textual emotion identification, the proposed model combines a mix of keyword-based and learning-based models, as well as a real-time Emotional Tendency Analysis
Web Social Media Privacy Preferences and Perception
Authors: Alshehri Aziz , Alamari2 Jebreel
Published: October 2023

The proliferation of social media websites has led to concerns over privacy breaches, as these sites have access to users' sensitive and personal data. This study sought to investigate users' perceptions and concerns for social media websites, with the aim of developing a system that meets their requirements. To achieve this, a questionnaire was designed for privacy permissions on eight popular social media websites, and 425 completed answers were analyzed. The results revealed that users' concerns were diverse and differed across different social media platforms. Gender, age, education level, and IT proficiency were found to be weakly correlated with privacy concerns. Women expressed greater concerns than men, particularly for Twitter and Snapchat, while older users expressed greater levels of concern for Snapchat and Instagram. As education levels increased, users tended to express greater levels of concern, especially on WhatsApp and Snapchat.

Furthermore, this study identified four hierarchical clusters of users based on their preferences and concerns regarding permission privacy for social media websites. The results revealed that the majority of participants (214 users) were highly concerned about privacy on social media, indicating that they were aware of the potential risks associated with sharing personal information online which represents the third cluster. The first and fourth clusters were the most unconcerned groups regarding permission privacy, consisting of a small number of users. The second cluster, comprising 124 participants, had an average score of 1.6, indicating that they were the second most concerned about privacy. Overall, the findings of this study could be useful for social media platforms in developing privacy policies and settings that align with users' concerns and preferences.
A Probabilistic Adaptive Cerebral Cortex Segmentation Algorithm for Magnetic Resonance Human Head Scan Images
Authors: Rajangam Sivanesan , Palanisamy Kalavathi
Published: October 2023

The total efficiency of Magnetic Resonance Imaging (MRI) results in the need for human involvement in order to appropriately detect information contained in the image. Currently, there has been a surge in interest in automated algorithms that can more precisely divide medical image structures into substructures than prior attempts. Instant segregation of cerebral cortex width from MRI scanned images is difficult due to noise, Intensity Non-Uniformity (INU), Partial Volume Effects (PVE), MRI's low resolution, and the very complicated architecture of the cortical folds. In this paper, a Probabilistic Adaptive Cerebral Cortex Segmentation (PACCS) approach is proposed for segmenting brain areas of T1 weighted MRI of human head images. Skull Stripping (SS), Brain Hemisphere Segmentation (BHS) and CCS are the three primary processes in the suggested technique. In step 1, Non-Brain Cells (NBC) is eliminated by a Contour-Based Two-Stage Brain Extraction Method (CTS-BEM). Step 2 details a basic BHS technique for Curve Fitting (CF) detection in MRI human head images. The left and right hemispheres are divided using the discovered Mid-Sagittal Plane (MSP). At last, to enhance a probabilistic CCS structure with adjustments such as prior facts change to remove segmentation bias; the creation of express direct extent training; and a segmentation version based on a regionally various Gaussian Mixture Model- Hidden Markov Random Field – Expectation Maximization (GMM-HMRF-EM). The underlying partial extent categorization and its interplay with found image intensities are represented as a spatially correlated HMRF within the GMM-HMRF-EM method. The proposed GMM-HMRF method estimates HMRF parameters using the EM technique. Finally, the outcomes of segmentation are evaluated in terms of precision, recall, specificity, Jaccard Similarity (JS), and Dice Similarity (DS). The proposed method works better and more consistently than the present locally Varying MRF (LV-MRF), according to the experimental findings obtained by using the suggested GMM-HMRF-EM methodology to 18 individuals' brain images.
A Systematic and Comparative Analysis of Semantic Search Algorithms
Authors: Shelke Priya , Shewale Chaitali , Mirajkar Riddhi , Dedgoankar Suruchi , Wawage Pawan , Pawar Riddhi
Published: October 2023

Users often struggle to discover the information they need online because of the massive volume of data that is readily available as well as being generated every day in the today’s digital age. Traditional keyword-based search engines may not be able to handle complex queries, which could result in irrelevant or insufficient search results. This issue can be solved by semantic search, which utilises machine learning and natural language processing to interpret the meaning and context of a user's query. In this paper we focus on analyzing the BM-25 algorithm, Mean of Word Vectors approach, Universal Sentence Encoder model, and Sentence-BERT model on the CISI Dataset for Semantic Search Task. The results indicate that, the Finetuned SBERT model performs the best.
Slime Mold Optimization with Relational Graph Convolutional Network for Big Data Classification on Apache Spark Environment
Authors: Manivannan K. , Suresh T.
Published: October 2023

Lately, Big Data (BD) classification has become an active research area in different fields namely finance, healthcare, e-commerce, and so on. Feature Selection (FS) is a crucial task for text classification challenges. Text FS aims to characterize documents using the most relevant feature. This method might reduce the dataset size and maximize the efficiency of the machine learning method. Various researcher workers focus on elaborating effective FS techniques. But most of the presented techniques are assessed for smaller datasets and validated by a single machine. As textual data dimensionality becomes high, conventional FS methodologies should be parallelized and improved to manage textual big datasets. This article develops a Slime Mold Optimization based FS with Optimal Relational Graph Convolutional Network (SMOFS-ORGCN) for BD Classification in Apache Spark Environment. The presented SMOFS-ORGCN model mainly focuses on the classification of BD accurately and rapidly. To handle BD, the SMOFS-ORGCN model uses an Apache Spark environment. In the SMOFS-ORGCN model, the SMOFS technique gets executed for reducing the profanity of dimensionality and to improve classification accuracy. In this article, the RGCN technique is employed for BD classification. In addition, Grey Wolf Optimizer (GWO) technique is utilized as a hyperparameter optimizer of the RGCN technique to enhance the classification achievement. To exhibit the better achievement of the SMOFS-ORGCN technique, a far-reaching experiments were conducted. The comparison results reported enhanced outputs of the SMOFS-ORGCN technique over current models.
Configuring and Implementing IPS Solutions for IoT Devices using NST
Authors: Prasad V. Maruthi , Bharathi B.
Published: October 2023

The necessity to ensure that Internet of Things (IoT) networks are secure is one of the biggest issues that has arisen as a result of the growing demand for technology that uses the IoT. Considering how many gadgets are linked to the internet, safeguarding their networks is a growing worry. Due to the IoT's network's complexity and resource constraints, traditional intrusion detection systems encounter a number of problems. The main objectives of this project are to design, develop, and evaluate a hybrid level placement method for an IDS based on multi- agent systems, BC technology (Block-Chain), and DL algorithms (Deep Learning). The breakdown of data administration, data collection, analysis, and reaction into its component parts reveals the overall system design. The National Security Laboratory's knowledge discovery and data mining dataset is used to test the system as part of the validation procedure. These results demonstrate how deep learning algorithms are effective at identifying risks at the network and transport levels. The experiment shows that deep learning techniques function well when used to find intrusions in a network environment for the Internet of Things.
A Enhanced Approach for Identification of Tuberculosis for Chest X-Ray Image using Machine Learning
Authors: G S Monisha , Beevi L.Sherin , Kumaran J. Chenni , G S Yogashree , Shanmuganathan M.
Published: October 2023

Lungs are the primary organs affected by the infectious illness tuberculosis (TB). Mycobacterium tuberculosis, often known as Mtb, is the bacterium that causes tuberculosis. When a person speaks, spits, coughs, or breathes in, active tuberculosis can quickly spread through the air. Early TB diagnosis takes some time. Early detection of the bacilli allows for straightforward therapy. Chest X-ray images, sputum images, computer-assisted identification, feature selection, neural networks, and active contour technologies are used to diagnose human tuberculosis. Even when several approaches are used in conjunction, a more accurate early TB diagnosis can still be made. Worldwide, this leads to a large number of fatalities. An efficient technology known as the Deep Learning approach is used to diagnose tuberculosis microorganisms. Because this technology outperforms the present methods for early TB diagnosis, Despite the fact that death cannot be prevented, it is possible to lessen its effects.
Heuristic Optimization Algorithm with Ensemble Learning Model for Intelligent Intrusion Detection and Classification
Authors: Hemavathi K. , Latha R.
Published: October 2023

Intrusion Detection (ID) for network security prevents and detects malicious behaviours or unauthorized activities that occurs in the network. An ID System (IDS) refers to a safety tool that monitors events or network traffic for responding to and identifying illegal access attempts or malevolent activities. IDS had a vital role in network security by finding and alerting security teams or administrators about security breaches or potential intrusions. Machine Learning (ML) methods are utilized for ID by training methods for recognizing behaviours and patterns linked with intrusions. Deep Learning (DL) methods are implemented to learn complicated representations and patterns in network data. DL methods have witnessed promising outcomes in identifying network intrusions by automatically learning discriminatory features from raw network traffic. This article presents a new Teaching and Learning based Optimization with Ensemble Learning Model for Intelligent Intrusion Detection and Classification (TLBOEL-IDC) technique. The presented TLBOEL-IDC method mainly detects and classifies the intrusions in the network. To attain this, the TLBOEL-IDC method primarily preprocesses the input networking data. Besides, the TLBOEL-IDC technique involves the design of an ensemble classifier by the integration of three DL models called Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BLSTM). Moreover, the hyperparameter tuning of the DL models takes place using the TLBO approach that improves the overall ID outputs. The simulation assessment of the TLBOEL-IDC approach takes place on a benchmark dataset and the outputs are measured under various factors. The comparative evaluation emphasized the best accomplishment of the TLBOEL-IDC technique over other present models by means of diverse metrics.
Develop a Hybrid Classification using an Ensemble Model for Phishing Website Detection
Authors: K Subashini , V Narmatha
Published: October 2023

Solutions to threats posed by technical and social vulnerabilities must be found to secure the web interface. Social engineering attacks frequently use phishing as one of their vectors. The importance is promptly detecting phishing attacks has increased. The classifier model was constructed using publicly accessible data from trustworthy and phishing websites. A variety of methods were used to extract relevant features to build the model. Before a user experiences any harm, Machine Learning algorithms can reliably identify phishing attacks. To identify phishing attacks on the website, this study presents a novel ensemble model. In this paper, the Artificial Neural Network (ANN) and the Random Forest Classifier (RFC) are used in an ensemble method along with the Support Vector Machine (SVM). Compared to previous studies, this ensemble method more accurately and efficiently detects website phishing attacks. According to experimental findings, the proposed system detects phishing attacks 97.3% of the time.
Lagrangian Recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for Secure Image Transmission
Authors: Ansari J. Shaik Dawood , Tamilselvan P.
Published: October 2023

Present-day evolution in communication and information technology dispenses straightforward and effortless access to data, but the most noteworthy condition is the formation of secure communication. Numerous approaches were designed for safety communication. One of the crucial approaches is image steganography. Moreover, provisioning of information security services is arrived at via cryptosystems where cryptosystems make certain the secure messages transmission between the users in an untrustworthy circumstance.  The conventional method of providing encryption and signature is said to be first signing and then encryption, but both the computation and communication costs are found to be high. A certificateless signcryption mechanism is designed to transfer the medical data or images securely. This mechanism will minimize the storage and verification costs of public key certificates. The author of this article proposes a method named Lagrangian recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for transferring the medical data or images securely. In two sections the LRS-HECS method is split. They are medical image steganalysis and certificateless signcryption. First with the Chest X-Ray images obtained as input, a Codeword Correlated Lagrangian Recurrent Neural Network-based image steganography model is applied to generate steg images. Second, to transfer the medical images securely the steg images provided as input is designed a model named a Hyper Elliptic Curve-based Certificateless Signcryption. The issue of providing the integrity and validity of the transmitted medical images and receiver anonymity is addressed by the application of Hyper Elliptic Curve. Chest X-Ray pictures were used in experimental simulations, and the findings showed that the LRS-HECS approach had more advantages over existing state-of-the-art methods in terms of higher peak signal to noise ratio with data integrity and with reduced encryption time and transmission cost.