bluuebunny/crossref_metadata_2025_split huggingface dataset:
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.
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.
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..
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.
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.
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.
A Deep Learning based Model using Review Associated Feature Extraction Approach for Sentiment Analysis
Authors: Irsad Mohammad , Khare Ashish 
Published: October 2023

With the advancement of internet technologies, in the present days, online forums, social media platforms and e-commerce sites have made the product reviews process very easy. There are a lot of mobile applications, websites and forums where consumers used to share and circulate their opinions, experiences, ideas and views regarding products, brands and services. In consequence, online user reviews have become a deciding factor for many consumers prior to purchasing their selected items. The sentiment analysis is a technique to extract sentiments, feelings and insights from customer reviews and public texts. Therefore, plenty of businesses perform sentiment analysis in order to more thoroughly comprehend of their customer opinions and suggestions regarding their products and services. Furthermore, a number of scientific researchers also have a keen interest in classifying customer reviews into a set of labels employing text classification techniques. The objective of the this research work is to develop an approach to extract review associated features using Part-of-Speech (POS) tagging and design a CNN model to classify the reviews' sentiment as positive or negative. In this paper, an approach to extract review associated feature has been presented. Natural Language Processing (NLP) techniques are utilized for data preprocessing to remove uninformative data from reviews. Deep learning model CNN is used for sentiment classification and Amazon mobile reviews dataset is used for the experiment. The proposed model is experimentally evaluated and provides enhanced performance than other models also provides improved accuracy of 97.23% on Amazon mobile review dataset.
Principal Component Analysis Approach for Yield Attributing Traits in Okra (Abelmoschus esculentus L.) Genotypes
Authors: Abhilash P V , Talekar Nilesh , Delvadiya I. R. , Singh Shailesh Kumar 
Published: October 2023

The research work was investigated among 55 okra accessions in RCBD with three replications and was evaluated for seventeen phenotypic characteristics of okra principal component analysis at the Agriculture Research Farm, Lovely Professional University, Phagwara (Punjab). According to principal component analysis (PCA), six of the seventeen (PC1 to PC6) PCs had eigenvalues above 1.0 and a cumulative variance of around 75.52. PC 1 alone accounted for the highest variance of 25.38 by PC1, followed by PC 2 with 15.98%. The outcomes of this investigation might be used as a foundation for defining and implementing subsequent okra breeding initiatives. Days to the 1st flowering, days to the 50% flowering and days to the first fruit harvest appear above the average variance contribution of each PC in the screen plot. Biplot analysis of PC1 and PC2 in contribution dim.1 revealed 25% and dim.2 revealed 16%.
Heterogeneity in Physio-Chemical Traits of Milk across Buffalo Breeds in Haryana
Authors: Sharma Himani , Mishra Ratish Chandra 
Published: October 2023

This research paper presents a comparative analysis of the physio-chemical properties of milk from three different buffalo breeds in Haryana, namely Murrah, Nilli-Ravi, and Surti. The study aimed to evaluate various parameters including fat content, solid not fat (SNF), total solids, protein content, density, lactose content, titratable acidity, freezing point, pH, and specific gravity. A total of 60 buffaloes, 20 from each breed, were selected for the study, and milk samples were collected twice a day, in the morning and evening. The analysis revealed significant differences among the three buffalo breeds in terms of their milk composition. The Murrah breed exhibited the highest fat content (8.02), while the Nilli-Ravi had the lowest (8.14). For SnF values, Murrah shows highest values of 9.41 and Surti buffalo shows lowest 8.78. Similarly, variations were observed in other parameters across different breeds.
These results can assist in breed selection for improved milk production, processing, and product development. Furthermore, the data obtained can serve as a valuable resource for dairy industry stakeholders, regulatory bodies, and researchers in the field of dairy science and technology. Future studies could explore the relationship between physio-chemical properties and milk yield, as well as investigating the impact of these variations on the processing and quality of dairy products.
Association of maternal serum calcium with neonatal birth weight in term deliveries
Authors: Afroje Shamima , Nesa Kusum Fazilatun , Siddique Sharmina , Sharmin Saria , Akter Dilruba 
Published: October 2023

Background: Calcium is an essential nutrient during pregnancy that supports the growth and development of the foetus, especially because of its maternal-foetal transfer. A few previous studies reported that decreased calcium in mothers can lead to low birth weight and some other complications like pre-eclampsia, preterm delivery, and fetal low bone mineral density. We do have not enough research-based information regarding this issue. The aim of the study was to evaluate the association of maternal serum calcium with neonatal birth weight in term deliveries.
Methods: This was a cross-sectional study that was conducted in the Department of Obstetrics and Gynecology at the Institute of Child and Mother Health (ICMH), Dhaka, Bangladesh from September 2018 to August 2019. In total 98 women attending the ICMH having an uncomplicated, singleton, term pregnancy were enrolled in this study as the study subjects. A purposive sampling technique was used in sample selection. All the demographic and clinical information were recorded. All data were processed, analyzed and disseminated by using MS Office and SPSS version 22.0 program as per necessity.
Results: Among the pregnant mothers who regularly took calcium supplements, 98.6% had normal s. calcium levels, whereas 69.2% of those with irregular supplement intake had low serum calcium levels; the distinction was statistically significant (p<0.001). Of the 98 newborns, 72.4% had normal birth weight, averaging 2.81±0.37 kg, while 27.6% had Low birth weight. Maternal serum calcium levels correlated with birth weight, with 86.36% of low-birth-weight babies born to mothers with lower serum calcium levels and 84.21% of normal birth weight babies born to mothers with normal serum calcium levels. This correlation was statistically significant (p<0.001), showing a positive association (p<0.001) between maternal serum calcium level (mg/dl) and neonatal birth weight (kg).
Conclusions: There was a statistically significant association between maternal serum calcium level and neonatal birth weight in term deliveries where the birth weight of neonates reduces when maternal serum calcium level reduces.
Laparoscopically assisted vaginal hysterectomy vs non-descent vaginal hysterectomy in the benign diseases of the uterus: an interventional study
Authors: Mukherjee Shobha , Singh Parru , Bedi Shivani 
Published: October 2023

Background: Hysterectomy is most common gynaecological surgery done for various indications varying from AUB and fibroid uterus to malignancies. There are various approaches for performing hysterectomy, ranging from laparotomy and laparoscopic to vaginal hysterectomy, in both descent and non-descent cases. Incidence of hysterectomies in India is reported to be low compared to developed countries. Most common indication for hysterectomy being excessive menstrual blood loss due to hormonal reasons or fibroids (size more often not exceeding 12 weeks). Aim of study was to compare intra-op and post-op complications between non-descent vaginal hysterectomy and laparoscopic hysterectomy and establish the better method for hysterectomy in non-descent uterus.
Methods: A prospective comparative study of 104 hysterectomies was done over a period of November 2019 to October 2020, with 52 cases each in one group of non-descent vaginal hysterectomy (NDVH) and other group of total laparoscopic hysterectomy LAVH. Demographic characteristics, indications for surgery, operative time, intra-operative blood loss, post-operative analgesia requirements, post-operative hospital stay and post-operative complications were compared between both groups.
Results: The most common age in both groups was 41-50 years. Among 104 cases undergoing NDVH and LAVH the most common indication of surgery was Fibroid and DUB. The mean operative time in NDVH group was 90.54±5.89 min while it was 127.12±12.58 min in LAVH group, and the mean blood loss in NDVH group was 108.56±7.14 ml, while it was 89.23±7.37 ml in LAVH group. The intraoperative complication rate in NDVH was more compared to LAVH.
Conclusions: The present study concludes that NDVH can be safely offered to patients with benign gynaecological conditions and this scarless approach appears to be the preferred method of hysterectomy. LAVH can be offered as a synergistic surgery in cases where difficulty in operative dissection is anticipated.
Laparoscopic cholecystectomy in situs inversus with partially intrahepatic gallbladder: a case report
Authors: Baxi Kamakshi , Jha Nitin , Khurana Vibhu , Neeli Dakshayani , Dogra Loveneet 
Published: October 2023

Situs inversus totalis (SIT) is a rare condition with mirrored visceral organs. Biliary colic has delayed diagnosis in this cohort of patients. Laparoscopic cholecystectomy (LC) in such condition is a technically challenging procedure and requires preoperative imaging. We report a case of a 37 years old lady with pain in left upper abdomen for last 1 year. Upon evaluation, she was diagnosed with cholelithiasis with SIT. MRCP was done to delineate the anatomy. The patient underwent LC in a split-leg position with mirrored port position by a right-handed surgeon. The duration of surgery was 90 minutes and the patient was discharged uneventfully in 48 hours and was well on one-week follow-up. LC is considered a safe surgery in patients with SIT. It requires an experienced surgeon, re-training of visual-motor coordination and modification of surgical steps.