bluuebunny/crossref_metadata_2025 huggingface dataset:
Liver reportalization in extrahepatic portal hypertension
Authors: Yagudin M. K.
Published: September 2023

The study involved literature review of history and current state of surgical treatment in extrahepatic portal hypertension in children and adults. The author analyzed Russian and foreign publications on surgical repair of hepatopetal blood flow in extrahepatic portal hypertension. The issue is a high priority in Russia, which is confirmed by the fact that the author found out the pioneer studies of Russian authors, which were published earlier than the works of foreign researchers. It was found that the role of hepatopetal blood flow restoration in extrahepatic portal hypertension in adults had been much less studied than in children. More clinical material is to be gathered to determine the feasibility of such interventions in adults.
Walt Whitman: A Current Bibliography, Winter/Spring 2023
Authors: Folsom Ed
Published: September 2023

Walt Whitman: A Current Bibliography, Winter/Spring 2023
The Impact of the Specifics of Enforcing Fines on the Repressive Power of Criminal Law Measures
Authors: Bavsun Maxim , Karpov Kirill , Kuznetsov Aleksey
Published: September 2023

The current legal regulation of fine enforcement, together with the clauses regulating the possibility of release from execution in cases of failure to enforce a court sentence, as well as the approaches to the interpretation of some concepts of criminal and penitentiary legislation, make it possible to free the persons who do not in fact pay the imposed fines from legal encumbrances constituting legal liability. The absence of a uniform understanding of the terms «enforcement of a sentence», «end of the enforcement of a sentence», «end of the enforcement proceedings» does not make it possible to objectively define the moment when a person can be released from punishment due to the statute of limitations for the guilty verdict. The inconsistency of the position of the Supreme Court of the Russian Federation regarding this issue also has a negative impact on the practice of law enforcement. The effectiveness of the criminal law systems impact is generally reduced as a result of a combination of various technical-legal, organizational and inter-branch factors. The situations of inter-branch misalignment, arising due to the absence of control at the stage of introducing amendments into criminal legislation and other normative legal acts, are unacceptable. The resulting practice of imposing this punishment is heterogeneous, which prevents the establishment of a clear and logical system of both enforcing fines and releasing from such enforcement. The existing situation is undoubtedly not normal and requires a legislative correction which, in its turn, will make it possible to ensure that the situation in this area corresponds with the ideas of justice and equality before the criminal law. The authors concept of improving the legal regulation in the analyzed sphere is based on the necessity to implement the principle of the inevitability of punishment and observe the principle of reasonable time for the enforcement of punishment. At the same time, it is necessary to recognize that indirect encouragement of the non-performance of obligations (restriction of rights) associated with punishment is unacceptable.
Лев Толстой и возможность мифологического мышления
Authors: Kulikov Anton
Published: September 2023

В статье разрабатывается подход к анализу мышления на основе философской интерпретации наследия Л.Н. Толстого как мифотворчества. Она помогает понять живую человеческую мысль (неизбежно теряемую, если полагаться только на спекулятивную объективацию), приблизиться к тайне ее осмысленности и связи с реальностью. Особая злободневность этой вечной тайны связана с тем, что не только сущность и основания мышления сегодня остаются непонятными, но и сама идея мышления сделалась аморфной и сомнительной. В этой ситуации критический анализ ряда распространенных представлений о мышлении (натуралистического, интеллектуалисткого и др.) выявляет их недостаточность и обосновывает необходимость иного понимания мышления. Цель работы — указать на событийный характер мышления в сопереживании Толстому и его персонажам. Толстой — создатель мифов, а в мифотворчестве реальность и человек соучаствуют в событиях мысли, в которых нельзя различить и противопоставить происходящее во внешнем мире и в самой мысли человека. Событие нельзя объяснить ни механически, ни телеологически: это мысль как не данное или заданное, а сбывающееся единство мира и человека. Мышление, как и миф, может быть понято как присутствие реальности в жизни человека, ее самовыражение и самообогащение.
Возможность воскресения
Authors: Van Inwagen Peter , Pavlov Aleksey
Published: September 2023

Перевод текста: Inwagen P. Van. The Possibility of Resurrection // Oxford Readings in Philosophical Theology. Volume II: Providence, Scripture, and Resurrection / ed. by M.C. Rea. — Oxford : Oxford University Press, 2010. — P. 321–327.
Смысл и политическое
Authors: Teslya Andrey
Published: September 2023

Рецензия на: Башков В.В. Репетиция политического : Сёрен Кьеркегор и Карл Шмитт. — СПб. : Владимир Даль, 2022. — (Архив политической мысли ; 2).
Жесткое ядро нового материализма в социальной теории
Authors: Popov Yegeniy
Published: September 2023

В статье представлено концептуальное описание жесткого ядра (в терминах Лакатоса) нового материализма в социальной теории, которое к настоящему моменту в социально-философском и социологическом дискурсе четко не определено. Существующая неопределенность базовых положений данного подхода ведет к тому, что исследователи не могут оценить объяснительный потенциал новоматериалистического подхода к анализу окружающей реальности, принимая его за новую версию эссенциализма или вариацию классической социальной философии, но с подвижными границами социального и материального. Концептуальное описание жесткого ядра происходит через соотнесение четырех базовых тезисов нового материализма, сформулированных конкретными авторами, работающими в данном направлении. Это тезисы о фокусировке на материи (Дж. Беннет, Р. Брайдотти); об акцентировании того, что материя делает, а не того, чем она является (М. Деланда, Б. Латур); о материальности эмоций и чувств (Г. Харман); о локализации действия материальности (К. Барад, Л. Брайант). Помимо этого, в статье рассматриваются критические аргументы в отношении нового материализма, которые сформулированы исследователями без обращения к тезисам жесткого ядра, но на основании обобщенного представления о новоматериалистическом течении как разрозненном и не до конца структурированном подходе. Обсуждается слабость этих аргументов, а также производится попытка их отражения через обращение к идеям конкретных авторов ядра новоматериалистического подхода.
Using recombinant human G-CSF to treat chemotherapy-induced neutropenia over 3 decades: What is next?
Authors: Jason Xu , Jonathan Sussman , Jessica Xu , Xing Zhao , Xiao Qiang Yan
Published: September 2023

Chemotherapy-Induced Neutropenia (CIN) is a potentially fatal side effect of cancer treatment, affecting > 50% of cancer patients treated with chemotherapy. Clinical use of recombinant human granulocyte colony-stimulating factor (rhG-CSF) has allowed for primary and secondary prophylaxis of CIN and its sequela (i.e., febrile neutropenia, fatal infection) during myelosuppressive chemotherapy. Here, we review the translation and properties of first, second, and third-generation rhG-CSF molecules, including filgrastim (Neupogen, FDA approved in 1991) and biosimilars, pegfilgrastim (Neulasta, FDA approved in 2002) and biosimilars, and F-627 (Ryzneuta, NMPA approved in 2023), a novel long-acting rhG-CSF agent developed this past decade. Even with the development of increasingly personalized and targeted cancer therapy, chemotherapy, and stem cell transplantation remains a backbone for the majority of patients with advanced cancers, especially in the hematopoietic system. As such, more than 20 million cancer patients have been treated with rhG-CSF drugs since the first approval of filgrastim. In the next decade, we envision third-generation rhG-CSF products such as Ryzneuta lowering costs to patients and healthcare providers, expanding access to this essential medication for cancer patients worldwide, particularly for patients who require more aggressive chemotherapy treatment.
Reviewing the Bio-Applications of SrAl2O4:Eu2+, Dy3+ Phosphor
Authors: Maryam Mollazadeh-Bajestani , AmirHossein Bahmanpour , Maryam Ghaffari , Fathollah Moztarzadeh , Azadeh Sepahvandi , Korebami Adebajo
Published: September 2023

Strontium aluminate (SrAl2O4) phosphor nanoparticles with Eu2+, and Dy3+ co-doping exhibit high brightness and long afterglow properties, storing light energy and glowing slowly under different conditions. It has been widely studied that SrAl2O4:Eu2+, Dy3+ (SAO) phosphor nanoparticles with a green visible emission can penetrate deep into the tissue, show low self-fluorescence, cause minimal light damage, and are biocompatible. SAO phosphor nanoparticle synthesis and modification mechanisms are outlined in this review. Biological therapies, in addition to the detection of substances in organisms, are provided by these excellent priorities. Despite the existing research, it has been demonstrated that nanostructures of SAO luminescence particles have great potential to be applied to tissue engineering and drug delivery systems. The current scientific achievements can provide a reference for research in the areas mentioned above, as well as stimulate biomedical disciplines to pay attention to SAO luminescence nanoparticles.
Türkiye’de kütük mobilya üretiminde kullanılan ahşabın görsel özelliklerinin incelenmesi
Authors: TIRAK HIZAL Kamile , ÖZTÜRK Emel
Published: September 2023

Ekolojik sistemin korunması, çevre odaklılık, sürdürülebilirlik, geri dönüşüm, erişilebilir ve temiz enerji, sorumlu üretim ve tüketim kavramlarının öne çıktığı günümüz dünyasında doğal malzemelere dönüş olduğu görülmektedir. Ahşap malzeme, insanlığın var oluşundan beri insan hayatında ve yaşamın her alanında yer alan bir malzemedir. Ahşabın ham, işlenmiş ya da kompozit gibi farklı formları mobilyanın temel bileşenini oluşturmaktadır. Bireyselleşmenin ön planda olduğu, kişiye özel ürünlerin ön plana çıktığı bir dönemde mobilyaların da kişiye özel ve tek olması, her biri başka bir örneği olmayan görsellere sahip kütük mobilyaların tercih edilme nedenlerinden biridir.

Bu çalışmada, son yıllarda mobilyada doğal formların yükselişi ile üretimi ve kullanımı yaygınlaşan kütük mobilyalar ele alınmıştır. Ağacın doğal formları ile üretilen kütük mobilya üreticilerine web ortamından ve görsel sosyal medya platformu üzerinden ulaşılarak, üretim yapan işletmelerin nerelerde yoğunlaştıkları, kütük mobilya üretiminde kullanılan ağaç türleri ile kullanılan ağaç malzemenin görsel özellikleri üzerine içerik analizi yapılmıştır.

Yapılan çalışma sonucunda, 25 il ve 5 coğrafi bölgede yoğunlaşan işletmelerin mikro ve küçük ölçekte oldukları, en fazla işletmenin İstanbul, İzmir, Ankara ve Bursa’da bulunduğu, mobilya türü olarak en fazla yemek masası ve sehpa üretildiği, ürünlerin metal ve epoksi malzemeler ile birleştirildiği, en çok kullanılan ağaçların ceviz, meşe, kestane, zeytin olduğu, en fazla düzensiz yıllık halkalar, çürüklük, koyu renkli şeritler, gövde çıkıntıları kusurlarına rastlandığı tespit edilmiştir.
DNA extraction v9.0 (modified BOMB) v1
Authors: Jie Phang Guan
Published: September 2023

DNA extraction using yttria-stabilized zirconia beads lysing and automated magnetic bead-based extraction.
Autoevaluación de competencias en estudiantes de medicina de último semestre adquiridas en diferentes modalidades de enseñanza y aprendizaje. Un experimento natural durante la pandemia por COVID-19
Authors: Patiño-Giraldo Santiago
Published: September 2023

Introducción: la pandemia por COVID-19 produjo cambios abruptos en la educación médica. La educación virtual se convirtió en una estrategia indispensable para dar continuidad a la formación médica. Sin embargo, aún no se cuenta con evidencia de sus resultados.Objetivos: identificar diferencias entre la metodología de enseñanza presencial y virtual en estudiantes de medicina del último año medidos por autoevaluación de competencias.Métodos: estudio observacional de la distribución natural en estudiantes del último año de Medicina. Se evaluó la autoevaluación de competencias y resultados de evaluación sumativa en tres diferentes grupos (presencial, virtual y bimodal). Se aplicó una encuesta para evaluar percepciones de la metodología virtual.Resultados: 83 estudiantes aceptaron participar. Dos terceras partes presentaron dificultades técnicas en la modalidad virtual. Se encontraron diferencias de medianas estadísticamente significativas a favor de la metodología presencial para las competencias en visitas domiciliarias (p = 0,03), ronda clínica (p = 0,021) y anticoagulación (p = 0,002); a favor de la metodología bimodal para la competencia en el manejo de la osteoartrosis (p 0,031) y a favor de la metodología virtual en la competencia de tamización de cáncer de pulmón (p = 0,02). Si bien la nota final presentó diferencias estadísticamente significativas en un curso, esta no fue académicamente relevante.Conclusiones: considerando la autoevaluación de competencias, es posible adquirir la mayoría de las competencias clínicas en cáncer y vejez a través de metodologías virtuales de forma similar a lo que se obtiene en la metodología presencial en un ambiente educativo emocionalmente seguro.
Escepticismo profesional
Authors: Vargas-González Carlos , Sáenz-Arenas Ángela
Published: September 2023

El escepticismo profesional hace parte del ADN de la auditoría, por lo que su comprensión y aplicación han sido aspectos relevantes para la investigación y la práctica de la contaduría y la contabilidad. Sin embargo, no se le ha prestado la suficiente atención al aporte que otorga la filosofía para la reflexión sobre el escepticismo profesional dentro de la auditoría. Por ello, este artículo tiene como objetivo explorar cómo la reflexión desde la filosofía, especialmente desde el pirronismo antiguo, contribuye a ampliar la comprensión del escepticismo profesional en la auditoría, para lo cual hace uso de una metodología cualitativa, específicamente, de revisión de literatura en filosofía y auditoría. El principal aporte de este estudio es mostrar que desde el pirronismo antiguo se puede ampliar el horizonte de comprensión del escepticismo profesional para la auditoría como un modo de vida y como una actitud mental.
Revelación de información no financiera en Universidades Colombianas en el contexto del COVID-19
Authors: Ceballos-García Daniel
Published: September 2023

Las universidades están llamadas a generar transparencia en la revelación de información no financiera, y aún más con el COVID-19, donde dichas instituciones juegan un papel fundamental para minimizar y comprender los efectos que trae este. El objetivo de este artículo es evaluar en qué medida las universidades de Colombia revelan información no financiera en sus sitios web y reportes universitarios, en relación con la priorización de los Objetivos de Desarrollo Sostenible (ODS). Se realizó un análisis descriptivo de 18 universidades entre públicas y privadas que se encuentra en el ranking de impacto THE, durante el año 2021. Se evidencia que las universidades tienen procesos de revelación de temas de sostenibilidad encaminadas a los ODS y a mitigar los efectos del COVID-19 de manera trasversal, sin incluir sesiones para los logros y retos. Sin embargo, el acceso a la información sigue siendo un reto para las universidades, que aun de manera voluntaria revelan información no financiera.
Finanzas como nueva forma de gubernamentalidad
Authors: Narváez Castillo Viviana Patricia
Published: September 2023

Desde la perspectiva de Foucault, el poder es entendido como el conjunto de prácticas utilizadas para gobernar a otros, y ha adoptado diversas formas a lo largo de la historia. Entre ellas, se encuentra el poder soberano que prohíbe y sanciona, el poder disciplinario que controla y vigila, el biopoder que interviene en la vida de las masas y, más recientemente, la gubernamentalidad que se enfoca en la gestión de la vida. Este artículo tiene como objetivo abordar la evolución de las formas de gobierno desde la óptica de Foucault y develar cómo las finanzas se configuran como una forma de gubernamentalidad que ejerce control sobre la economía y la sociedad.
Feature Extraction Techniques in Medical Imaging: A Systematic Review
Authors: Josphineleela R. , Preethi S. , M Ashwin. , Srikanth Meda , Ramesh Eluri , Kolluru Venkata Anusha
Published: September 2023

With the surge in the development of various applications in the field of Computer Vision and Digital Image Processing, a significant amount of medical pictures are being produced. Thus, the patient-specific scan pictures represent the boundless volume of data that requires careful organization and supervision to assist clinical decision support systems. Now that retrieval, classification, segmentation, and other procedures have been completed, these devices assist doctors to uncover serious illnesses including skin conditions, tumors, and cancer. This imaging largely depends on characteristics to detect the afflicted region and perform the diagnosis visually. The authors of this paper present an overview of numerous feature extraction approaches used to extract features from medical images obtained via different modalities, but only used a handful of these techniques for this job and provided the findings.
Predictive Internet of Things Based Detection Model of Comatose Patient using Deep Learning
Authors: Venkatesan Mithra , Kandalkar Sarthak Kishorrao , Menon Radhika , Kulkarni Anju.V. , Prasad Shashikant
Published: September 2023

The needs and demands of the healthcare sector are increasing exponentially. Also, there has been a rapid development in diverse technologies in totality. Hence varied advancements in different technologies like Internet of Things (IoT) and Deep Learning are being utilised and play a vital role in healthcare sector. In health care domain, specifically, there is also increasing need to find the possibility of patient going into coma. This is because if it is found that the patient is going into coma, preventive steps could be initiated helping patient and this could possibly save the life of the patient. The proposed work in this paper is in this direction whereby the advancement in technology is utilised to build a predictive model towards forecasting the chances of a patient going into coma state. The proposed system initially consists of different medical devices like sensors which take inputs from the patient and helps aid to monitor the condition of the patient. The proposed system consists of varied sensing devices which will help to record patient’s details such as blood pressure (B.P.), pulse rate, heart rate, brain signal and continuous monitoring the motion of coma patient. The various vital parameters from the patient are taken in continuously and displayed across a graphical display unit. Further as and when even if one vital parameter exceeds certain thresholds, the probability that patient will go into coma increases. Immediately an alert is given in. Further, all such records where there are chances that patient goes into coma state are stored in cloud. Subsequently, based on the data retrieved from the cloud a predictive model using Convolutional Neural Network (CNN) is built to forecast the status of the coma patient as an output for any set of health-related parameters of the patient. The effectiveness of the built predictive model is evaluated in terms of performance metrics such as accuracy, precision and recall. The built forecasting model displays high accuracy up to 98%. Such a system will greatly benefit health sector and coma patients and enable build futuristic and superior predictive and preventive model helping in reducing cases of patient going into coma state.
Brain Tumor Detection by using Fine-tuned MobileNetV2 Deep Learning Model
Authors: Jadhav Archana , Gadekar Amit
Published: September 2023

Most of the deaths in the world happen due to Cancer. It is a disease in which the cells of our body organs or tissues grow in an undisciplined way which in turn can harm our normal cells and tissues in our body. These cells very smartly trick the immune system so that the cancerous cells are kept alive and are not destroyed. In the human body, tumors can be classified into three types: cancerous, non-cancerous, and pre-cancerous. Timely identification of the cancer can be helpful in many ways. As it improves a patient’s chances of survival. The most valuable, uncomplicated technique used is MRI scans for predicting tumor is a tough task and have chances of human error. So to be more accurate with our predictions we have moved on to use computerized techniques to ease the work. The focus of this research is the development of an automated brain tumor classification system using magnetic resonance imaging (MRI) scans, leveraging a deep learning model. The proposed model employs a convolutional neural network (CNN) architecture known as MobileNetV2, which is trained on a pre-processed MRI image dataset to classify brain tumors into one of two categories: tumor tissues and normal brain tissue. To mitigate overfitting and expand the dataset, data augmentation techniques are employed. The trained model achieves high accuracy, sensitivity, and specificity in classifying brain tumors. Proposed CNN model outperformed other deep learning models, including VGG16, Xception, and ResNet50, which were used for comparison.
Pseudo Random Binary Sequences Obtained Using Novel Chaos Based Key Stream Generator and their Auto-correlation Properties
Authors: G S Pushpalatha , S Ramesh
Published: September 2023

In this paper, psuedo random binary sequences are generated from the “Chaos Based Key Stream Generator- using novel Permutation technique with two dimensional patterns and substitution technique with Z4 mapping” and investigation of auto correlation property for the generated seuwnces is presented. Initially a chaotic function, considering Logistic map is used to generate a Pseudo Random Numbers (PRNs). Then these numbers are converted into binary sequences using binary mapping. These sequences are further modified by novel permutation techniques defined using 2-Dimensional patterns, and substitution technique defined over Z4 transformation in order to improve their statistical properties. The resulting sequences are investigated for auto correlation properties using Normalized Hamming Auto Correlation function. The purpose of this work is to assessing the quality of sequences of uniformly distributed pseudorandom numbers from the proposed generator. It is found that, generated sequences exhibit good auto-correlation property which is suitable for key sequence or secret key for cryptographic applications.
An Image Quality Selection and Effective Denoising on Retinal Images Using Hybrid Approaches
Authors: Saju Binju , R Rajesh
Published: September 2023

Retinal image analysis has remained an essential topic of research in the last decades. Several algorithms and techniques have been developed for the analysis of retinal images. Most of these techniques use benchmark retinal image datasets to evaluate performance without first exploring the quality of the retinal image. Hence, the performance metrics evaluated by these approaches are uncertain. In this paper, the quality of the images is selected by utilizing the hybrid naturalness image quality evaluator and the perception-based image quality evaluator (hybrid NIQE-PIQE) approach. Here, the raw input image quality score is evaluated using the Hybrid NIQE-PIQE approach. Based on the quality score value, the deep learning convolutional neural network (DCNN) categorizes the images into low quality, medium quality and high quality images. Then the selected quality images are again pre-processed to remove the noise present in the images. The individual green channel (G-channel) is extracted from the selected quality RGB images for noise filtering. Moreover, hybrid modified histogram equalization and homomorphic filtering (Hybrid G-MHE-HF) are utilized for enhanced noise filtering. The implementation of proposed scheme is implemented on MATLAB 2021a. The performance of the implemented method is compared with the other approaches to the accuracy, sensitivity, specificity, precision and F-score on DRIMDB and DRIVE datasets. The proposed scheme’s accuracy is 0.9774, sensitivity is 0.9562, precision is 0.99, specificity is 0.99, and F-measure is 0.9776 on the DRIMDB dataset, respectively.
A Secure Storage Management & Auditing Scheme for Cloud Storage
Authors: Rathod Subhash G. , Bhise Ashish K. , Shaikh Nisar S. , Dongare Yogesh B. , Kothavle Suhas R.
Published: September 2023

Cloud computing is an evolving domain that provides many on-demand services that are used by many businesses on daily basis. Massive growth in cloud storage results in new data centers which are hosted by a large number of servers. As number of data centers increases enormous amount of energy consumption also increases. Now cloud service providers are looking for environmental friendly alternatives to reduce energy consumption. Data storage requires huge amount of resources and management. Due to increasing amount of demand for data storage new frameworks needed to store and manage data at a low cost. Also to prevent data from unauthorized access cloud service provider must provide data access control. Data access control is an effective way to ensure data storage security within cloud. For data storage cost minimization we are using DCT compression technique to ensure data compression without compromising the quality of the data. For data access control and security asymmetric cryptographic algorithm RSA is used. For data auditing we have used MD5 with RSA to generate digital signatures, In proposed work we tried to cover all attributes in terms of efficiency, performance and security in cloud computing.
Machine Learning Based Dynamic Band Selection for Splitting Auditory Signals to Reduce Inner Ear Hearing Losses
Authors: Divekar Sudhir Narsing , Nigam Manoj Kumar
Published: September 2023

Quality of hearing has been severely impacted due to signal losses occurs in the human inner ear explicitly in the region of cochlea. Loudness recruitment, degraded frequency selectivity and auditory masking are the major outward effects of inner ear hearing losses. Splitting auditory signals into frequency bands and presenting dichotically to both ears became a comprehensive solution to reduce inner ear hearing losses. However, these methods divide input signal into the fix number of frequency bands, this limits their applicability where signals have large variations in their spectral characteristics. To address this challenge, we have proposed machine learning based intelligent band selection algorithm to split auditory signals dynamically. Proposed algorithm analyze input speech signal based on spectral characteristics to determine the optimum number of bands required to effectively present major acoustic cues of the signal. Further, dynamic splitting algorithm efficiently divides signal for dichotic presentation. Proposed method has been examined on large number of subjects from different age groups and gender having cochlear hearing impairment. Qualitative and quantitative assessment shown significant improvement in the recognition score with substantial reduction in the response time.
Twitter Sentiment Analysis on Meta: A Lexicon-Based Analysis using Rapidminer
Authors: Md Saad Nor Hasliza , Zainul Abidi Alya Syahirah , Yaacod Zulnaidi , Mohd Ali Muhamad Mu’az , Kun Zhu
Published: September 2023

Meta is a new parent company of Facebook, Instagram, WhatsApp, and more as the tech giant tried to move on from its scandal-plagued social network to its metaverse vision. Meta-trended on Twitter as users worldwide tweeted their opinions about the rebrand. The rebranding of Meta has become a popular topic of debate. Social media users, companies, and investors desire to be a part of the next big thing. In this study, lexicon-based polarity detection automatically classifies data as positive, negative, and neutral. This study will investigate the main sentiments towards Meta and the topic social media users discussed Meta. In this study, 2997 tweets from the 9th  to the 10th of November 2021 were scraped from Twitter using RapidMiner. The findings showed that 36% had a positive sentiment, 29% had a negative sentiment, and 35% were neutral about the Mete rebranding announcement. In addition, the topics often tweeted include NFT and stock, reaction, Metaverse, partnership, AR and VR, and policy. This study can help the Meta understand the people's multi-faceted views towards the Meta rebranding.
A Novel User Experience Cloud Computing Model for Examining Brand Image Through Virtual Reality
Authors: Nie Yan , Liu Fengguo
Published: September 2023

This research paper presents a novel Cloud Computing User Experience (CCUE) approach to reconstructing the brand image of traditional Shanghai cosmetic brands by leveraging virtual reality (VR) technology and user experience (UX) research. Traditional Shanghai cosmetic brands possess rich cultural heritage and unique product offerings, but often face challenges in maintaining relevance in the modern market. The proposed CCUE uses the VR technology to create immersive and interactive experiences that allow consumers to explore and engage with the brand in a virtual environment. The developed CCUE model integrates the Artificial Intelligence (AI) integrated Imperialist Competitive Algorithm (ICA) for the user-machine interaction. With the CCUE a combination of VR simulations, product showcases, and interactive storytelling, users can experience the essence and history of traditional Shanghai cosmetic brands, fostering a deep connection and emotional attachment. Additionally, UX research techniques are employed to gather user feedback and insights, enabling the refinement and optimization of the VR experience. The findings of this CCUE contribute to the field of brand reconstruction and provide practical insights for traditional brands seeking to revitalize their image in a rapidly evolving market.
Region-based Convolutional Neural Network Driven Alzheimer’s Severity Prediction
Authors: Rajesh Ravula , Reddy Singadi Akhil , Devraj Gandikota Varma , Bhukya Raghuram , Dasari Harika , Srichandana Naaram
Published: September 2023

It's important to note that Alzheimer's disease can also affect individuals over the age of 60, and in fact, the risk of developing Alzheimer's increases with age. Additionally, while deep learning approaches have shown promising results in detecting Alzheimer's disease, they are not the only techniques available for diagnosis and treatment. That being said, using Region-based Convolutional Neural Network (RCNN) for efficient feature extraction and classification can be a valuable tool in detecting Alzheimer's disease. This new approach to identifying Alzheimer's disease could lead to a more accurate and personalized diagnosis. It can also help in early treatment and intervention. However, it's still important to continue developing new methods and techniques for this disorder. Considering this our work proposes an innovative Region-based Convolutional Neural Network Driven Alzheimer’s Severity Prediction approach in this paper. The exhaustive experimental result carried out, which proves the efficacy of our Alzheimer prediction system.
An Intelligent Knowledge Graph-Based Directional Data Clustering and Feature Selection for Improved Education
Authors: Zhang Weizhi , Chung Ee Jonathan Yong
Published: September 2023

With advancements in technology and the increasing availability of data, there is a growing interest in leveraging intelligent learning models to enhance the educational experience and improve learning outcomes. The construction of intelligent learning models, supported by knowledge graphs, has emerged as a promising approach to revolutionizing the field of education. With the vast number of educational resources and data available, knowledge graphs provide a structured and interconnected representation of knowledge, enabling intelligent systems to leverage this wealth of information. This paper aimed to construct an effective automated Intelligent Learning Model with the integration of Knowledge Graphs. The automated intelligent model comprises the directional data clustering (DDC) integrated with the Voting based Integrated effective Feature Selection model through the LSTM-integrated Grasshopper Algorithm (LSTM_GOA). The data for analysis is collected from educational institutions in China. Through the framed LSTM_GOA model the performance is evaluated fro the analysis of the student educational performance. The simulation analysis expressed that the developed model exhibits a higher classification performance compared with the conventional technique in terms of accuracy and Mean Square Error (MSE).
A New Artificial Intelligence based Internet Online English Teaching Model with Curriculum of Ideological and Political Concern
Authors: Hu Weiwei
Published: September 2023

With the development of artificial intelligence and the rapid spread of the Internet, online teaching has become an increasingly popular method of education. However, in the context of the post-epidemic era of COVID-19, online teaching has become even more important, as many educational institutions have been forced to transition to this model to ensure continuity of learning. In this context, there is a growing need to develop innovative approaches to online teaching that can effectively address the challenges posed by the pandemic. Online teaching has become increasingly important for higher education institutions around the world, and it has been particularly crucial during the COVID-19 pandemic. The teaching of English at universities and colleges exhibited significant performance for online teaching. The ideology concept performs online teaching in English for politics and comprises of different strategies. English teaching, several strategies can be implemented. This research paper proposes a novel approach to integrate artificial intelligence (AI) and cloud computing technologies in the online English teaching model with a curriculum of ideological and political concern for colleges and universities. The proposed model, referred to as AIIOE, aims to enhance the quality and effectiveness of online English teaching while also providing a comprehensive education on ideological and political issues. The AIIOE model utilizes natural language processing (NLP), machine learning, and cloud computing technologies to provide a personalized and interactive learning experience to students. The proposed curriculum includes topics related to political ideology, history, and culture to enhance students' awareness and understanding of their social and political environment. The study adopts a mixed-methods approach, including a survey of English teachers, focus group interviews with students, and an analysis of students' performance in English language proficiency and ideological and political awareness. The results indicate that the AIIOE model significantly improves students' English language proficiency, knowledge of ideological and political issues, and overall learning experience. The examination is evaluated based on the ideological and political curriculum with an Internet-based online teaching mode in English teaching. With the investigation of the Internet online teaching model, the significant contribution is evaluated. Through analysis, it is concluded that the concept of the Internet Online teaching model significantly contributed to ideological and political factors.
Automated Brain Tumor Detection from MRI Scans using Deep Convolutional Neural Networks
Authors: S. M. Manasa , B. K. Jeevitha
Published: September 2023

The brain, as the central nervous system's most critical part, can develop abnormal growths of cells known as tumors. Cancer is the term used to describe malignant tumors. Medical imaging modalities, such as computed tomography (CT) or magnetic resonance imaging (MRI), are commonly used to detect cancerous regions in the brain. Other techniques, such as positron emission tomography (PET), cerebral arteriography, lumbar puncture, and molecular testing, are also utilized for brain tumor detection. MRI scans provide detailed information concerning delicate tissue, which aids in diagnosing brain tumors. MRI scan images are analyzed to assess the disease condition objectively. The proposed system aims to identify abnormal brain images from MRI scans accurately. The segmented mask can estimate the tumor's density, which is helpful in therapy. Deep learning techniques are employed to automatically extract features and detect abnormalities from MRI images. The proposed system utilizes a convolutional neural network (CNN), a popular deep learning technique, to analyze MRI images and identify abnormal brain scans with high accuracy. The system's training process involves feeding the CNN with large datasets of normal and abnormal MRI images to learn how to differentiate between the two. During testing, the system classifies MRI images as either normal or abnormal based on the learned features. The system's ability to accurately identify abnormal brain scans can aid medical practitioners in making informed decisions and providing better patient care. Additionally, the system's ability to estimate tumor density from the segmented mask provides additional information to guide therapy. The proposed system offers a promising solution for improving the accuracy and efficiency of brain tumor detection from MRI images, which is critical for early detection and treatment.
Churn Identification and Prediction from a Large-Scale Telecommunication Dataset Using NLP
Authors: Sonawane Vijay R. , Thorat Abhinav S. , Suryawanshi Jaya R. , Dabhade Ravindra G. , Patil Megharani , Musmade Bhausaheb
Published: September 2023

The identification of customer churn is a major issue for large telecom businesses. In order to manage the data of current customers as well as acquire and manage new customers, every day, a substantial volume of data gets generated. Therefore, it's crucial to identify the causes of client churn so that the appropriate steps can be taken to lower it. Numerous researchers have already discussed their efforts to combine static and dynamic approaches in order to reduce churn in big data sets, but these systems still have many issues when it comes to actually identifying churn. In this paper, we suggested two methods, the first of which is churn identification and using Natural Language Processing (NLP) methods and machine learning techniques, we make predictions based on a vast telecommunication data set. The NLP process involves data pre-processing, normalization, feature extraction, and feature selection. For feature extraction, we employ unique techniques like TF-IDF, Stanford NLP, and occurrence correlation methods, have been suggested. Throughout the lesson, a machine learning classification algorithm is used for training and testing. Finally, the system employs a variety of cross validation techniques and training and evaluating Machine learning algorithms. The experimental analysis shows the system's efficacy and accuracy.
Survey on Hinglish to English Translation and Classification Techniques
Authors: D’Souza Nicole , Patel Devarsh , Saravta Jigyashu , Rao Ashwini
Published: September 2023

Code-mixing is the process of using many languages in one sentence and has a widespread occurrence in multilingual communities. It is particularly prevalent in texts on social media. Due to the widespread usage of social networking sites, a substantial amount of unstructured text is produced. Hinglish, i.e. code-mixed Hindi and English, is a frequent occurrence in everyday language use in India. Hence, a translation process is required to help monolingual users and to aid in the comprehension of language processing models. In this paper, we study the effective techniques for classification and translation tasks and also find gaps and challenges in the current research domain. After comparing a few existing methodologies for machine translation, a framework which showed an improvement in task of translation over the previous methods is proposed.
Pragmatic Evaluation of Health Monitoring & Analysis Models from an Empirical Perspective
Authors: Vallabhuni Sivanagaraju , Debasis Kumar
Published: September 2023

Implementing and deploying several linked modules that can conduct real-time analysis and recommendation of patient datasets is necessary for designing health monitoring and analysis models. These databases include, but are not limited to, blood test results, computer tomography (CT) scans, MRI scans, PET scans, and other imaging tests. A combination of signal processing and image processing methods are used to process them. These methods include data collection, pre-processing, feature extraction and selection, classification, and context-specific post-processing. Researchers have put forward a variety of machine learning (ML) and deep learning (DL) techniques to carry out these tasks, which help with the high-accuracy categorization of these datasets. However, the internal operational features and the quantitative and qualitative performance indicators of each of these models differ. These models also demonstrate various functional subtleties, contextual benefits, application-specific constraints, and deployment-specific future research directions. It is difficult for researchers to pinpoint models that perform well for their application-specific use cases because of the vast range of performance. In order to reduce this uncertainty, this paper discusses a review of several Health Monitoring & Analysis Models in terms of their internal operational features & performance measurements. Readers will be able to recognise models that are appropriate for their application-specific use cases based on this discussion. When compared to other models, it was shown that Convolutional Neural Networks (CNNs), Masked Region CNN (MRCNN), Recurrent NN (RNN), Q-Learning, and Reinforcement learning models had greater analytical performance. They are hence suitable for clinical use cases. These models' worse scaling performance is a result of their increased complexity and higher implementation costs. This paper compares evaluated models in terms of accuracy, computational latency, deployment complexity, scalability, and deployment cost metrics to analyse such scenarios. This comparison will help users choose the best models for their performance-specific use cases. In this article, a new Health Monitoring Metric (HMM), which integrates many performance indicators to identify the best-performing models under various real-time patient settings, is reviewed to make the process of model selection even easier for real-time scenarios.
Acoustic Feature Identification to Recognize Rag Present in Borgit
Authors: Barthakur Spandan Kumar , Sarma Parismita , Baruah Gaurav Narayan
Published: September 2023

In the world of Indian classical music, raga recognition is a crucial undertaking. Due to its particular sound qualities, the traditional wind instrument known as the borgit presents special difficulties for automatic raga recognition. In this research, we investigate the use of auditory feature identification methods to create a reliable raga recognition system for Borgit performances. Each of the Borgits, the devotional song of Assam is enriched with rag and each rag has unique melodious tune. This paper has carried out few experiments on the audio samples of rags and a few Borgits sung with those rugs. In this manuscript three mostly used rags and a few Borgits  with these rags are considered for the experiment. Acoustic features considred here are FFT (Fast Fourier Transform), ZCR (Zero Crossing Rates), Mean and Standard deviation of pitch contour and RMS(Root Mean Square). After evaluation and analysis it is seen that FFT  and ZCR are two noteworthy acoustic features that helps to identify the rag present in Borgits. At last K-means clustering was applied on the FFT and ZCR values of the Borgits and were able to find correct grouping according to rags present there. This research validates FFT and ZCR as most precise acoustic parameters for rag identification in Borgit. Here researchers had observed roles of Standard deviation of pitch contour and RMS values of the audio samples in rag identification.
Oxygen Saturation and Heart Rate Monitoring System by Mobile
Authors: Perez-Siguas Rosa , Matta-Solis Hernan , Matta-Solis Eduardo , Matta-Perez Hernan , Remuzgo-Artezano Anika , Perez-Siguas Luis , Meneses-Claudio Brian
Published: September 2023

COVID-19 changed the way people live, causing a high mortality rate and making the entire health system in the world difficult due to the lack of equipment and infrastructure, causing many people to be treated from home, constantly measuring their saturation level and heart rate with the help of a pulse oximeter,  since the virus directly affects the respiratory system, it has been added that people with lung or cardiovascular problems also have to perform the constant measurement of both vital signs to visualize that they are in a normal range, otherwise, they would have to attend a medical center, so it is important to monitor both vital signs. In view of this problem, in this article a monitoring system of oxygen saturation and heart rate was carried out by mobile application, in such a way that both vital signs can be constantly monitored and an alert can be given to the health center with the coordinates of the person when the system detects a measurement outside the normal range,  with this, it would help people who may need medical attention immediately, being able to save their lives. Through the development of the monitoring system, it was possible to observe the operation of the mobile application with an efficiency of 99.15% in monitoring the level of oxygen saturation and 98.78% in monitoring the heart rate, taking 4 seconds to send the alert to the health center to assist the patient.
Prediction of Epilepsy Seizures by Machine Learning Methods
Authors: Deshmukh Mayuri Tushar , Suralkar Shekhar R.
Published: September 2023

According to the Globe Health Organization (WHO), more than 50 million people throughout the world are living with a diagnosis of epilepsy, making it perhaps of the most widely recognized neurological issue. Epileptic seizures are a leading cause of hospitalization and mortality across the globe. Accurate and prompt diagnosis is more crucial than ever given the increase in epileptic seizures all through the globe and their effect on individuals' lives. Epilepsy, cancer, diabetes, heart disease, thyroid disease, and many more are only some of the diseases for which machine learning approaches are being applied in prediction and diagnosis. Epilepsy is one ailment that may be treated early on to save a person's life. The main objective of this research is to use feature label extraction to the dataset in order to obtain the best ML models for epileptic seizures. In order to predict epilepsy, we used the techniques of logistic regression, SVM, linear SVM, KNN, and RNN in this study. The models employed in this research are accurate to varying degrees and have attributes including precision, recall, f1-score, and support. This study demonstrates that the model is able to accurately predict the occurrence of epilepsy. Our discoveries demonstrate that involving Examination highlight extraction in the dataset, the Regional Neural Network (RNN) model with 99.9998 % Training data accuracy and 97.78% Test data accuracy and 100% prediction probability of epilepsy seizure produces the best results and also the feature characteristics of RNN is better as compared to other models used in current research work.