bluuebunny/crossref_metadata_2025 huggingface dataset:
Advancing portfolio optimization: The convergence of machine learning and traditional financial models
Authors: Huang Siyu
Published: April 2024

This paper delves into the transformative impact of machine learning (ML) on portfolio optimization, showcasing how ML algorithms can significantly enhance traditional financial models such as the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT). Through a comprehensive examination of regression analysis, classification algorithms, and reinforcement learning, we illustrate the methodologies by which ML refines the prediction of asset returns, assesses investment risks, and dynamically adjusts portfolio allocations. We discuss the integration of ML with CAPM and APT to improve the estimation of systematic risk and identify multi-factor influences on asset returns, offering a more nuanced approach to optimizing portfolios. Additionally, the paper highlights the role of big data in augmenting predictive accuracy and the application of optimization algorithms like Gradient Descent and Genetic Algorithms in achieving optimal asset allocations. By addressing challenges such as multicollinearity and overfitting, we demonstrate the potential of ML to revolutionize investment strategies, enabling more sophisticated risk management and return maximization. This study not only underscores the synergy between ML and traditional financial theories but also paves the way for future innovations in financial analytics.
Development Status and Future Prospects of Photovoltaic Cells
Authors: Wang Ruoxi
Published: April 2024

With the rapid development of social economy, the consumption of conventional energy is growing at an amazing rate. The energy shortage crisis and the environmental problems brought by conventional energy will seriously restrict social development and affect the daily lives of residents. Therefore, paying attention to the creation and use of new energy, protecting the environment, improving efficiency, controlling the emission of pollutants and realizing sustainable development have become the main research topics in the new era of the energy sector. Photovoltaic energy has the advantages of economic energy saving, green environmental protection, wide application and sustainability, and is an ideal new energy, that has been developed to the third generation. This paper mainly combs the development process of photovoltaic technology, summarizes the characteristics, advantages and disadvantages of the third generation of photovoltaic technology, analyzes the current situation and prospects of photovoltaic technology development, and analyzes the problems and challenges faced. This research finds that as the economy and technology continue to advance, photovoltaic cell technology is developing rapidly, and the application cost is constantly reduced. The photovoltaic cell industry will get more attention and better development, and its application prospect is very broad. The research of this topic is helpful in enhancing the comprehensive and objective understanding of the development of photovoltaic cell technology, and will provide a valuable reference in order to advance the photovoltaic sector in the future, which has important practical significance.
Application and recycling of lithium-ion batteries
Authors: Cheng Xingqi
Published: April 2024

As an emerging technology with a vast worldwide market application and potential over decades, electric vehicles (EVs) have played a significant role in human society to prevent the deterioration of the environment. Meanwhile, the research on lithium-ion batteries (LIBs) in EVs improving their performances, including energy density and cycle life, has recently attracted much attention, aiding the further development of the functions of EVs. This research can be divided into three parts. Firstly, the fundamental of LIBs is presented, including its theory and reasons for having low energy density. Then, the alternative Li-S battery technology employed for EVs is introduced, and its advantages and limitations for future research are illustrated. Lastly, the three main approaches for recycling used LIBs, pyrometallurgical recovery (Pyro.), hydrometallurgical metals reclamation (Hydro.) and direct recycling (Dir.) are elaborated, each of their strengths and weaknesses are shown, and their performances are compared by considering several aspects for selection.
The recovery of the Antarctic ozone layer and suggestions for addressing the global warming
Authors: Zhang Erjia
Published: April 2024

The ozone layer is a critical shield for humanity, located in the Earths atmosphere with a high ozone concentration. Its primary role is to absorb and filter out the majority of harmful ultraviolet rays emitted by the sun, which pose a threat to all living beings. However, the ozone layer suffered from very severe depletion. To counteract this, the Montreal Convention was established in 1987, mandating a reduction in chlorofluorocarbon emissions by humans. Because of the environmental problem of ozone layer destruction for a long time, based on some existing research background at this stage, people find that the ozone layer is gradually recovering through observation of some data. In this essay, it explains the causal factors behind the depletion of the Antarctic ozone layer, as well as the various elements that contribute to its recovery and their respective levels of significance, through some research. Additionally, this essay explores how these models can be applied to address other environmental concerns. To achieve a more comprehensive understanding of this subject, this paper has conducted an intensive search through various academic references. In conclusion, through the use of balloon and satellite ozone data, a chemistry-climate model, and volcanic aerosol measurements, the healing of the Antarctic ozone layer is contributed by three factors: chemical reduction, kinetics, and temperature. Among these factors, chemical factors have the greatest contribution.
Impacts of spatial and temporal patterns and economic development on carbon emissions in Chinese cities
Authors: Ma Mengjie
Published: April 2024

With the economic and social development and the improvement of peoples living standards, the urban carbon emission increases. Studying the influence of industrial structure and spatial patterns on carbon emission under the economic development of different cities can help to find the main factors affecting carbon emission, formulate policies to solve the problem according to local conditions, and provide an important scientific basis for the transformation of economic growth mode and the construction of low-carbon cities. This paper draws conclusions by analyzing the trend curves of urban carbon emissions and economic factors over the years. Different regions of the citys economic development potential differences are obvious, and the potential for greater spatial concentration of cities; citys economic development potential in the spatial and temporal distribution of key cities and major urban agglomerations as the basis for the point - line - surface in order to promote. economically developed cities, the total carbon emissions, carbon emissions growth rate of a declining trend; economically underdeveloped areas, the total carbon emissions, carbon emissions growth rate of a year-on-year trend; the degree of carbon emissions and the degree of prosperity of the secondary industry is closely linked. Chinas urban carbon emissions have a significant positive spatial correlation between 2006 and 2016, the cold spot area of urban carbon emissions is relatively stable, mainly distributed in the eastern and southern economic zones, while the hot spot area is mainly distributed in the northwest, northeast and the middle reaches of the Yellow River economic zone.
Optimization of structural damage identification method based on finite element method and Bayesian updating
Authors: Chen Leming , Liu Yingtong , Luo Derui
Published: April 2024

The application of Bayesian updates to the finite element method for structural damage detection represents an avant-garde approach in the industry. Its merit lies in astutely transforming the influence of uncertainty into a forward problem. Nonetheless, the selection of simulation experiments and loading measurement schemes still relies to a certain extent on exhaustive methods. In this paper, based on the finite element method and Bayesian method, the OpenSees software platform is used to compare the actual experimental values with a large number of simulation experiments. The sensitivity of loading and measuring schemes is clarified, and the significance of simulation experiments in finding efficient schemes and reducing detection costs is verified. Concurrently, taking a truss structure as a case study, the combination of structural optimization and simulation experiment is deeply studied. The conclusion of this paper can provide a way to improve the level of simulation experiments and save computing resources.
Az Anti-Tőke mint kritikai elmélet
Authors: Weiss János
Published: April 2024

Bence György, Kis János és Márkus György közös könyve a hetvenes évek elejére készült el, és a címe: Hogyan lehetséges a kritikai gazdaságtan? A köznyelvben az Anti-Tőke vagy az Überhaupt elnevezés terjedt el. A kritikai töltés miatt én az előbbit preferálnám, de tartalmilag az utóbbi beszédesebb: lehetséges-e egyáltalán a kritikai gazdaságtan? Ez a szocializmusnak egy olyan elmélete lenne, amely a marxizmus reneszánszának kontextusába illeszkedik. Dolgozatom azt szeretné megmutatni, hogy az elemzett könyv két fontos tézist tartalmaz: (1) a marxi elmélet nyomvonalain haladva egy ilyen elmélet nem lehetséges, (2) a szocialista gazdaság nem lehetséges, illetve működésképtelen.
Determinación de la vida en anaquel de hamburguesa de carne de Pseudoplatystoma punctifer mediante evaluación bacteriológica y sensorial
Authors: Esquivel Sajami Dalia Miluska , Dávila Ruiz Berita , Rivadeneyra Sánchez Norma Lorena , Correa Martel Junior , Maslucan Canayo Óscar , Bustillos Aponte Gino , Vara Valverde Carlos Augusto , Moreno Ríos Christiaan Errol , Alarcón Castillo Teresa , Muro Lozada Paul Francis Martín , AMARINGO CORTEGANO CARLOS ANDRE
Published: April 2024

La presente investigación tuvo como objetivo determinar la vida en anaquel de hamburguesa de carne de Pseudoplatystoma punctifer mediante evaluación bacteriológica y sensorial del producto. Se elaboraron hamburguesas y se evaluaron sensorialmente las hamburguesas fritas en el día 0, y pre-cocidas en los días 0, 7 y 14, utilizándose diez jueces para el día 0 para las hamburguesas fritas y tres jueces para los días 0, 7 y 14 para las hamburguesas pre-cocidas. Se evaluaron Aerobios mesófilos, Escherichia coli y Salmonella sp. de la pulpa de pescado en el día 0, y de las hamburguesas pre-cocidas en los días 0, 7 y 14. Las hamburguesas fueron conservadas en refrigeración durante el período de evaluación. La escala de Karlshure fue usada para la evaluación sensorial. Para el análisis de los datos bacteriológicos se empleó la prueba no paramétrica de Friedman, y para los datos sensoriales se empleó la prueba no paramétrica de Friedman para verificar la diferencia de calidad de las hamburguesas en diferentes días de muestreo y la prueba no paramétrica de Kruskal-Wallis para verificar la posible diferencia causada por la aplicación de prueba con diferentes jueces (p < 0.05). Los resultados de la escala sensorial no mostraron diferencia estadística en los diferentes días de evaluación, con valores aceptables. En cuanto a los parámetros bacteriológicos evaluados, solo Aeróbios mesófilos se ha hecho presente, siendo los días 0 y 7 con niveles muy bajos, creciendo ostensiblemente en el día 14, aunque sin superar el límite permitido. Aunque sensorialmente no hubo diferencia entre los tiempos analizados, se considera que la vida en anaquel de la hamburguesa en refrigeración adecuada podría ser de 7 días considerando el aumento de Aerobios mesófilos en el día 14.
Two-stage pancreaticoduodenectomy: a case report
Authors: Ozdemir Egemen , Gunes Orgun , Sumer Fatih , Kayaalp Cuneyt
Published: April 2024

Pancreaticoduodenectomy is one of the major gastrointestinal surgeries and still has significant complication rates. Although most complications appear postoperatively, severe intraoperative difficulties and complications can occur in certain circumstances. Here, we presented the management of two cases who underwent two-stage pancreaticoduodenectomy due to intraoperative problems. Two patients have become hemodynamically unstable during an elective pancreatic resection combined with both arterial and venous reconstructions. All the gastrointestinal reconstructions were postponed to second sessions for damage control surgery. A two-stage pancreaticoduodenectomy is an option to be considered in a hemodynamically unstable patient during elective pancreaticoduodenectomy.
Enseñanza y Ansiedad: Experiências de los docentes en el início de la Carrera
Authors: Vunge João de Deus Ferraz , Morais Tomásia Francisca Estevão
Published: April 2024

This article will allow us to scientifically obtain an updated view of work anxiety, focused on the beginning of a university professor's career, with the aim of understanding what the experience of a beginning professor is like and how they build their identity. Specifically describe the challenges that these professionals face in their professional practice. S. Freud's psychoanalytic theory was used, according to which human behavior is based on instincts or unconscious impulses, some being aggressive and others destructive. Currently, the challenges that society poses, and that schools face, are increasingly demanding, with the level of anxiety and stress among teachers being notorious and their possible association with the degree of satisfaction in the educational process. For this research, the qualitative approach was used for a deeper analysis and in-depth interviews were used to collect the data. The target audience for the research was beginner and seasoned teachers from higher education institutions. As the results indicate, every teacher is affected by anxiety in the exercise of their profession, which is good when it drives and pathological when it creates obstacles in the teaching activity. Professionals are satisfied with their profession, but hard work is still needed on a personal and professional level to achieve the university's objectives and overcome the challenges imposed on it. However, it is necessary to invest in the continuous training of teaching staff to improve the quality, pedagogical conditions of teaching and scientific production.
Exploración de las Matemáticas en Juegos de Damas: un Aporte a la Enseñanza de las Matemáticas
Authors: Selezi Simão Pedro Mateus
Published: April 2024

Aversion to mathematics shown by learners belonging to different age stages, worries and challenges mathematicians and/ or mathematics teachers to develop surveys which might improve learners´ mathematics learnings. “Thawing” recondite mathematics in several games may aid to provide a different mathematics vision. The current research article aims to identify and analyze the “concealed” mathematics through checker games played in Angola, mostly in Cuando Cubango province. The survey is placed in ethnomathematics and considering its sorts, qualitative methodology has been used supported by a half-structured interview, direct observation to picture recording.  The research was conducted in the Menongue municipality (Cuando Cubango capital city). The three versions of checkers games were described (12 by 12; 10 by 10 and 8 by 8), some questions were created that guided this investigation (Why does the structure of the checkers game have the same number of rows and columns? Why do players when placing checkers pieces in the middle always leave two empty rows? Why is the sum of rows and columns equal to the even number? Why is each painted square on the checkers board corresponds to an odd number?) and, finally, some mathematical concepts involved in that game were explored (such as, for example, the notion of the Cartesian plane, diagonal lines, distance between two points, median (element of Statistics), numbers even and odd).
Auditoría como Instrumento para la Gestión Estratégica de Recursos Humanos: Caso de la Facultad de Humanidades de la Universidad Agostinho Neto
Authors: Garcia Karen Lucélia Pedro
Published: April 2024

It is important to create conditions for differentiation of teachers throughout their career and the performance assessment reports on their performance in professional practice. To improve teaching and institutional processes, it is essential to find mechanisms that carefully verify the compliance of procedures and evaluate the teaching staff themselves. For this reason, this study aims to diagnose the effects of auditing on the teaching performance process in higher education institutions. To achieve the objectives, exploratory and descriptive research was used, in the qualitative and quantitative approach based on the case study. It was possible to access data analysis through the compliance grid previously prepared from a questionnaire applied to teachers at the Faculty of Humanities. The results indicate that the faculty has a positive audit report as most teachers who participated in this research responded positively in relation to the institution's compliance with the evaluation of its performance, which allows us to conclude that there is compliance in this process at the institution.
GESTÃO ESCOLAR DEMOCRÁTICA, O SUCESSO ESCOLAR E OS INDICADORES EDUCACIONAIS: O CASO DAS ESCOLAS PÚBLICAS ESTADUAIS EM MORADA NOVA, CEARÁ, BRASIL
Authors: Silva Tomé e
Published: April 2024

O artigo é parte de uma pesquisa de Mestrado em Ciências da Educação e investiga a gestão escolar a partir da instalação do modelo de gestão democrática, das escolas da rede pública estadual de ensino, implantado em todo o estado do Ceará, buscando relacionar suas interferências nos indicadores educacionais. O projeto de educação básica implantado pela Secretaria de Educação Básica do Estado do Ceará, a partir de 1995 quando do início do processo de abertura das escolas para vivência da democracia, com criação de Conselhos Escolares e eleições diretas para Diretores Escolares. A discussão dos educadores e especialistas na área de educação e gestão escolar proporcionou embasamento teórico aos dados coletados e analisadas nas Escolas Estaduais no Município de Morada Nova, Ceará, a saber: Escola de Ensino Médio Egídia Cavalcante Chagas e Colégio Estadual Maria Emília Rabelo. O ambiente da pesquisa é a escola, a sala de aula, as galerias, os laboratórios e todos os espaços vivos da escola, onde encontramos seus sujeitos principais, os alunos. Os sujeitos que serão investigados, através de entrevistas e questionários, são os professores, coordenadores, diretores, funcionários, alunos e pais, em uma amostragem que contemple 10% de cada segmento escolar, abrangendo os três turnos de funcionamento: manhã, tarde e noite. As questões tratam do problema que norteia a pesquisa: o modelo de gestão democrática com as eleições diretas para diretores escolares favoreceu ou dificultou a aprendizagem do alunado, de modo que contribuísse para o sucesso escolar? Cruzamos os dados oficiais de desempenhos do SPAECE com os projetos pedagógicos das escolas, e as entrevistas com os membros da comunidade escolar, para desvendar nuances da gestão democrática escolar, em que indicam elementos que elucidem os acertos desse projeto de educação, bem como vislumbrem contribuições e novos caminhos para a educação cearense e para o sucesso do aluno e da escola.
Empowering Peer Leaders: Designing Dynamic PLTL Training in the United Kingdom
Authors:   Queen Mary University of London, Christian Tyler , Wilton Hannah , Shahid Redwan , Faulkner Sally , Howell Lesley
Published: April 2024

In the academic year 2021-2022, Queen Mary University of London (UK) launched its successful pilot of Peer-Led Team Learning (PLTL) within undergraduate chemistry and economics courses. This year (2022-2023), the program expanded, with PLTL also introducing courses within biological sciences and business studies. Over 900 students are now enrolled in our PLTL program facilitated by approximately 80 Peer Leaders. Building on this success and inspired by colleagues from the University of Rochester on Peer Leader training materials, we sought to develop our own bespoke training materials for QMUL Peer Leaders. Working with our Peer Leaders, content was co-created to develop a feedback-orientated program for future cohorts. The current cohort of Peer Leaders was surveyed to identify gaps in the previous training program and suggest ideas to be prioritized. The enhanced training program emerged as an immersive online ‘course page’ comprising of written documents, videos, and interactive activities. This material will be consolidated and reinforced through an in-person training day to be held in late September during “Welcome Week” culminating in a final online assessment. This paper explores the intricacies of the decision-making process involved in crafting the training materials and serves as a template for other UK Universities adopting PLTL.
Introduction to Training Peer Leaders Workshop-Style at the University of Houston-Downtown
Authors:   University of Houston-Downtown, McKee Allison
Published: April 2024

The training of Peer Leaders at the University of Houston-Downtown (UHD) differs from other institutions that have implemented Peer-Led Team Learning (PLTL). For over a decade, peer leader training has been treated as its own kind of workshop. This requires the position of a training coordinator, an experienced peer leader whose job is to prepare incoming peer leaders in how the workshop is supposed to be conducted, as well as to consider possible scenarios that may occur and other considerations that peer leaders may encounter when working with other students. Facilitating the preparation of peer leaders in the same workshop style that they would use helps to build camaraderie between the novice peer leaders and lets them experience the group learning environment firsthand before running workshops on their own.
Job Assistant: Job Preparation and Community Engagement Application with AI
Authors:   Sinhgad Institute of Technology, Lonavala, Maharashtra, India, Bhopale Assistant. Prof. Sagar
Published: April 2024

This paper outlines the offerings of our application tailored for job seekers, emphasizing the extensive range of job listings provided alongside an interactive community platform and AI Assistant for personalized guidance and support, streamlining the job preparation journey. The research project delves into the pivotal role of job search websites and underscores the importance of efficient user logins on these platforms. Given the growing significance of job placements in sustaining livelihoods, both job seekers and recruiters increasingly rely on job portals to find the ideal match for their requirements. Moreover, job portals facilitate engagement within communities, fostering connections between individuals and companies. This study aims to shed light on the multifaceted benefits of integrating AI-driven features, interactive platforms, and comprehensive job listings within a single application, ultimately enhancing the job search experience and facilitating meaningful engagement within professional communities. Key Words: Employment, AI Job Assistant, Interview preparation, Job hunting, AI-powered job support, Career development.
Role of Influencer Marketing in Consumer Purchase Decisions: A Bibliometric Analysis and Discourse
Authors:   Mittal School of Business, Lovely Professional University, Punjab, India, Bhanot Neeraj
Published: April 2024

In today's digital age, the power of influencer marketing has an important place and determines customer decisions in different sectors. This compendium delves into the complex world of influencer marketing, informed by a comprehensive analysis of the latest research in the field. With this research, we aim to re-veal the important role of employees in the formation of consumer behaviour and purchasing intention. Our journey begins by revealing the impact of an influencer's presence on multiple platforms on behaviour, showing how messaging influences emotions and behaviour. Know the connection to the affected person. Additionally, we examined the claim of polarizing influencers' identity by revealing how the debt issue leads to defensiveness that impacts on influencing marketing strategies that deliver a unique network of customer loyalty, consumer behaviour and purchasing behaviour. We explore the hidden gems in retail live streaming and marketers' social media presence, revealing their impact on offline purchasing decisions beyond the digital world. By exploring interpersonal interactions, we show how interpersonal interactions influence brand perceptions and communication outcomes. We also carefully examine the influence of creators on information followers according to the balance between business and reality. We present the next list, revealing the difficulty of uncovering practices in the influencer industry, their small impact on various dimensions of reality, and the impact of stakeholders' needs. Combining recent research studies, this cornerstone provides guidance to business leaders as they navigate the dynamic areas of business. By presenting small conversations between influencers, consumers, and brands, we provide experts with insights to increase business potential that are useful in guiding customer engagement and purchasing decisions.
ADVANCMENTS IN TEXT SUMMARIZATION AND EXTRACTIVE QUESTION- ANSWERING : A MACHINE LEARNING APPROACH
Authors:   dept of Electronics and Communication Engineering RVR & JC College Of Engineering, Guntur, Chaitanya DR D. Eswara
Published: April 2024

In the era of social media platforms, the rapid expansion of data mining in the fields of information retrieval and natural language processing emphasizes the crucial need for automated text summarization. At the current time, pretrained word embedding techniques and sequence to sequence models can be effectively repurposed in the realm of social network summarization to efficiently condense significant information with strong encoding capabilities. However, dealing with the challenge of extended text dependency and efficiently utilizing latent topic mapping presents an increasingly significant obstacle for these computational models. In this document, we propose a topic- focused approach for both extractive and abstractive summarization, integrating Question Answering features by employing BERT and Pegasus pretrained models. A comprehensive analysis of the architectural complexities of the pre-existing models utilized in the creation of our model has been presented. The evaluation of our Text Summarization & Question Answering Model was conducted meticulously on a variety of datasets including Multi news, XSum, and CNN/Daily Mail, with experimental results indicating its achievement of state-of-the-art performance levels based on ROGUE scores. Following this, the verification of our results through human evaluation confirms that the summaries generated by our model are in alignment with the performance standards set by humans across various datasets. Furthermore, we have integrated an Extractive Question Answering task into our model, proposing an architecture leveraging BERT and conducting comparative analyses against alternative language models to gauge its efficacy. Keywords—Natural Language Processing (NLP), Text Summarization, Question & Answering (Q&A), Transformer Models, Pegasus, BERT (Bidirectional Encoder Representations from Transformers), Neural Topic Modeling, Extractive Summarization, Abstractive Summarization, Semantic Representation, Topic Embedding
The Paradox of Power: Securing User Data in the Cloud's Shadow
Authors:   Department of Computer Applications  Chandigarh School of Business  Chandigarh Group of Colleges, Jhanjeri, Mohali, India, Ahuja Ajay
Published: April 2024

Whilst the blessing of the cloud, which provides for adaptability and easy data sharing as well as storage, comes with some security doubts. We place a major responsibility on our backs of protecting the data provided by our users from any evil attempts to sneak in and also risks of being accidentally exposed and the ones that exist through shared infrastructure. This paper traverses this complex terrain by noting the need for targeted security solutions which are aimed towards dealing with the compounded nature of the fast-growing problem. By exploring such threats as data breaches, the possible encryption breaks and the nature of server sharing we will analyze the safety of cloud-based services. We position that the existing security methods, though very necessary, are not exclusive in responding to the ones posed by the new threats. This means we will therefore be rather flexible in our design of the data security in the cloud. By the way, we will employ innovative strategies, maybe, by replacing a word for words such as homomorphic encryption, zero-knowledge proofs, and federated learning, presenting how it holds promise for private and confidential assets. Furthermore, we investigate the exploding influence of blockchain technology, regarding the wages it might deserve in providing manipulated data authentication and creating trust. The work in this paper creates a path towards a future when database users' virtual information in the cloud is safe and certified. Through proposing a wide-ranged approach, which equates realized ideas with well-established security frameworks, we shall lead a cloud infrastructure where stability and power would prevail. Keywords – Cloud Computing, Security Issues, Security Challenges
A STUDY ON CONSUMER BUYING BEHAVIOR TOWARDS SOFT DRINKS: A CASE STUDY IN BANGALORE
Authors:   JAIN (Deemed-to-be-University),, Begum Dr. Salma
Published: April 2024

Understanding consumer behavior of an essential component in developing a marketing plan since customers are the most crucial aspect in the success of any organization. The study of what consumers purchase, how they buy it, where they buy it, and how much of it they buy at what time and place. Along with the choice of whether to buy or not, there is also the choice of which source to use. The purpose of this study is to examine soft drink consumer preferences and purchasing trends. Understanding consumer behavior is one of the essential elements in developing a marketing plan, as customers are the most important aspect of the success of any business. Consumer demand is key in the context of the perpetual business as it has been proven to drive companies into the organization. Therefore, you need to understand the importance of their actions while creating and executing your marketing tactics. Consumer behavior is the object of study. What to buy, why to buy (price, advertising), why to buy (product), where (store), how (method) to buy (method of purchase), etc. In addition, factors that influence internal and external behavior, such as self-concept, age, family, social and cultural background, and views on both socioeconomic class and personality. Keywords: Consumer buying behavior, buying soft drinks, product, price, place, promotion.
A STUDY ON STUDENTS SATISFACTION LEVEL TOWARDS ONLINE EDUCATION
Authors:   JAIN(Deemed-to-be-University),, Begum Dr. Salma
Published: April 2024

The rapid growth and integration of technology in the educational sector have led to a significant shift towards online education, especially in recent years due to global events like the COVID-19 pandemic. This study aims to investigate students' satisfaction levels towards online education and identify the factors influencing their perceptions. A mixed-method approach was employed, combining quantitative surveys and qualitative interviews to gather comprehensive data from a diverse sample of students across various educational levels. The quantitative data were analyzed using descriptive statistics and inferential tests to determine the overall satisfaction level and significant differences among demographic groups. Meanwhile, qualitative data were subjected to thematic analysis to gain deeper insights into students' experiences and perceptions. Preliminary findings indicate a generally positive satisfaction level among students towards online education, with the flexibility and convenience of online learning being the most appreciated aspects. However, there were also concerns raised regarding the lack of face-to- face interaction, technological challenges, and the quality of online instruction. Significant variations in satisfaction levels were observed based on factors such as age, prior experience with online learning, and access to resources. The study highlights the importance of continuous improvement in online educational platforms and pedagogical approaches to address the identified concerns and enhance students' learning experiences. Keywords: Online Education, Student Satisfaction, Distance Learning, Educational Technology, E-Learning
Automatic Medical Dispatcher with Dynamic Telemonitoring System Using IoT in Rural Areas
Authors:   Guru Nanak Institute of Technology, Hyderabad,, Reddy Mrs.G.Praveena
Published: April 2024

Accessing quality healthcare in rural areas can be challenging, often requiring individuals to travel long distances to nearby towns or cities. To address this issue, the Anytime Medical Machine (AMM) project was developed, leveraging Internet of Things (IoT) technology to provide on-the-spot medical consultations. AMM, a part of the Internet of Medical Things (IoMT), is designed for primary deployment in rural areas. Through AMM, individuals can receive consultations from qualified doctors without the need to travel. Doctors can prescribe medications and provide treatment recommendations, reducing both time and expenses associated with travel. The system collects patient data using sensors for temperature, heart rate, and blood oxygen saturation (SPO2), transmitting this information to doctors via a Wi-Fi module. The doctor-patient interaction is facilitated through secure video conferencing, requiring a unique username and password for each user to prevent misuse. After analyzing the patient's health parameters, the doctor prescribes appropriate medication, which is then dispensed through AMM. Patient information is securely uploaded to a cloud server for future reference, and the system is maintained by an administrator who refills medications as needed. Keywords- Rural Healthcare, Remote Consultation, IoT Healthcare, Medical IoT, Telemedicine, Remote Monitoring, Cloud-Based Healthcare, Healthcare Accessibility
Investigating Concrete Properties with Partial Replacement of Cement by Alccofine and Eggshell Powder
Authors:   U.G Students, Department of Civil engineering & Srinivasa Ramanujan Institute of Technology (Autonomous), Anantapur, K Praveen Kumar.
Published: April 2024

In this study, we investigate the effect of substituting cement with Alccofine and eggshell powder on the strength properties of concrete. Cement production is a significant source of carbon dioxide emissions, prompting the search for sustainable alternatives. Our research aims to address this environmental concern by evaluating the potential of Alccofine and eggshell powder as partial cement replacements while maintaining or enhancing concrete quality. We prepared various concrete mixtures with a constant 10% substitution of Alccofine, combined with different proportions of eggshell powder replacing cement (ranging from 0% to 20%). Through thorough testing including compressive, split tensile and flexural strength assessments, we analyzed the mechanical performance of these mixtures. Our findings demonstrate that Alccofine and eggshell powder can effectively substitute cement in concrete, providing a more environmentally friendly option that improves material strength. However, further research is needed to optimize the mix proportions and assess long-term durability in diverse environmental conditions. Notably, combining eggshell powder with Alccofine presents a promising approach to enhancing concrete performance while reducing the environmental impact of cement production. This study contributes to advancing sustainable practices in concrete construction, promoting the use of eco-friendly building materials. Key Words: Alccofine, eggshell powder, compressive, split tensile and flexural strength.
A Review on Crop and Weed Identification Using Deep Learning
Authors:   Dept. of CS&E , JNNCE, Shivamogga, India, S M Dr. Benakappa
Published: April 2024

Agriculture remains the backbone of several economies in the world, especially in underdeveloped countries. With the rapid growth of the population and the increasing demand for food, farmers need to maximize productivity and one possibility is the reduction of losses. Weeds are one of the major dangers in farming. Indeed, they compete vigorously with the crop for nutrients and water. Improved methods are required to get good yields from crops. The proposed model aims to organize a diverse dataset of crop and weed images, leveraging Convolutional Neural Networks (CNN) for crop and weed identification, texture feature extraction, and employing CNN for precise crop and weed identification. The paper introduces a novel deep-learning approach utilizing Residual Neural Networks (ResNet) for effectively identifying and classifying crops and weeds in agriculture. The findings underscore the effectiveness of various deep learning models such as CNNs, in accurately detecting weeds within crops, aided by preprocessing techniques and model optimization. These advancements hold promising prospects for revolutionizing agricultural practices and enhancing productivity in the future. Key Words: Agriculture, Weed Management, Crop Identification, Convolutional Neural Networks, Residual Neural Networks, Deep Learning
VIDEO FORGERY IDENTIFICATION APPLICATION
Authors:   Student (MGIT), Inamdar Meenakshi
Published: April 2024

The rapid advancement of deepfake technology has raised significant concerns about the authenticity of visual content. Deep fake videos, which convincingly superimpose one person's likeness onto another's, can be used for both harmless entertainment and malicious misinformation. Detecting these manipulated videos is crucial for maintaining trust and accuracy in media. In this study, we propose a novel approach for deep fake video detection using deep learning techniques, coupled with a user-friendly graphical user. Our methodology involves training a deep neural network using a large dataset of both real and deep fake videos. These features are then fed in to a series of convolutional and recurrent layers to capture both spatial and temporal information. Keywords: Superimpose, Misinformation, Neural Network, convolutional, spatial.
Fingerprint Identification with Fusion of Gabor and Minutiae Features Using BPNN Classifier
Authors:   dept of Electronics and communication engineering  RVR & JC College Of Engineering, Bhavani Janjanam Lakshmi
Published: April 2024

This study presents an innovative approach to fingerprint identification by leveraging the fusion of Gabor and Minutiae features, employing a Backpropagation Neural Network (BPNN) classifier for accurate categorization. The process involves initial handling of a fingerprint image dataset, including crucial pre-processing steps such as resizing images and implementing morphological operations like dilation, erosion, and opening. Subsequently, Gabor features and minutiae extraction are performed, followed by the fusion of these features to create a comprehensive representation. To address dimensionality concerns, Principal Component Analysis (PCA) is applied. The dataset, comprising the fused features and corresponding labels, is then loaded for the final step - classification using a BPNN. The network is configured for feed-forward backpropagation, distinguishing fingerprint patterns into categories such as arch, left loop, right loop, tented, and whorl. The evaluation metric used to measure the success of the classification process is accuracy. This approach aims to enhance fingerprint recognition by combining distinctive Gabor and minutiae features, ultimately achieving a more robust and precise identification system through the utilization of neural network-based classification. Keywords—BPNN, PCA, Finger Print Images Dataset
Bigmart Sales Prediction Based on Voting Classifier Algorithm and Linear Regression
Authors:   Department of Computer Science and Engineering,  Tirupur, Tamil Nadu, India., B Suresh.
Published: April 2024

Machine Learning is a technology that allows machines to become more accurate in predicting outcomes without being explicitly programmed for it. The basic premise of machine learning is to build models and deploy algorithms that can receive input data and use statistical analysis to predict an output while modifying outputs as the new data becomes available. These models can be used in different areas and trained to match the expectations so that accurate steps can be taken to achieve the organization’s target. In this paper, the case of Big Mart Shopping Centre has been discussed to predict the sales of different types of items and for understanding the effects of different factors on the sales of different items. Taking various features of a dataset collected for Big Mart, and the methodology followed for building a predictive model, results with high levels of accuracy are generated, and these observations can be used to take decisions to improve sales. Key words : Machine Learning, Sales Prediction, Big Mart, Voting classifier algorithm, Linear Regression.
Cardiovascular Disease Prediction from Retinal Images using Machine Learning
Authors:   Electronics and Communication Engineering  R.V.R & J.C College of Engineering,India, G AnilKumar
Published: April 2024

The structure and function of the microvascular are significantly influenced by the key cardiovascular disease risk factors of hypertension and heart attacks. Images taken with a fundus camera can be used to spot irregularities in the blood vessels of retina that indicate the extent injury on blood vessels by hypertension and heart attacks. Using machine learning and AI techniques, detecting the preclinical signs that fall below the threshold of an observer. The proposed methodology aimed to investigate the effects of hypertension and heart attacks on morphological characteristics of retinal blood vessels. With a diagnosis of hypertension and heart attack, data scientists collect retinal images. Interference data is removed— information about structures other than that retinal vasculature using the vessel segmentation method, leaving only morphological details about the blood vessel of retina. Keywords— Heart Disease, Retinal Image, Microvasculature, Hypertension
Evaluating the Feasibility of Digital Twin in Architecture Industry
Authors:   Student, Amity School of Architecture & Planning, Amity University Chhattisgarh, Thakre Shivam
Published: April 2024

This review paper examines the feasibility of adopting digital twin technology in the architecture industry. It examines the current state of digital twin technology, its potential applications, drivers, benefits, and challenges, and compares findings with existing guidelines and standards. The study is based on review literature, including industry reports, surveys, and online articles, due to its rapidly evolving nature. The findings are compared with existing guidelines and standards to identify any alignments, gaps, or contradictions between industry best practices and the literature. The overall assessment indicates that the adoption of digital twin technology in the architecture industry is feasible, but significant challenges and barriers need to be addressed. Key factors supporting the feasibility include growing industry awareness, advancements in enabling technologies, and alignment with existing guidelines and standards. However, challenges include the lack of clear industry-wide standards and protocols, data security and governance concerns, and the need for significant investments in digital infrastructure and organizational change. The study concludes that the feasibility of digital twin adoption in the architecture industry is promising, but successful implementation will require a concerted effort by industry stakeholders to address the identified challenges and align with emerging best practices and standards. Key Words: Digital twin, Virtual asset, BIM integration, Digital infrastructure, Built Environments.
INTEGRATION OF INDUSTRY 4.0 TECHNOLOGIES IN MACHINE DESIGN AND MANUFACTURING
Authors:   Assistant Professor, Department of Mechanical Engineering, Chaitanya Engineering College, Kommadi, Andhra Pradesh, India, Swathi Mudunuri
Published: April 2024

The integration of Industry 4.0 technologies into machine design and manufacturing processes signifies a significant modern industrial shift. This paper explores the implications, challenges, and opportunities associated with this integration. Through literature review and case study analysis, it examines how technologies like IoT, AI, and Big Data Analytics are reshaping machinery production. Key challenges, including data security and workforce upskilling, are addressed alongside integration strategies adopted by industries. The research underscores Industry 4.0's impact on machine design, emphasizing modularity, flexibility, and connectivity. It also evaluates its influence on manufacturing processes, enhancing efficiency, quality control, and smart manufacturing adoption. By identifying trends and future directions, the study offers insights into Industry 4.0's evolving landscape in machine design and manufacturing. This research contributes to understanding the transformative potential of Industry 4.0, guiding future innovations in this critical industrial domain. Key Words: Industry 4.0, Machine design, Manufacturing, Integration.
CHRONIC KIDNEY DISEASE PREDICTION USING MACHINE LEARNING
Authors:   Student, Department of Computer Science and Engineering, Tirumala Engineering College, SAI NANDAM VENKATA KUSUMA
Published: April 2024

The term ―chronic kidney disease‖ means lasting damage to the kidneys that can get worse over time. If the damage is very bad, your kidneys may stop working. This is called kidney failure, or end-stage Kidney disease (ESRD). Kidney disease patients have the potential to get into the chronic phase and chronic kidney disease (CKD) is a decrease in kidney function gradually. So, doctors can diagnose kidney disease patients. So, predicting whether patients with Kidney disease have entered a phase of chronic kidney disease or not by showing the best accuracy result of comparing supervised classification machine learning algorithms. The aim is to investigate machine learning- based techniques for CKD forecasting by predicting results in the best accuracy. The analysis of the dataset by supervised machine learning technique (SMLT) to capture several information like, variable identification, univariate analysis, bi-variate and multi-variate analysis, missing value treatments and analyze the data validation, data cleaning/preparation, and data visualization will be done on the entire given dataset. Additionally, to compare and discuss the performance of various machine learning algorithms from the given hospital dataset with an evaluation classification report, identify the confusion matrix and to categorizing data from priority and the result shows that the effectiveness of the proposed machine learning algorithm technique can be compared with the best accuracy with precision, Recall and F1 Score. Key Words: Chronic kidney, Damage, Univariate, bi-variate, Multi-variate.