bluuebunny/crossref_metadata_2025 huggingface dataset:
Lived Experiences of Suicide-Bereaved Siblings in Pakistan: A Qualitative Approach
Authors: Noureen Amna , Haque Muhammad Anis ul
Published: April 2024

The perspective of siblings who are suicide-bereaved in Pakistan denote a rather underexplored but significant part of mental wellbeing. This qualitative research reconnoiters the feeling of people whose siblings have been lost in the event of a suicide while also aiming to bring light to the different facets of the grieving journey that they have navigated through. Using techniques of thematic analysis, data was collected using semi-structured interviews involving 8 participants who were siblings and belonging to a wide range of backgrounds, each of them offering a different and unique viewpoint that was shaped by their experiences and backgrounds. The analysis discovered seven predominant themes, each of which contained several subthemes. From these, a universal and main aspect of the bereavement process was emotional distress which was interlinked with coping mechanisms and resilience portrayed by siblings while undergoing that journey of grief. Furthermore, posttraumatic growth as a phenomenon came forth as an intricate interconnection of transformation of the individual due to huge loss. The research also revealed the extensive effect of stigmatization in Pakistani society regarding suicide. Nevertheless, the significance of Environmental Reward, complexities of stressed relationships and social support dynamics came forth as important features that shaped the experience of bereavement. The discoveries highlight the need for specialized interventions and support services that cater to individualized needs of individuals in Pakistan who are suicide bereaved. By identifying and authenticating their experiences, officials involved in making policies and mental health professionals can nurture a more effective and sympathetic method for helping those who are currently experiencing loss of a sibling due to suicide. This research contributes important discernments to the increasing literature on the topic of bereavement and emphasizes the importance of cultural bound and contextual helping strategies for catering mental health problems in Pakistan.
Prevalence of Intimate Partner’s Violence (IPV): A Demographical Analysis of Women Residing in Shelter Homes
Authors: Firdos Syeda Sajida , Bukhari Fatima Khurram
Published: April 2024

The study investigated the prevalence of intimate partner’s violence (IPV) through the analysis of key demographical factors among women who sought refuge in shelter homes. This quantitative cross sectional research was conducted on the sample of 110 sheltered women (ages between 16 to 65 years) from the 10 different shelter homes (Dar-ul-Aman) of the Punjab, Pakistan. Sample was drawn through purposive sampling technique.  HITS (Sherin et al., 1998a), was used for assessment of IPV along with a customised demographic sheet and consent form. The quantitative analysis revealed that that the ratio of IPV is significantly higher in arranged/forced marriages as compared to love marriages (p =.042*), additionally, husbands having extra-marital affairs abuse their partners more (p = .049*) as compare to the husbands having no extra-marital affair. It was also revealed that there are no significant difference in geographical boundaries in facing the IPV, both urban and rural women are being the victim of IPV equally (p = .327). Furthermore, it was also evidenced on the basis of significant positive correlation that the ratio of IPV will increase gradually (r = .263**) with the increasing years of marriage, it can be concluded that women in long-term marriages are more prone to IPV and there is a need to address the issue more closely in long term marriages.
Impact of Physical Classrooms Environment and Teaching Methods on Learning Experiences of University Students
Authors: Rasheed Sidra , Shoukat Syeda Lubna
Published: April 2024

The objectives of the study were to find out students’ learning experiences regarding the physical environment of classroom at university level and to determine students’ learning experiences about different teaching methods used in classroom at university level. This study was descriptive and quantitative in nature in which a questionnaire was developed. The population of the study was consisted of three public and private universities of Bahawalpur and Multan. The total sample was 600 students (300 males and 300 females).The sample were selected by using convenient sampling techniques. A questionnaire was self-developed and used as a tool for data collection, and every question was answered in the form of multiple responses. Most of the respondent agreed that the proper sitting space is most suitable for their work in their classroom, and most agreed that the lecture method is most effective method   in their learning. It was suggested that the learning of students could be enhanced by providing a better physical classroom environment and using innovative and modified teaching methods in classroom during teaching learning process.
The Impact of Entrepreneurial Bricolage on Sustainable Entrepreneurship: Exploring the Role of Frugal Innovation
Authors: Imran Ali , Iqbal Jawad
Published: April 2024

This study aims to investigate the determinants that impact sustainable entrepreneurship within the entrepreneurial community in Pakistan. The convergence of globalization and increased consciousness regarding sustainable development has generated tremendous ambition, zeal, and curiosity regarding the course of sustainable entrepreneurship. With these conditions, the study's goal is to investigate potential predictive factors that promote sustainable entrepreneurship among Pakistani entrepreneurs. This research investigated the relationship between entrepreneurial bricolage and sustainable entrepreneurship by employing a frugal innovation as mediators. Through the use of structural equation modelling with partial least squares, 392 responses to a Pakistani survey instrument were analyzed. The findings of this study suggest that there is a relationship between entrepreneurial bricolage and the level of sustainable entrepreneurship. This relationship is mediated by the function of frugal innovation. This research emphasizes the significance of sustainable entrepreneurship in light of the increased environmental and societal consciousness among consumers, as well as global regulations pertaining to environmental safeguarding.
Examining the Regression Analysis of Teacher Acceptance on School Children's Self-Esteem
Authors: Sarfaraz Beenish , Malik Anila Amber , Nadeem Rimsha
Published: April 2024

This paper aims to explore the perceived teacher's acceptance and its effects on the self-esteem of school children. The teacher's role is very important in the social, emotional, and academic achievement of children. The sample was collected from Hyderabad schools in January 2018. This study was performed on a total sample of 500 students. Sample was justified through A-priori sample size calculator. Respondents range in age range from 10 to 18 years (Mean = 13.49 years, SD = 1.33). Three questionnaires were employed to collect the data. Teacher acceptance-rejection TARQ (Rohner, Rohner, & Khaleque, 2005), Personality Assessment Questionnaire PAQ (Rohner et al., 2005) Urdu version were administered to participants and convenient sampling was used for data collection. Regression analysis survey has been used in the present study. The result of the regression analysis revealed that students who tended to perceive themselves to be accepted by their teacher reported high self-esteem. Hence it is clear that teacher acceptance is the significant predictor of student self-esteem. This finding is useful for teachers' preparation, and professional development as well as for the promotion of teacher acceptance.
RELAÇÕES CONCEITUAIS ENTRE O BUILDING INFORMATION MODELING E O LEAN NA GESTÃO DA INFORMAÇÃO DA CONSTRUÇÃO CIVIL
Authors: Medeiros de Oliveira Bruno , Traguetto Silva Felipe , Gondim do Amaral Tatiana
Published: April 2024

O Building Information Modeling (BIM) integra, dentre outras funcionalidades, o planejamento e a construção com suas tecnologias. E o pensamento do Lean Construction busca otimizar a produção e facilitar, reduzir custos a gestão da informação. Embora os dois sejam conceitualmente independentes, existem sinergias entre eles que se estendem além da natureza essencialmente circunstancial de sua maturidade que se aproxima contemporaneamente. Esta pesquisa investiga as interações entre o Lean e o BIM, por meio de uma Revisão Sistemática da Literatura (RSL).  Neste sentido, acredita-se que este diagnóstico revele tendências de pesquisa existentes bem como desafios e oportunidades para trabalhos futuros.  A relevância dessa pesquisa se dá pela baixa quantidade de artigos identificados nos últimos 10 anos sobre o tema, o que se intensifica com a constante busca por maior controle de custo, prazo e qualidade na indústria da construção. Com a RSL foi possível classificar as lacunas do conhecimento em cinco grupos, além de poder separar o tema dos artigos por fase do ciclo de vida da construção. Esta   pesquisa   contribui   para   ampliação   das   fronteiras   do conhecimento ao analisar o estado da arte e identificar tendências de pesquisa.
ESTUDO DO USO DAS FICHAS DE VERIFICAÇÃO DE SERVIÇO DIGITAL EM EMPRESAS GOIANIENSES
Authors: Eterna Taveira e Silva Larissa , Viana de Sousa Talyssa , Venâncio da Silva Victor , Gondim do Amaral Tatiana
Published: April 2024

Nos últimos anos, especialmente no atual contexto de pandemia, observa-se uma evolução no interesse sobre a utilização de tecnologias digitais em canteiros, a exemplo do crescimento de ações voltadas à adoção do BIM, as iniciativas visando à capacitação profissional para a realização de projetos e obras, e as discussões em torno da utilização integrada das tecnologias digitais. A Ficha de Verificação de Serviço (FVS) Digital é uma dessas tecnologias, sendo uma ferramenta de controle e gestão dos serviços executados mais efetivamente se comparada com as fichas tradicionais. Assim, o objetivo deste trabalho é estudar, por meio de entrevistas via Google meet e visitas nos canteiros, o uso desta tecnologia por algumas empresas goianienses, levantando as motivações, dificuldades, adaptabilidade e os ganhos que elas tiveram ao implantar a FVS digital nos seus canteiros. Analisando as respostas dos entrevistados tanto nas reuniões online quanto nas visitas nas obras, é notável que a FVS digital consegue resolver muitos dos problemas de controle e gerenciamento dos serviços executados em obra, como a agilidade das inspeções, a facilidade de visualização e controle dos dados obtidos e a diminuição no volume de papel gerado, mas ainda há alguns conflitos em relação à tecnologias e seus usuários e o setor, como a problemas na assimilação da ferramenta por alguns usuários e o preenchimento das plataformas em si.
DESENVOLVIMENTO DE ADD-IN PARA CONTROLE DA INFORMAÇÃO EM MODELOS BIM
Authors: Pinto de Cerqueira Tiago , Martins da Silva Gusmão Gustavo , Gomes de Oliveira Brandstetter Maria Carolina
Published: April 2024

O uso do BIM tem crescido e consolidado no meio da Construção Civil e muitos países já têm exigido seu uso para conceder licitações em obras públicas. O crescimento do BIM vem acompanhado de tentativas de padronizações por parte de organizações internacionais, órgãos públicos e universidades. A padronização dos projetos tem ajudado a tornar os mesmos acessíveis a quem o leia, independentemente da localização geográfica ou disciplina (por exemplo: ar condicionado, estrutura, eletricidade, entre outros). Entre os vários itens a serem padronizados, buscou-se desenvolver uma ferramenta que permitisse a padronização da nomenclatura, que é o único parâmetro comum e obrigatório a todos os modelos BIM. O objetivo do trabalho é apresentar um processo de modelagem paramétrica por meio da solução de um Add-In para o software Revit®, capaz de controlar este fluxo de informações. Para atingir esse objetivo, utilizou-se programação e banco de dados em conjunto com a plataforma BIM da Autodesk, o Revit®. Dentro da ferramenta desenvolvida, o nome foi dividido em partes menores que possuem um valor definido de acordo com seus parâmetros, tornando o nome dos elementos também a sua identidade. Essa padronização tornará os modelos não só legíveis para os usuários como também a outros softwares, por meio da vinculação com as várias ferramentas do mercado da construção, como ERPs e ferramentas de planejamento.
The Unfinished Revolution?
Authors: Ciarkowski Błażej , Miarczyński Maciej
Published: April 2024

The future reconstruction of Ukrainian cities from wartime devastation will require an extensive discussion on strategies and concepts of preservation of cultural heritage – including the heritage of the Modern Movement. It should involve not only the technical aspects but political and historical issues as well. The history of the Palace of Culture of the Railway Workers in Kharkiv (architect Alexander I. Dmitirev, 1927-1932) provokes a number of questions on the essence of Ukrainian pre-war Modernism and Constructivism, the idea of the Sovietisation of the theater against the Great Theatre Reform movement or the role of Workers’ Clubs and Palaces as social condensers. Thus, its analysis cannot by limited to the form and content of the edifice itself, but should be perceived in the broader context of similar projects (for example the Kharkiv Opera House) and views on architecture in the 1920s and 1930s.The Palace of Culture in Kharkiv can be considered as an example of “architecture in transition” where evolving trends in art as well as a dynamic socio-political situation left their marks and created a multi-layered palimpsest. Dmitriev’s design included the Constructivist spirit as well as conservative monumentality. It seems to become a legacy of a revolution (in architecture, theater and society) which has never been really completed.
Concreto Academy kicks off
Authors: Concreto Academy
Published: April 2024

The Concreto Academy has officially launched its mission to advance modern concrete heritage conservation with an impactful kick-off meeting held on March 6th at the Town Hall of the City of Ivrea. The event, attended by a diverse and enthusiastic audience, marked the beginning of an ambitious Erasmus Plus funded project aimed at preserving architectural heritage through innovative approaches.
Corroded Stagnancy: A Literary Testimony
Authors: Alva Nayantara
Published: April 2024

This piece navigates the long term post-traumatic impacts of an acid attack victim in India.

In South Asia, the prevalence of reported acid attacks – or the throwing of concentrated acid on another person with the intention to disfigure or kill – remains high, despite strict penal codes against the crime. According to Acid Survivors Trust International, 80% of reported cases globally have been against women. 

I had the privilege of speaking to a remarkable woman who survived one of these attacks. The physical, psychological, and social elements of this work come from her story.
Creation of a ‘New Species’: A Decolonial Reading of Frantz Fanon and B.R Ambedkar
Authors: Siri Murthy Srijana
Published: April 2024

This paper argues that Fanon’s critique of colonisation and Ambedkar’s evaluation of the caste system share similarities because they collectively present a psycho-social understanding of the oppressed human condition. Both Fanon and Ambedkar argue that colonial and caste oppression has introduced a loss of cultural legacy, identity and capacity for self-determination. This loss is imprinted so deeply in the minds of the colonised and stakeholders of the caste system that a new species with a different social, political and economic consciousness is needed to transform and facilitate the formation of radical new social beliefs. For such a decolonisation of the mind to occur, Fanon proposes that violence—the capacity to resist and question—is fundamental, which I argue is also the foundation of Ambedkar’s solution to annihilate caste. However, violence in terms of Fanon and Ambedkar does not translate to physical harm. It is the intellectual capacity and desire for self-reflection that ruptures existing hierarchical power structures and renews the meaning of being human in democratic civilisations.
Sijo as Affective Refuge: Chosŏn Court Politicians’ Emotional Community in Poetry
Authors: Ramirez Andrew
Published: April 2024

This paper aims to analyze the prevalence of negative emotions in Sijo written by aristocratic politicians of the Joseon court. To analyze these emotions, I will use two analytical frameworks: first, the analysis of emotions from the perspective of Neo-Confucianism and, second, the conception of “emotional communities” as put forth by scholar Barbara H. Rosenwein. By using these two frameworks, I aim to reconcile the tension between the prevalence of such negative emotions in the politicians’ poetry and the Confucian notion that such negative emotions are strictly contrary to the balance of the polity and must be sublimated.
Virtual research laboratory in foreign language teaching: Strategies and role
Authors: Wu Ting
Published: April 2024

With the growth of technology and the availability of virtual tools, the creation of interactive and dynamic learning environments that increase students’ interest and motivation becomes relevant and possible. The study aimed to analyse and evaluate the effectiveness of using virtual laboratories in the process of learning foreign languages. Analytical research methods, generalisation, systematisation, and surveying were used in the research. The study demonstrated that virtual reality (VR) could change the traditional approach to language learning through immersive experiences of students’ interaction with language structures. The article highlighted technical and pedagogical challenges that require an integrated approach. The study confirmed that mobile learning and augmented reality (AR) offer new education opportunities, providing interactivity and personalisation. The research concluded that augmented reality could improve the learning of complex concepts and increase student motivation. This article highlighted a need for effective pedagogical strategies for the successful implementation of virtual reality and augmented reality. A survey of students at Northwest Normal University was also conducted. The survey of students who use virtual labs to learn foreign languages indicates that they are more effective and useful for the learning process, including the use of Duolingo, Rosetta Stone, Babbel, Memrise, Busuu, Lingodeer, BBC Languages, and Italki. Although the majority of students have a positive attitude towards this technology, some prefer traditional methods, which indicates the need for additional adaptation and support of virtual resources to ensure their optimal effectiveness. The study findings can be used to adapt existing foreign language curricula and courses to the use of virtual research laboratory tools
The use of Class Dojo in the preparation of future primary school teachers for the organization of distance learning
Authors: Bilyk Yulia
Published: April 2024

The urgent transition to distance learning has necessitated the preparation of teachers to organize the educational process based on the use of information and communication technologies in emergency situations. Therefore, research on the process of preparing future elementary school teachers for distance learning in higher education institutions using learning management systems, particularly ClassDojo, is relevant. The purpose of the study was to investigate the effectiveness of implementing the ClassDojo platform in preparing teachers to organize distance learning in elementary schools. The methodological basis of the research consisted of basic theoretical (analysis, synthesis, comparison, abstraction, and specification), empirical (observation, surveys, diagnostic practical work methods, pedagogical experiment), and statistical methods (variation characteristics, Student's t-test, Pearson's criterion). The article describes the main characteristics, functions, interface features, and advantages of using ClassDojo in the educational process of elementary school, namely: its positive impact on shaping behaviour, learning motivation, value orientations, character, and emotional intelligence of younger school students. The main ways of preparing future elementary school teachers to use distance learning platforms in their professional activities in higher education pedagogical institutions are presented. Examples of preparing students specializing in “Elementary Education” for organizing distance learning through practical work using the ClassDojo learning management system are described. The results of the research on the effectiveness of using the ClassDojo learning management system in preparing students specializing in “Elementary Education” at the Communal Higher Education Institution “Barsky Humanities and Pedagogical College named after Mykhailo Hrushevsky” are presented, analysed, and summarized. The effectiveness of the teaching methods used by education seekers is statistically verified. The research results demonstrate the effectiveness of the forms, methods, and techniques used in preparing teachers to organize distance learning in elementary school. The practical significance of the research results lies in their applicability by higher education institutions in the professional training of future teachers to organize distance learning and by practising teachers of elementary school classes
Effect of Environmental Disclosure on Cost of Equity of Listed Consumer Goods Firms in Nigeria
Authors: C. A. Dorathy , E. A. Patrick , E. I. Precious
Published: April 2024

As concerns about climate change, pollution and resource scarcity intensifies, stakeholders are placing greater emphasis on the environmental practices of organisations. Based on this, the study examined the effect of environmental disclosure on the cost of equity of listed consumer goods firms in Nigeria. Ex-post facto research design was adopted, and panel data covering ten (10) years (2013-2022) were collected across eighteen (18) listed consumer goods firms in Nigeria which formed the sample size of the study. The data collected were analysed using panel multiple regression analysis via E-views 10.0 statistical package. The study findings revealed environmental risk disclosure (Coeff. = -0.0269{0.0107}) and waste management disclosure (Coeff. = -0.0178{0.0009}) have significant negative relationships on cost of equity (COE) of listed consumer goods firms in Nigeria while greenhouse gas emission disclosure (GGED) has an insignificant negative effect (Coeff. = -0.0075{0.3966}) on cost of equity (COE) of listed consumer goods firms in Nigeria. It was thus concluded that environmental accounting disclosure plays a crucial and significant role in shaping the cost of equity of listed consumer goods firms in Nigeria. The study recommended, amongst others, that regulatory bodies and industry associations should advocate for the integration of robust waste management disclosure strategies within corporate reporting frameworks to mitigate environmental impact and promote sustainable business practices.
Online Reviews Sentiment Analysis Based on High-Rise Residential Projects in Malaysia
Authors: Ahmad Taufik Nursal
Published: April 2024

The Malaysian property industry has suffered due to increasing numbers of unsold properties, especially high-rise residential types. Industry practitioners believed the absence of accurate market data for developers caused a mismatch where the developer failed to provide the right properties for the market demand. Thus, a crucial need to understand public opinion on sentiment and preferences for high-rise property. This study derived an online-reviews-based process for evaluating public sentiment and preferences to understand the reason behind the imbalances of supply and demand currently faced by the property industry in Malaysia. The present study introduced an alternative approach to the evaluation of public sentiments and preferences towards high-rise residential via sentiment analysis and interpreted the cause of positive and negative opinions via Term Frequency-Inverse Document Frequency (TF-IDF) analysis. To demonstrate the applicability of the proposed approach, an experiment based on multiple high-rise properties as a case study. The findings are expected to be employed by developers/government in the evaluation of their weaknesses and strengths on the high-rise properties project and how developers/government can improve certain areas.
Design and Implementation of Automatic Music Composition System Based on Deep Learning
Authors: Wang Fei
Published: April 2024

Recently, there has been a notable increase in interest surrounding the utilization of Deep Learning Approaches for AMCS in recent times. However, current methods often lack coherence and authenticity in the generated music. This study proposes a new approach that utilizes RNNs to bridge the gap between traditional compositional methods and modern deep learning techniques. The goal is to produce expressive and coherent musical pieces. Our methodology involves designing and implementing a customized RNN architecture that can effectively capture the complex temporal dependencies present in musical sequences. We experiment with different types of including LSTM networks, RNNs, and GRUs, to overcome challenges such as vanishing gradients and better model longer-term dependencies in music. To train the neural network efficiently and improve model convergence, various deep learning optimizers are utilized in our system. Specifically, we use SGD optimizer to improve the hyper parameters of LSTM and GRUs. The data for training is converted into MIDI format and analyzed, with music lines being identified through a similarity matrix technique. The MIDI data is then prepared for use in the LSTM and GRUs networks. The resulting music is assessed using both objective measures, such as mean squared error, and subjective approaches. This research adds to the advancements in automatic music composition by demonstrating the capability of RNNs in capturing and producing complex musical arrangements.
Automatic Translation of English Terms for Computer Network Security Based on Deep Learning
Authors: Wei Jia, Cuiping Shi Xuan Zhou,
Published: April 2024

Translation computer network security refers to the protection of sensitive information during the transmission of data across networks in different languages. With the increasing globalization of businesses and communications, the need for secure translation services has become paramount. Security measures such as encryption, authentication, and data integrity verification play a crucial role in safeguarding translations from unauthorized access, interception, or tampering. Additionally, the use of secure communication protocols and encryption algorithms ensures that data remains confidential and protected from cyber threats. This paper presents an automatic translation approach for English terms related to computer network security, leveraging deep learning techniques with Cryptographic Hashing Authentication Classification (CHAC). The proposed framework aims to enhance the accuracy and efficiency of translating security terms across different languages, facilitating effective communication and collaboration in cybersecurity contexts. Through simulated experiments and empirical validations, the effectiveness of the CHAC-enhanced deep learning model is evaluated, demonstrating significant improvements in translation accuracy and performance. For instance, the CHAC model achieved an average accuracy rate of 90% in translating security terms, outperforming traditional translation methods by 20%. Additionally, the framework reduced translation time by 30%, streamlining the process of generating multilingual security documentation and communications. These results underscore the potential of deep learning with CHAC in automating the translation of English terms for computer network security, enhancing global cybersecurity efforts and facilitating cross-cultural collaboration.
Enhancing Diagnostic Accuracy: A Hybrid Approach to Mitigate Speckle and Rician Noise in Medical Images
Authors: Naveen Kumar Jayakumar
Published: April 2024

This research explores the detrimental effects of Rician and speckle noise in medical imaging, particularly within MRI and radiographic modalities. Such noise can significantly obscure critical details, potentially leading to misdiagnoses and inaccurate measurements. Rician noise candidly dangles on the signal strength. Removing this noise affects the origial information in image that makes this noise removal perplexing. On other hand speckle noise is visualised like granular clog in ultrasound imageries. It creates ambiguous details and edges in the images.   To address this challenge, the study introduces a novel hybrid method implemented in Java. The method employs a frequency-based decomposition approach for speckle noise and separates images into magnitude and phase components for Rician noise. This allows for targeted filtering, utilizing customized versions of median and Gaussian filters, to effectively remove noise while preserving crucial image features. The proposed method was rigorously assessed using 200 contaminated brain and lung MRIs and radiographs, demonstrating superior performance compared to traditional filters based on crucial system of measurement like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Squared Error (MSE). This research presents a promising solution for enhancing the quality and reliability of medical images, potentially leading to improved diagnostic accuracy and patient outcomes.
Revolutionizing Agriculture IoT and Random Forest Algorithm for Crop Monitoring and Disease Prediction
Authors: M. A. Sayyad,
Published: April 2024

This research about presents a groundbreaking approach to revolutionize farming through the integration of Web of Things (IoT) innovation and progressed machine learning calculations. Centering on the improvement and execution of an IoT-based edit checking framework coupled with the Random Forest calculation for malady expectation, the ponder points to improve agrarian hones and relieve trim misfortunes caused by infections and natural components. Real-time information collection from IoT sensors sent in rural areas empowers comprehensive checking of vital natural parameters such as temperature, mugginess, soil dampness, and light concentrated. The Random Forest calculation analyzes this information to precisely foresee trim maladies, giving ranchers with significant bits of knowledge for proactive illness administration. Test comes about illustrate the adequacy of the proposed approach, with the Random Forest show accomplishing an exactness of 92%, exactness of 93%, review of 91%, and F1-score of 92%. These comes almost defeat customary methodologies and existing explore works, highlighting the potential of IoT and machine learning for optimizing alter proficiency and ensuring around the world food security.
Transfer Learning for Object Detection in Remote Sensing Images with YOLO
Authors: A. Devi
Published: April 2024

This investigation explores exchange learning methodologies for protest locations in inaccessible detecting pictures utilizing the YOLO design. Four unmistakable exchange learning calculations, specifically YOLO Fine-Tuning, Highlight Extraction, Space Versatile Preparing, and Knowledge Distillation are investigated and assessed on an assorted dataset. The tests illustrate noteworthy advancements in location execution, with YOLO Fine-Tuning accomplishing an exactness of 0.85, review of 0.78, F1 score of 0.81, and mean average precision (mAP) of 0.75. Highlight Extraction grandstands competitive comes about, with an accuracy of 0.87, a review of 0.80, an F1 score of 0.83, and a mAP of 0.78. Domain Adaptive Training exhibits predominant execution, accomplishing an exactness of 0.89, review of 0.82, F1 score of 0.85, and mAP of 0.80. Information Refining yields promising results, with a precision of 0.88, review of 0.81, F1 score of 0.84, and mAP of 0.79. These discoveries highlight the viability of exchange learning algorithms in upgrading the adaptability and precision of YOLO for protest locations in diverse inaccessible detecting scenarios. The study contributes important bits of knowledge to the field of further detecting, emphasizing the viable appropriateness of tailored exchange learning techniques for real-world applications.
A Novel Approach for Gender Prediction through Fingerprint Analysis using Fuzzy C-Means Algorithm and Artificial Neural Network
Authors: Chandrakant P. Divate
Published: April 2024

Wireless Sensor Networks (WSN) is a group of Sensor Nodes (SNs) which are performed to intellect a normal singularity from an environment. The minimum resources and computational power of WSN creates vulnerable to number of security attacks. To addresses this problem, this paper proposes a Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm (M-TDWPOA)-based secure and energy aware multi-hop routing in WSN with the Base Station (BS). The proposed method comprises of two significant steps: Initially, dynamic energy-efficient Cluster Head (CH) selection by utilizing the multi-objective functions such as Distance between neighbor nodes, Distance between BS to CH, Energy, Centrality and Trust Threshold. Then, dynamic secure multi-hop routing is selected by the multi-objective functions such as Distance between BS to CH, Energy, Trust Threshold which is dynamically reducing the network overhead. Furthermore, a security aware multi-hop routing is employed through the different trust classes like direct, indirect, recent and authentication. The proposed M-TDWPOA approach is estimated with various performance metrics like alive nodes, dead nodes, energy consumption, Throughput and so on. The proposed M-TDWPOA achieves the minimum energy consumption of 0.4927J, 0.4851J, 0.4771J, 0.4693J and 0.4617J when compared to the existing methods like Fractional Artificial Lion (FAL).
Evolving Landscape Of E-Commerce, Marketing, and Customer Service: the Impact of Ai Integration
Authors: Kim Oanh Vo Thi
Published: April 2024

Background: In today's digital age, consumers interact with brands through various channels, including websites, social media, and mobile apps. This has led to the rise of "digital consumers" with distinct cognitive skills and expectations. Businesses are constantly seeking ways to improve customer experience (CX) and stay competitive.

Objective: This article explores how artificial intelligence (AI) is transforming the digital consumer experience.

Methods: A qualitative research approach was employed, utilizing literature reviews, expert interviews, and case studies to gain insights into the application of AI in customer interactions and its impact on the digital customer journey.

Findings: The research found that the integration of AI in digital consumerism has created new trends in business, enabling personalized and dynamic interactions between consumers and brands. AI-powered tools like recommendation systems, chatbots, and virtual assistants blur the lines between pre-purchase, purchase, and post-purchase stages, offering a more seamless customer experience.

Conclusions: The integration of artificial intelligence and digital customer experience has the potential to transform the future of e-commerce, marketing, and customer service, opening new horizons for both businesses and consumers. However, challenges related to data privacy, ethics, and algorithmic biases need to be addressed.

Originality/Significance: This article presents an original contribution to the understanding of the evolving digital consumer experience and the impact of AI on this landscape. While the use of AI in various aspects of business has been explored
RMDEASD: Integrating Rule Mining and Deep Learning for Enhanced Aspect-Based Sentiment Analysis Across Diverse Domains
Authors: Sonali Ridhorkar Tabassum Khan,
Published: April 2024

The rapidly evolving landscape of Aspect-Based Sentiment Analysis (ABSA) in the realm of natural language processing necessitates innovative approaches to comprehend and interpret the intricate nature of sentiments expressed in textual data. Traditional ABSA methods have often struggled with the nuanced sentiments inherent in various textual sources, limited in their ability to adapt to domain-specific vernacular and context. This study introduces a novel approach that synergizes rule mining with advanced deep learning techniques, aiming to address these limitations and enhance the precision and contextual understanding in sentiment analysis. Our proposed model integrates rule-based systems with deep learning Transformers, a method recognized for its effectiveness in extracting structured, domain-specific insights. This integration results in a significant enhancement in the model's ability to capture nuanced sentiments, as demonstrated by an 8.5% increase in aspect-based sentiment analysis precision and an 8.3% improvement in accuracy over existing methods. The model employs a combination of techniques including Bi-LSTM, BiGRU-CRF with RNNs, and ontology mapping operations, forming a Hybrid Multi-Level Architecture that effectively captures both sequential data relationships and semantic context. Additionally, the model incorporates Cross-Domain Transfer Learning, utilizing BERT-based pre-trained models with added layers for contextual semantics, which has shown notable generalization capabilities across various domains. Furthermore, this study refines evaluation parameters, tailoring metrics such as domain-specific accuracy, recall, and precision to more accurately assess model performance in particular domains. This is especially pertinent in our analysis of Twitter datasets specific to industries like finance and healthcare. The inclusion of Temporal Dynamics and Aspect Summarization, using techniques like the VARMAx process, provides insights into the evolution of sentiments and aspects over time, an aspect crucial for longitudinal sentiment analysis. The comprehensive testing of our model on diverse spatial and temporal datasets reveals not only improved precision and accuracy in sentiment analysis but also a reduction in delay, demonstrating its efficiency and responsiveness. The study's findings indicate that our approach could significantly impact fields reliant on sentiment analysis, such as market analysis, public opinion research, and social media monitoring, providing a more nuanced and accurate understanding of consumer sentiments and trends. This paper's contribution lies in its innovative amalgamation of rule-based and deep learning techniques, tailored evaluation metrics, and its emphasis on temporal dynamics, setting a new precedent in the field of ABSA and opening avenues for further research and application in real-world scenarios.
ML-CPC: A Pathway for Machine Learning Based Campus Placement Classification
Authors: Anuj Gupta,
Published: April 2024

The intense competition among students for a limited number of job opportunities poses a significant challenge to campus placements. There are various strategies that organizations can employ to tackle this issue. Primarily, it is essential to provide high-quality educational programs and opportunities for professional development that align with current market needs. This involves regularly updating the curriculum, integrating sector-relevant projects, and facilitating hands-on training experiences. Campus placements play a crucial role in evaluating an institution's caliber and ensuring the employability of its students. Institutions can enhance their placement records by implementing innovative solutions to challenges encountered in placements, such as intense competition and economic fluctuations. This requires a proactive strategy, collaboration with businesses, a focus on skill enhancement, and support for students' soft skills and professional development. By implementing these corrective measures, institutions can contribute to students' future success by better preparing them for the workforce. The primary objective of this paper is to conduct an exploratory analysis of the recruitment dataset. The application of supervised machine learning is employed to predict whether a student was placed, utilizing classification models. The proposed approaches and methods surpass all other machine learning models, achieving a recall value of 1, accuracy of 0.9524, precision of 0.8667, and an F1 score value of 0.9286 to address the ensemble architectural.
Integrating IoT and Artificial intelligent for Enhanced Electric Vehicle Charging and Autonomous Driving for sustainable transportation
Authors: Y.Madhu Sudhana Reddy
Published: April 2024

This research explores the confluence of Internet of things and artificial intelligence towards fostering electric vehicle charging and autonomous driving for sustainable transportation. By meticulously collecting and preprocessing, and executing multiple machine learning models such as Artificial Neural Network , Decision Trees , Naive Bayes , and Random Forest , the research delves into charging infrastructure and easy autonomous driving. Particularly, real-time datasets, such as EV station data, road geography and mapping data, weather and telematics data, were properly collected and processed . Then, the preprocessing stage, which included data cleaning, normalization, and feature engineering had improved the datasets’ predictability. Consequently, the datasets such as charging recommendation, predicting energy use, predicting of optimal route, had produced highly accurate training and testing for forecasting elelctric vehicles’ demands. The ML models indicated their proficiency in predicting not only the optimal charging schedules, but also predictive energy consumption, and predictive routes. Specifically, ANN with 0.945 held the highest precision, while DT 0.912, NB 0.887, and RF 0.819 were almost identical . Likewise, the recall scores, F1 scores, and AUC ROC scores also coincided the prized outcome of the models. The findings of the research are groundbreaking for both transportation sector, policymakers, as well as urban planners and stakeholders at large. Following the data-fed recommendations would not be excessively challenging, and they would yield favourable results whether the stakeholders are dealing with EV charging infrastructure, or dealing with traffic congestions, or are establishing their own sustainable solution for future mobility via not just electric vehicles but also other tools and devices. Hence, we are hopeful that our research serves as a lighthouse for the transportation domain towards a greener future.
Improving Routing Performance in Mobile Ad Hoc Networks Using Artificial Neural Networks for Mobility Prediction using deep learning
Authors: Mohammad Arif
Published: April 2024

In this study, a research was done to examine the effectiveness of deep learning models, such as the Artificial Neural Networks, Convolutional Neural Networks, Gated Recurrent Units and Generative Adversarial Networks, to boost routing’s efficiency and performance in the Mobile Ad Hoc Networks through mobility prediction. In total, 10 experiments were conducted, and ANNs demonstrated an average prediction accuracy of 0.86, MAE of 0.10, RMSE of 0.16, and correlation coefficient of 0.93. CNNs showed the most impressive performance, featuring the following indicators: the average prediction accuracy of 0.87, MAE of 0.09, RMSE of 0.15, and the correlation coefficient of 0.94. GRUs, in their turn, displayed quite decent performance, with an average prediction accuracy of 0.87, MAE of 0.10, RMSE of 0.16, and the correlation coefficient of 0.94; in the meanwhile, GANs can be also seen as offering a decent performance, with the average prediction accuracy of 0.87, MAE of 0.10, RMSE of 0.16, and the correlation coefficient of 0.94. In the end, the identified findings imply that deep learning models can be used to enhance the routing efficiency in MANETs, since they can predict the node movement with the high level of accuracy. The identified changes are possible to lead to the creation of effective wireless communication networks.
Impact of Network Size on Routing Protocols Performance in Wireless Sensor Network: A Simulation
Authors: Prasanta Pratim Bairagi
Published: April 2024

Wireless sensor networks (WSNs) are a decentralized kind of network which is equipped with sensor nodes and is rapidly being utilized to intelligently monitor different environmental conditions at a minimal cost. Sensor nodes are often battery-powered and the essential issue to address is how to decrease node energy consumption so that the network lifetime may be prolonged to realistic lengths. Routing is one of the primary operations performed within the network which consumes a sufficient amount of energy. Therefore, it is very much essential to consider the finest routing protocol so that it consumes less energy. In this article, we look at several routing protocols used in WSNs, including AODV, DSDV, DSR, OSLR and M-GEAR. Furthermore, we studied the execution of selected protocols based on network factors such as network latency, packet delivery ratio, and network throughput against multiple network sizes. The simulation findings show that protocol performance varies depending on network size and that selecting the optimum protocol based on network parameters is also crucial.
A Predictive Modeling for Characterization and Grading of Knee Osteoarthritis Using Machine Learning Algorithms: A Study in Early Diagnosis and Prognosis
Authors: Geeta R. Bharamagoudar Malathi S. Y.,
Published: April 2024

Osteoarthritis (OA), particularly knee osteoarthritis, is the most prevalent form of arthritis, resulting in severe dis-ability for sufferers throughout the world. A Manual diagnosis, segmentation, and annotating joints of knee continue to be the most used procedure for diagnosing osteoarthritis (OA) in clinical settings, despite being laborious and highly susceptible to user variation. Several prediction models displayed prognostic ability in ways of predicting the possible onset of OA, the potential aggravation of OA, the prospective progression of pain and structural deterioration as well as the potential occurrence of total knee replacement (TKR). Apart from research gaps, techniques of machine learning continue to demonstrate enormous potential for challenging tasks e.g., initial knee OA detection and recognition of further disease events, also basicthings such as identifying innovative imaging features and establishing a novel measure of OA status. Future OA treatment discoveries may be aided by the continuous improvement of machine learning models.