bluuebunny/crossref_metadata_2025 huggingface dataset:
PENYULUHAN HUKUM DAN BAHAYA NARKOBA
Authors: Satrio Reza
Published: February 2025

Saat ini narkoba merupakan suatu hal yang dianggap sangat membahayakan oleh masayrakat dan pemerintah. Penyalahgunaan serta peredaran gelap narkotika, psikotropika serta bahan berbahaya lainnya dengan berbagai implikasi dan dampak negatifnya merupakan suatu masalah global yang mengancam kehidupan masyarakat, bangsa dan negara. Selain itu kurangnya kesadaran hukum didalam lingkungan masyarakat juga menjadi salah satu faktor yang menjadikan penyalahgunaan narkoba ini terjadi. Berdasarkan permasalahan-permasalahan tersebutlah kelompok KKN Program Studi Hukum menyelenggarakan kegiatan Penyuluhan Hukum dan Bahaya Narkoba untuk memberikan pemahaman lebih jauh terkait bahaya narkoba ini dikalangan masyarakat terkhususnya remaja. Metode yang digunakan didalam kegiatan Pengabdian Masyarakat ini ialah masyarakat dituntut aktif untuk mengikuti kegiatan Penyuluhan ini secara langsung, selain itu juga masyarakat diharapkan dapat memahami materi yang disampaikan. Kompetensi yang dibentuk ditandai dengan indikator peningkatan pengetahuan peserta mengenai pencegahan penyalahgunaan narkotika dan perubahan sikap dengan berpartisipasi dalam pencegahan penyalahgunaan narkoba. Kegiatan Penyuluhan Hukum dan Bahaya Narkoba ini dilaksanakan di Aula Desa Cikahuripan.
SOSIALISASI TAMAN EDUKASI GUNA MENUMBUHKAN MINAT BACA ANAK DI DESA CIKAHURIPAN
Authors: Satrio Reza
Published: February 2025

Membaca merupakan salahsatu aktivitas yang dapat memperluas Ilmu Pengetahuan. Melalui kegiatan membaca, seorang anak dapat mengeksplorasi dirinya. Namun pada kenyataan di lapangan, minat baca yang rendah masih menjadi permasalahan yang banyak ditemukan pada saat ini. Minat baca anak di Indonesia termasuk dalam kategori yang memprihatinkan. Desa Cikahuripan termasuk daerah di Kecamatan Cisolok yang harus ditingkatkan lagi minat bacanya. Diperlukan sarana dan prasarana yang dapat memadai anak dalam kegiatan membaca. Oleh karena itu, hadirnya Taman Edukasi dalam kegiatan pengabdian kepada masyarakat ini sebagai jembatan untuk menumbuhkan minat baca anak di Desa Cikahuripan. Selain dapat menumbuhkan minat baca, program kegiatan Taman Edukasi ini dapat meningkatkan kemampuan lingustik, psikomotorik dan sosial anak.
PEMBUATAN DAN PEMASANGAN PAPAN PLANG NAMA JALAN DI DESA BANGUN HARJA
Authors: Nanik Lestariningsih  , Kharisma  , Risky Amelia  , Muhammad Syairazi  , Lisa Solikhatin Sumantri  , Siti Dila Nuratih
Published: February 2025

Pengabdian kepada masyarakat merupakan salah satu program dalam mengimplementasikan keterampilan mahasiswa untuk hidup bermasyarakat. Pengabdian wajib dilaksanakan oleh Universitas atau Institusi yang menganut Tri Dharma Perguruan Tinggi. Lokasi pengabdian di Desa Bangun Harja, Kecamatan Seruyan Hilir Timur, Kabupaten Seruyan. Program kerja yang dilakukan salah satunya adalah membantu warga dalam pembuatan dan pemasangan papan nama jalan, penunjuk rumah ketua RT/RW dan papan nama mushola. Salah satu yang menjadi kendala terutama untuk warga pendatang adalah tidak tersedianya papan penunjuk jalan di desa. Oleh karena itu dilakukannya program kerja pembuatan dan pemasangan papan plang ini. Program kerja ini dilakukan dalam 4 tahapan. Tahap pertama yaitu melakukan observasi/survey lokasi, tahap kedua menyiapkan perlengkapan yang dibutuhkan untuk proses pembuatan, tahap ketiga yaitu proses pembuatan mulai dari pemotongan papan dan tiang, pengecatan, dan lain-lain serta tahap keempat yaitu pemasangan papan plang nama tersebut.
PERANCANGAN IDENTITAS VISUAL PRODUK TEH HERBAL BUNGA TELANG UNTUK MENINGKATKAN DAYA TARIK KONSUMEN DI DESA CIKELAT
Authors: Novia Irsa , Hidayat Alfiansyah , Ahmad Gozali Rizqi , Alamsyah Zaenal
Published: February 2025

Telang Flower Herbal Tea is a herbal drink produced from butterfly pea flowers (Clitoria ternatea), a plant that grows abundantly in tropical areas, including Indonesia. Butterfly pea flower, also known as butterfly pea flower or blue flower, has a distinctive blue color that gives this herbal tea its color and natural properties. This research is the result of collaboration between Nusa Putra Sukabumi University and Cikelat Village, in an effort to increase the attractiveness of telang flower herbal tea products. The product's visual identity is the main focus of this research, with the aim of creating a strong and attractive visual identity to increase consumer appeal. A strong visual identity can help these products be more easily recognized by consumers, create a positive image, and increase confidence in the product's quality. Visual identity design involves color selection, logo design, packaging design, and branding strategy, as well as active participation from the Cikelat Village community to ensure conformity with local values and culture. It is hoped that this research will provide benefits to the people of Cikelat Village, enrich students' educational experiences, and become a reference for other rural communities who are trying to increase the competitiveness of local products through designing effective visual identities.Keywords: Telang Flower, Visual Identity, Cikelat Village
PENGARUH IDENTITAS VISUAL UMKM MELALUI DESAIN LOGO DAN DESAIN BANNER PROMOSI TERHADAP MINAT KONSUMEN DI DESA KARANGPAPAK
Authors: Nursifa Fauziah Rizki , Setiawati Asti , Fauzia Widya Dara , Puspita Ika , Nurlatipah Lela
Published: February 2025

Pengaruh identitas visual dalam desain logo dan desain banner promosi terhadap minat konsumen terhadap Usaha Mikro, Kecil, dan Menengah (UMKM). Identitas visual merupakan elemen penting dalam membangun citra dan merek UMKM. Hasil penelitian menunjukkan bahwa identitas visual yang kuat dalam desain logo dan desain banner promosi memiliki pengaruh positif yang signifikan terhadap minat konsumen untuk mengenal lebih jauh dan membeli produk atau layanan yang ditawarkan oleh UMKM. Faktor-faktor seperti konsistensi desain, kesan visual, dan daya tarik estetis juga ditemukan berperan penting dalam mempengaruhi minat konsumen. Penemuan ini memberikan pandangan berharga bagi pemilik UMKM dan desainer grafis dalam mengembangkan strategi identitas visual yang efektif untuk meningkatkan daya tarik produk atau layanan mereka di pasar yang kompetitif. Selain itu, penelitian ini juga menyumbangkan pemahaman lebih lanjut tentang keterkaitan antara desain grafis dan perilaku konsumen dalam konteks UMKM
EDUKASI LINGKUNGAN DAN PENGELOLAAN SAMPAH BERBASIS MASYARAKAT UNTUK MEMBANGUN DESA MANDIRI DALAM MENJAGA KELESTARIAN ALAM DAN EKOSISTEM
Authors: Tri Hadianto  , Hafidz M atsal , M Galang fajar  , M Taufik Faturahman  , Wafaunnisa  , Ayang marliani  , Nenden putri  , Isep teddy  , M Irsyad fauzan  , Winda sugesti  , Viery bagja  , Januar aditia  , Isfa hani  , Syafaat juliansyah  , Nakita syaila  , Elga indah  , Nabila putri  , Maria rukmawati
Published: February 2025

Masalah pengelolaan sampah menjadi tantangan besar bagi banyak negara, termasuk Indonesia. Sampah yang tidak dikelola dengan baik dapat menyebabkan berbagai masalah lingkungan, seperti pencemaran tanah, air dan udara. Hal tersebut bisa terjadi disebabkan oleh Masyarakat yang membuang sampah sembarangan. Solusinya adalah dengan membuat bank sampah agar sampah bisa terkontrol dan terarah dengan baik. Dengan melakukan observasi dan wawancara dengan Masyarakat kita mengetahui permasalahan sampah yang tidak terkontrol dengan baik. Setelah terlaksana nya program pengelolaan dan pembuatan bank sampah Masyarakat menjadi lebih sadar akan dampak dari sampah.
OPTIMALISASI POTENSI LOKAL DALAM UPAYA MENINGKATKAN PRODUKTIVITAS PEREKONOMIAN, PENDIDIKAN DAN PENCEGAHAN STUNTING
Authors: Rendika Febrian
Published: February 2025

Abstrak
Penelitian ini bertujuan   untuk   menyampaikan   informasi   kuliah   kerja   nyata   di   Kelurahan
Palabuhanratu, Kecamatan Palabuhanratu. Kuliah kerja nyata ini dilaksanakan dimulai pada 1 Agustus – 31 Agustus 2024. Beberapa rumusan masalah yang ada di Kelurahan Palabuhanratu yaitu, pengelolaan keuangan pengusaha UMKM yang belum efektif dan pasar penjualan belum luas, kurangnya fasilitas ruang kelas di SDN Cipatuguran, angka stunting yang masih tinggi dan Pemberian Makanan Tambahan yang kurang gizi, kurangnya kesadaran masyarakat mengenai kebersihan lingkungan. Metode yang digunakan pada kegiatan ini menggunakan metode kualitatif dengan cara wawancara dan observasi kepada warga masyarakat Kampung Cipatuguran, dan beberapa program yang dilaksanakan pada kegiatan ini antara lain peningkatan UMKM, kegiatan belajar mengajar, zero new stunting, program pembuatan ecobrick, kemerdekaan HUT RI dan mitigasi bencana. KKN ini bertujuan untuk meningkatkan pemberdayaan warga masyarakat dan potensi lokal di Kampung Cipatuguran.
 
Kata kunci: Kuliah Kerja Nyata, Kampung Cipatuguran, Kelurahan Palabuhanratu
PENINGKATAN KESEJAHTERAAN MASYARAKAT DESA CIKAHURIPAN MELALUI KEBERSIHAN DAN PENGEMBANGAN EKONOMI UNTUK MENCEGAH STUNTING SERTA PEMBERDAYAAN UMKM
Authors: Purnarahayu Kania , Siti Nurviatika
Published: February 2025

Artikel ini membahas mengenai eksplorasi upaya peningkatan kesejahteraan masyarakat melalui program kebersihan lingkungan dan pengembangan ekonomi lokal sebagai strategi untuk mencegah stunting dan memberdayakan Usaha Mikro, Kecil, dan Menengah (UMKM) di Desa Cikahuripan. Pendekatan yang digunakan dalam penelitian ini melibatkan partisipasi aktif masyarakat dalam kegiatan kebersihan, serta pengembangan kapasitas UMKM melalui pendampingan dan pemasaran digital. Hasil penelitian menunjukkan bahwa adanya peningkatan kesadaran masyarakat terhadap pentingnya kebersihan dan sanitasi dapat secara signifikan mengurangi risiko stunting di Desa Cikahuripan. Selain itu, pengembangan ekonomi lokal melalui pemberdayaan UMKM terbukti efektif dalam meningkatkan pendapatan dan kesejahteraan masyarakat. Studi ini menyimpulkan bahwa pendekatan terpadu yang menggabungkan upaya kesehatan dan ekonomi dapat menjadi model yang efektif untuk meningkatkan kesejahteraan masyarakat dan mencegah stunting. Rekomendasi penelitian ini meliputi peningkatan kolaborasi antara pemerintah, dan masyarakat dalam program kebersihan dan pemberdayaan ekonomi untuk menciptakan lingkungan yang sehat dan mendukung pertumbuhan ekonomi yang berkelanjutan di desa-desa lain di Indonesia.
Capital, câmbio, investimento e a legislação sobre mercado de câmbio no Brasil
Authors: Alves de Morais Viviane
Published: February 2025

This article seeks to contribute to the analysis of the adequacy of Law 14,286, of December 29, 2021, to the national legal system and, in particular, to article 172 of the Constitution of the Federative Republic of Brazil of 1988 based on the definitions of capital and investment, the forms of application of foreign capital in national territory previously regulated by Law 4,131, of September 3, 1962, and the guidelines of the Liberalization Codes of OECD, to which the new legislation on the foreign exchange market and the legal discipline of Brazilian foreign investment were definitively adapted with the approval of Law 14,286, of December 29, 2021.
HOLISTIC LIFE CYCLE SUSTAINABILITY ASSESSMENT FOR RETAIL STORES AND APPLICATION OF IMPACT REDUCTION STRATEGIES
Authors: FERNÁNDEZ VAZQUEZ ARÁNZAZU , Biedermann Anna , MUÑOZ LOPEZ NATALIA , SANTOLAYA PEREZ JOSE LUIS
Published: February 2025

Sustainability has become a critical value for retail stores, aligning with corporate social responsibility and the goals of the 2030 Agenda. Despite this, the spaces devoted to the retail sector remain under-researched in terms of comprehensive sustainability assessments. This paper fills this gap by conducting a Life Cycle Sustainability Assessment (LCSA) of retail stores, examining their environmental, economic, and social impacts from creation, through the use, to end-of-life. Three key sustainability indicators—Global Warming Potential, Global Costs, and Time of Labour —were analysed for a case study store in Spain. The analysis revealed that the creation phase has the highest impact across all dimensions, highlighting the importance of targeted interventions at this stage. Several strategies were proposed to reduce the store's sustainability impacts, including optimising lighting layouts, improving insulation, and reusing materials. These changes led to notable improvements, particularly in environmental and economic performance, while maintaining a balanced social impact. This research contributes to the field by offering a holistic methodology for assessing retail store sustainability and presenting practical strategies for reducing life cycle impacts. It provides a framework that can guide both designers and retailers in implementing more sustainable practices, thus addressing a critical area of sustainability research.
Keywords: Retail space, LCSA, Sustainability strategies.
INTEGRATION AND OPTIMIZATION OF 3D MODELS FOR THE ENHANCEMENT OF CULTURAL HERITAGE: CASE STUDY OF THE ALBANCHEZ DE MÁGINA CASTLE (JAÉN. SPAIN)
Authors: VALDERRAMA ZAFRA JOSE MANUEL , GARCIA MOLINA DIEGO FRANCISCO , CACERES CRIADO IRENE , MONTALVO GIL JUAN MANUEL , RUBIO PARAMIO MIGUEL ANGEL
Published: February 2025

Nowadays, engineering and archaeology
work together to address
the documentation, conservation and promotion
of cultural heritage, a collaboration
that has led to an
interdisciplinary field in constant evolution.
[1, 2]. This cross-disciplinary approach has enabled
the development of advanced tools and methodologies
that combine traditional techniques
with emerging technologies,
contributing to the conservation and dissemination
of cultural heritage at a global level.
[3, 4, 5].
Dampak Penetapan Tarif Impor Kedelai di Indonesia dengan Analisis Keseimbangan Parsial
Authors: Bayu Aunaya
Published: February 2025

Soybean is one of strategic foods in Indonesia, with their processed products widely consumed by the population. However, domestic soybean production is currently low, leading to a reliance on imported soybean to meet domestic demand. One form of government intervention to regulate soybean imports is to set soybean import tariffs. The Indonesian government has set a target for food self-sufficiency, including soybean. However, low import tariffs in recent years have had the opposite effect, weakening domestic efforts to increase soybean production and facilitating the flow of soybean imports. This study aims to analyze the impact of soybean import tariffs in Indonesia. The analytical method used is partial equilibrium to calculate the impact of import tariffs on the volume of soybean imports and other economic performance. This study uses basic data on world soybean prices, exchange rate, soybean import volume, domestic soybean production, soybean consumption, and soybean import tariff in 2022. The simulation results show that the liberalization of soybean import tariff results in an increase in soybean import volume and benefits the consumers, but harms soybean producers. On the contrary, the implementation of soybean import tariffs of 5% and 10% results in a decrease in the volume of soybean imports and benefits soybean producers, but harms the consumers.
Systematic Literature Review : Knowledge Management sebagai pilar dalam Implementasi Corporate University
Authors: Utami Teja
Published: February 2025

This research produces an understanding of the importance of knowledge management in the development and implementation of corporate university (CorpU) in an organization. This research uses a systematic literature review method, consisting of identifying, classifying, reviewing, evaluating the results of previous researches that have been published on Google Scholar and Scopus. The research result shows that knowledge management and CorpU development and implementation cannot be separated. CorpU is developed to create a learning organization, of course it needs to be driven by individuals who learn continuously (learning culture). Knowledge management is very important to manege and facilitate individuals learning. Knowledge management trough a knowledge management system (KMS) provides to collect knowledge assets, manage and organize them, disseminate knowledge, use and develop knowledge, to save knowledge assets and realize to development sustainable innovation.
Evaluating the Anti-Ulolithiatic Potential of Methanolic Extract of Hygrophila salicifolia Using In vitro Method of Urolithiasis Induction
Authors: Mohapatra Santosh Bandhu , Sarkar Ashish
Published: February 2025

Utilising an in vivo approach to induce urolithiasis with ethylene glycol, the research examines the anti-urolithiatic potential of a methanolic extract derived from the whole Hygrophila salicifolia plant. Over the course of 28 days, five groups of male Wistar albino rats were randomly assigned to different treatment groups. The first group was used as a control, the second group got a lithiatic agent, the third group had Cystone, and the fourth and fifth groups got two doses of the methanolic extract. From the urine samples, many parameters were analysed, including the amounts of calcium, creatinine, phosphate, magnesium, and oxalate. The development of calcium oxalate monohydrate (COM) crystals was significantly inhibited by the methanolic extract in comparison to the control and other extracts. A significance threshold of P<0.001 was used in the statistical study to validate the effectiveness.
Automated Detection and Classification of Diabetic Retinopathy using Machine Learning and Ensemble Techniques
Authors: R. Indhumathi
Published: February 2025

Diabetic retinopathy is an eye condition caused by chronic diabetes. Diabetes is a serious consequence because it can harm blood vessel tissue that is sensitive to light. People of working age are predominantly affected by this ailment. At first, diabetic retinopathy may show no symptoms. On the other hand, it can eventually result in blindness. The suggested approach makes use of the Local Binary Pattern feature extraction technique. The K Nearest Neighbour, Random Forest, and Logistic Regression algorithms are given the features that were extracted. Stacking, voting, and averaging are ensemble strategies that combine them during training and testing. Images of diabetic retinopathy, which are categorized as mild, moderate, no diabetic retinopathy, proliferating diabetic retinopathy, and severe, are gathered for this project from the Kaggle dataset. The experimental results show that averaging produces good results of 74%.
Emotional Intelligence (EI) Spectrum: Comparing Undergraduate and Postgraduate Management Students
Authors: Anurag Agarwal
Published: February 2025

Education plays a major role in the development of any country. Emotional Intelligence (EI) first arose in the 1990s as an ability-based construct similar to general intelligence. Management students' academic progress, interpersonal interactions and professional preparation are all influenced by their EI. However, the comparative understanding of EI skills at the undergraduate and postgraduate levels is still overlooked. The main objective of this research was to compare the EI spectrum of these two groups, focusing on core EI competencies such as self-awareness, empathy, self-regulation, motivation and social skills. This study employed a quantitative research methodology and included 100 management students from Greater Noida. The primary data was collected via a standardised questionnaire. Statistical tools such as mean, correlation, regression, ANOVA were utilised. The findings reveal that there is no significant difference in EI spectrum between undergraduate and postgraduate management students except for self-regulation. By addressing the unique developmental needs of management students, the findings will assist educators and policymakers in creating focused interventions to improve EI. These interventions can result in better academic performance, professional readiness and interpersonal skills, thereby producing future leaders who are well-rounded.
Learnable Conjunction with Adjective Enhanced Model for Mandarin Sentiment Analysis of Social Media Text
Authors: Zhang Jie
Published: February 2025

The advent of research into neural networks has led to the widespread utilisation of deep learning methodologies in the domain of text sentiment analysis, owing to their formidable data processing and pattern recognition capabilities. In recent years, Transformer and its variant Bert have attracted considerable attention, and their performance has been demonstrated to be superior in practice. However, it is important to note that China's unique social media data set is characterised by a variety of languages, distinct cultural backgrounds, and intricate emotional expressions, which poses a significant challenge to the generalisability of models trained on more homogeneous data sets. Consequently, the conventional Transformer model's efficacy in feature extraction is constrained in certain scenarios, impeding the efficiency of its implementation. It is necessary to adapt and optimize the model further to account for the specific attributes of social media data in the task design. Nevertheless, within the framework of Chinese linguistics, the significance of word order and conjunction is inherently apparent. Conversely, the potential of adjectives in emotional expression is frequently disregarded. This study endeavours to investigate an innovative approach, underpinned by a Transformer encoder model, to construct a reinforcement model that can integrate and learn adjective features. The model will be evaluated through experimentation with three publicly available Chinese social media datasets. The experimental data demonstrate that the model can utilise the attention mechanism to not only identify the emotional tendency of keywords but also to effectively combine the positional characteristics of conjunctions and adjectives to obtain local details and contextual meanings of the text. This enhancement of the model's effectiveness in feature extraction is a significant contribution to the field.
The Role of Family Support and Social Networks in Shaping The Entrepreneurial Attitudes and Behaviours of Tribal Women
Authors: Sangeetha. M
Published: February 2025

Purpose
                Entrepreneurship among tribal women is a key driver of economic empowerment and social transformation; however, these women face significant barriers such as limited financial resources, socio-cultural constraints, and restricted market access. This study, grounded in the Resource-Based View (RBV) model, explores how family support and social networks function as valuable, rare, inimitable, and non-substitutable (VRIN) resources in shaping their entrepreneurial attitudes and behaviors.
Methods/Measures
                A quantitative research approach was employed, using Structural Equation Modeling (SEM) to examine the relationships between family support, social networks, entrepreneurial attitudes, and entrepreneurial behavior. Data was collected from tribal women entrepreneurs through a structured questionnaire utilizing a five-point Likert scale.
Findings
                The results indicate that family support and social networks significantly influence entrepreneurial attitudes, which, in turn, positively impact entrepreneurial behavior. Financial assistance, emotional support, and skill-sharing from families enhance self-efficacy and risk-taking, while social networks provide mentorship, market access, and financial linkages, fostering business sustainability and innovation.
Originality
                This study contributes to entrepreneurship literature by highlighting the crucial role of intangible resources in tribal women’s business success. By applying the RBV framework, it offers new insights into how family and social capital drive entrepreneurship, providing policy implications for financial inclusion, capacity-building programs, and gender-focused entrepreneurial development.
Assessing the Impact of Softscape features in Parks on the Wellbeing of Senior Citizens: A case of NOIDA
Authors: Ruchi
Published: February 2025

Introduction: The study examines the role of softscape features in urban parks in facilitating environmental sustainability and enhancing the well-being of vulnerable communities, with a focus on senior citizens. The study evaluates the impact of softscape components, including trees, shrubs, biodiversity zones, and shaded seating, on the physical, mental, and social well-being of older adults who frequent parks. The study uses nine parks from three administrative divisions of NOIDA (India) as a case study and systematically investigates the correlation between the park design and the quality of life of senior citizens. A mixed-method research design was used including quantitative and qualitative data collection methods. Data were obtained through a survey done with 200 senior citizens in the selected parks, examining their perceptions of well-being, engagement with green spaces, and perceived health benefits. Furthermore, field-based ethnographic observations were employed to confirm the survey results concerning accessibility, frequency of park usage, and contributions to environmental quality performed by urban greenery. The study also included consideration for Service areas implementing rankings of the top three performing parks in each Division (Division I, II, and III) as measured by user engagement, spatial distribution, and total impact on senior citizens. In an analysis of the data, there is a direct correlation between softscape and the structural benefit it has on the well-being of seniors. The results show that parks that have more vegetation, shaded paths and interesting landscapes are strongly correlated with reductions in stress, physical activity and social interaction. Moreover, in addition to the individual health benefits, the research underscores wider environmental benefits including cleaner air, heat mitigation and more biodiversity emphasizing the ecological importance of urban parks. These cooling effects from green infrastructure are vital in mitigating urban heat islands, thus enabling more livable and climate-resilient cities.
This study provides practical insights for urban planners and policymakers. As evidenced by the importance of ecological cooperation and an older-friendly infrastructure, the research brings to light the importance of sustainable urbanization that incorporates softscape characteristics. Its research highlights critical design and sustainability practices that optimise green space usage for healthy, active ageing, creating environmental sustainability alongside improved health and wellbeing outcomes for older adults across the life course. By identifying and evaluating these beneficial hotspots, this study lays the foundation for further enhancing these environmental assets and for creating a more resilient and sustainable urban ecosystem, where nature-based solutions ensure the well-being of communities and the environment as a whole.
Machine Learning-Based Prediction of Energy Consumption in Smart Buildings for Sustainable Energy Management
Authors: Kiran Sree Pokkuluri
Published: February 2025

Sustainable operations together with higher efficiency depend on proper energy management in buildings with smart functionality. In this paper, the machine learning methods using multiple regression and advanced learning models are employed to predict the energy consumption (EC). The dataset consists of key input variables, including temperature (T), humidity (H), occupancy (O), building area (A), lighting power usage (L), HVAC energy consumption, and the day of the week (D). The proposed methodology involves data preprocessing, feature selection, and hyperparameter tuning to enhance model accuracy. Four machine learning models—multiple linear regression (MLR), random forest regressor (RFR), support vector regression (SVR), and artificial neural networks (ANN)—were evaluated based on performance metrics such as R², mean squared error (MSE), and root mean squared error (RMSE). Results indicate that the random forest regressor outperforms other models, achieving an R² of 0.84 and an accuracy of 89.27% on the test data, while ANN, despite excelling in training, demonstrated overfitting with reduced generalization ability. Sensitivity analysis highlights HVAC energy consumption and lighting power usage as the most influential parameters. An actual case study illustrates how the implemented model functions in modern-day energy management practice by demonstrating energy saving opportunities along with proposed optimization measures. These findings help sustainable energy practices through predictable choice making and enhanced energy efficiency monitoring of intelligent building operations.
Exploring the Role of AI in Understanding Human Emotion
Authors: Noor Alwan Malk
Published: February 2025

This research will explore the intersection of artificial intelligence (AI) and understanding human emotions, a growing field known as emotional AI. The research aims to provide a comprehensive overview of how AI techniques, such as machine learning, natural language processing (NLP), and facial and voice expression analysis, can be used to analyze human emotions and improve human-machine interactions.
The research reviews the theoretical foundations for understanding emotions based on several psychological and neurological models, and also discusses AI applications in the fields of mental health, education, and marketing, where these technologies can improve user experiences by recognizing their emotional responses and tailoring their interactions accordingly.
However, this field faces significant challenges, such as privacy and data security, model bias, and accuracy in sentiment analysis. The study highlights the need to develop more transparent and fair systems to ensure the ethical use of this technology.
The research suggests that advances in deep learning and multi-dimensional interaction can improve AI’s ability to accurately understand human emotions, opening the door to developing AI systems that are more aware and responsive to human emotions.
Optimization of Detection Error Rate in Cooperative Spectrum Sensing Using Multi-Objective JAYA Algorithm
Authors: Keraliya Divyesh R
Published: February 2025

In order to make the most of the available spectrum, Cognitive Radio (CR) is used. When a licensed user isn't actively utilizing the spectrum, CR may interact with each other using it. Therefore, CR places a premium on spectrum sensing, the process of observing the presence of the main user. Cooperative spectrum sensing is a method for quickly identifying spectrum gaps in cognitive radio networks (CRNs) by merging the sensing data from different cognitive radio users. Cooperative spectrum sensing's overall performance is largely affected by how well secondary users' and the fusion center's local observations are combined. The accuracy of local observations and the data acquired by the fusion center (FC) are the two main factors that affect the detection performance. That is why system performance is affected by global decision logic and the amount of bits transferred to the fusion center (FC). However, since the sensing data collection control channel has a limited bandwidth, it is essential to quantify the received data on the energy of the signal for every user. We provide a quantized cooperative spectrum sensing method after researching coordinated spectral sensing methods based on energy detection. Through the optimization approach that is based on the Multi-Objective JAYA Algorithm, we are able to create an ideal quantized data fusion scheme that optimizes the detection error probability for a precise value of threshold. Results demonstrate that the proposed framework is valid, with the multi-objective JAYA (MOJAYA) based performance being almost identical to that of the standard soft combining technique while using less bandwidth and overhead.
Quantile Regression Machine Learning Techniques to Handle Outliers in Time Series Data
Authors: Tushar Mehta
Published: February 2025

Time series data often exhibit outliers that can significantly distort the results of traditional regression methods, leading to inaccurate forecasts and suboptimal decision-making. Quantile regression offers a robust alternative by estimating the conditional median or other quantiles of the response variable, thus providing a more comprehensive analysis of the underlying data distribution. This review paper explores the integration of quantile regression with advanced machine learning techniques to effectively handle outliers in time series data. By comparing different approaches, we evaluate how well they perform in terms of root mean square error (RMSE) and mean absolute error (MAE). According to the comparison graph, the EMD-QRnet method greatly boosted the MAE and RMSE by 84.3% and 1.00%, which is the highest gain of any method when compared to others.
Object Detection In Real-World Scenarios Using Artificial Intelligence And Machine Learning Technologies
Authors: Senada Bushati
Published: February 2025

This study explores the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques for object detection in real-world scenarios, with a particular focus on Albania. The purpose of the research is to develop and evaluate advanced object detection models that can enhance accuracy and reliability in various applications such as security and road safety. The study employs an experimental approach, leveraging the YOLO algorithm and Convolutional Neural Networks (CNNs) to train and evaluate customized object detection models using diverse datasets. Comparative analyses are conducted to identify the most effective methodologies. The findings demonstrate that larger, high-quality datasets significantly enhance model performance, as evidenced by a maximum F1-score of 0.96 achieved with 80 training images and 50 epochs. The research highlights the transformative potential of AI-driven object detection in improving processing speed and accuracy for critical applications. Challenges such as computational resource limitations and dataset constraints are identified as barriers to broader implementation. The study concludes with practical recommendations for improving model scalability and reliability, emphasizing the importance of integrating AI with complementary technologies for real-world deployment. These insights have implications for policymakers, developers, and industries aiming to leverage AI for enhanced safety and efficiency in infrastructure and beyond.
Internet of Medical Things (IoMT): Opportunities and Security Challenges
Authors: N. K. Sakthivel
Published: February 2025

The IoMT is nothing but a technology phase breakthrough in the healthcare industry that uses wearables, and other low-level digital data collection devices for better patient care and operational efficiency. Through this research, we seek to bring together the opportunities and security threats related to IoMT with a special focus on machine learning algorithms as a predictive tool for health assessment. Empirical experiments were conducted to determine the performance of four machine learning techniques (Random Forest, Support Vector Machine, K-Nearest Neighbors, and Deep Learning Neural Network) using different set of data covering medical sensor data and patient's health records. The prediction results show that the DNN-based model had higher accuracy than other algorithms, its deep learning producing the right outcomes of 0. 88, precision of 0. 89, recall of 0. With more than eighty-seventy five percent recall rate and F1-score of 0. 88. The comparison will enlighten the advantages and shortcomings of various algorithms in using the IoMT data to predict the health outcomes, showing the prospect of the DNN as one of the better algorithms in the space. Along with this, this study brings to the foreground the significance of focusing on strong cybersecurity measures to ensure that patients’ data remains protected and IoMT ecosystems are free from flaws.
A Comparative Analysis of F-RAN and C-RAN Architectures for Next-Generation Wireless Networks
Authors: Shivya Srivastava
Published: February 2025

By means of optimization, next-generation wireless networks will be able to meet the growing need for lowered latency, fast data rates, and enhanced connection. We investigate two interesting ideas for next-generation radio networks and assess Cloud Radio Access Network (C-RAN) and Flexible Radio Access Network (F-RAN). These designs seek to improve network performance via better use of resources and overcome problems with regular radio access networks. F-RAN guarantees that multiple network components communicate without any problems and lets network management flexibility by letting resources be dispersed according to the demand. On the other hand, C-RAN aggregates cloud-based solutions' administration of radio access resources. This allows networks to save costs and promotes their collaboration. We investigate the benefits and disadvantages of both designs with respect to size, energy economy, interference control, and network speed. We also look at how each solution meets changing needs for high-speed transmission—including those of 5G and beyond. F-RAN is a great choice for large-scale operations as it is better in responding and being flexible in changing surroundings; C-RAN is superior in resource organization throughout the whole network. As this study illustrates, each one of these concepts has benefits and disadvantages. This helps to choose and improve wireless network architectures for future generations of networks.
A Review: Data Overfitting and Underfitting Techniques
Authors: Kiran Maharana
Published: February 2025

Modern problem statements demand advanced, futuristic, cutting-edge, progressive and trend-setting solutions. In today’s landscape, Artificial Intelligence (AI) stands out as a revolutionary, data-driven technology reshaping our perception of modern applications. However, when AI interacts with Machine Learning (ML), another data-driven domain, a significant hurdle emerges concerning the quality and quantity of data. This problem profoundly impacts the performance of ML Models, prompting engineers and architects to address a myriad of questions before selecting a suitable mode. Consequently, altering the architecture and models becomes arduous due to data-related issues, which can be numerous. Hence, a solution must be sought that caters to both needs. This review paper delves into the various challenges related to Data Overfitting and Data Underfitting, exploring methods for identification, key questions for consideration, underlying reasons, techniques, real-life examples, synthetic data illustrations, and a plethora of case studies from diverse research papers showcasing how they tackled the problem and how overfitting and underfitting can be solved.
5G: The Impact and Prospects for the Future of Connectivity
Authors: Rekha Kumari
Published: February 2025

The mobile industry is currently busy creating and getting ready to deploy networks of the fifth generation, also known as 5G. The proliferation of the Internet of Things (IoT) and other intelligent automation applications is being significantly fueled by the expansion of 5G networks, which are starting to become more accessible. For advancements in intelligent automation, such as the Internet of Things (IoT), artificial intelligence (AI), driverless cars, digital reality, blockchain, and future discoveries that we haven't even dreamt of yet, the lightning-fast connectivity and low latency that 5G provides are essential. Rather than merely being a generational stride, the arrival of 5G is a step that opens up a whole new world of possibilities for any industry that deals with technology. The objective of this study is to investigate the ways in which 5G communications can facilitate or simplify intelligent automation in a variety of business sectors.The purpose of this paper is to examine the influence and uses of 5G technology in many key industries, as well as to emphasize the role that it plays in shaping the age of boundless connectivity, intelligent automation, and industry digitization.
Ergonomic Risk Assessment and Work-Related Musculoskeletal Disorders in Seated and Standing CNC Operators
Authors: V. P. Kshirsagar, G. K. Awari, M. P. Singh
Published: February 2025

Objective: This study is to assess the ergonomic challenges faced by CNC machine wheelchair-bound and standing posture operators, this study to identify the risk factors contributing to Work-Related Musculoskeletal Disorders (WMSDs). This research specifically determines which operators are more susceptible to WMSDs by evaluating the ergonomic compatibility of CNC machine designs for standing and wheelchair-bound operators.
Methods: Data were collected from 124 CNC machine operators, including 11 wheelchair users, through structured interviews, discomfort questionnaires, and direct observation of tasks. A discomfort mapping technique, based on Corlett & Bishop's method, was used to assess body pain and discomfort at different work intervals. Injury records were reviewed, and capture discomfort ratings from operators. Using quantitative evaluation tools, anthropometric data for standing and wheelchair-bound operators were analyzed to identify the critical ergonomic risk factors. Discomfort ratings were further analyzed using one-way ANOVA to determine the significance of various factors. The identified significant ergonomic risks were assessed using the Rapid Upper Limb Assessment (RULA) tool to evaluate physical workloads and awkward postures.
Results: The study found significant discomfort more in wheelchair-bound operators as compare to standing posture operators. The discomfort in wheelchair-bound operators due to awkward postures, repetitive tasks, and poor machine accessibility, particularly affecting the neck, shoulder, and lower back conversely standing operators reported significant discomfort, particularly in the abdomen, chest, and knees. A comparative analysis and quantitative analysis highlighted that the ergonomic design of CNC machines profiles revealed that only a few designs are ergonomically suitable for both seated and standing operators and often fails to accommodate mostly wheelchair users effectively, resulting in awkward postures and repetitive strain injuries.
Conclusion: The findings highlight the need for ergonomic interventions in CNC machine design to improve accessibility for wheelchair-bound more as compare to standing posture operators and reduce WMSD risks. Inclusive design modifications, such as adjusting machine height and improving monitoring window placement, can significantly enhance operator comfort and efficiency..
AI-Driven Predictive Maintenance in Smart Manufacturing: Enhancing Efficiency through Deep Learning Models
Authors: Naveen Chandra Kukkala
Published: February 2025

Tool wear monitoring and predictive maintenance are critical in manufacturing, where traditional methods often struggle to adapt to changing conditions. This research presents an Adaptive Reinforcement Learning Framework for Real-Time Tool Wear Optimization and Predictive Maintenance (ARTOM). The framework integrates reinforcement learning with real-time sensor feedback to optimize machining parameters and maintenance schedules dynamically. Proximal Policy Optimization (PPO) is used to guide decision-making by balancing tool life, product quality, and operational costs. Multi-agent reinforcement learning divides tasks among agents to handle diverse machining scenarios, while sliding window techniques and dimensionality reduction ensure efficient data processing. The study has used the benchmark dataset, which include time-series sensor data and machining parameters. Metrics potential metrics have been used to evaluate prediction accuracy, while runtime and memory usage assess computational efficiency. Results has shown that ARTOM consistently achieves lower prediction errors and faster execution times than contemporary baseline models. These findings demonstrate ARTOM’s ability to adapt to different tool conditions and improve operational decision-making.
Quantum-Resistant Cryptographic Algorithms: A Comparative Analysis for Securing Next-Generation Communication Networks
Authors: Sagar Ramesh Rane
Published: February 2025

The advent of quantum computing poses a significant challenge to conventional cryptographic methods such as RSA, Elliptic Curve Cryptography (ECC), and Diffie-Hellman key exchange. Quantum algorithms, particularly Shor’s algorithm, have the potential to break these encryption techniques, making it essential to develop cryptographic approaches that can withstand quantum threats. Post-quantum cryptography (PQC) has emerged as a crucial area of research, aiming to establish cryptographic mechanisms that remain secure even in the presence of quantum adversaries.
This study presents a detailed comparative analysis of five primary categories of quantum-resistant cryptographic algorithms: lattice-based, code-based, hash-based, multivariate polynomial, and isogeny-based encryption schemes. Each of these approaches offers distinct advantages and challenges in terms of security, efficiency, and implementation feasibility. Among them, lattice-based cryptography has gained significant attention due to its robust security properties and computational efficiency, making it a strong candidate for standardization. Conversely, code-based cryptography provides high security but is hindered by its large key sizes, affecting its practical deployment.
The research includes a real-time performance assessment of selected PQC algorithms, analyzing key factors such as encryption speed, key size, and computational demands. Furthermore, the study examines the challenges associated with transitioning from classical encryption standards to quantum-resistant frameworks, including compatibility constraints, computational overhead, and the necessity for global standardization. Potential mitigation approaches, such as hybrid cryptographic techniques that integrate both classical and post-quantum encryption models, are also explored.
Our findings emphasize that while quantum-resistant cryptography is still evolving, early adoption of PQC frameworks is essential for safeguarding future communication networks. This paper provides valuable insights for researchers, cybersecurity professionals, and policymakers on strategic measures to implement quantum-secure encryption systems effectively.
Diagnostic Predictive Approaches for Liver Disease Detection using Stacked Ensemble Model with Data Augmentation
Authors: Mahendran. S
Published: February 2025

The global medical fraternity is challenged with evolving a perfect prediction model that diagnoses a liver ailment at the right time and calls for an immediate medical intervention for its critical need. Potential threat to life of liver disease chronically contracted stresses for necessity for its root cause and time-bound medical remedy. By making use of a dataset of Indian liver patients from the pool of data maintained at the national level, this work is entailed with the introduction of an architecture of innovative nature that desegregates Stacked Ensemble Model, feature engineering for foretelling liver ailments. The core contribution to this work is the harness of feature engineering utilizing SHapely Additive exPlanations (SHAP), encompassing the state-of-the-art techniques that are not delved into by data-driven machine learning approaches that are in vogue now as a vaticinator of liver ailment. The labyrinthine design of Stacked Ensemble Model facilitates timely prediction of liver disease, exploiting the maximum of learning techniques that ramp up the detection activity and augment accurate diagnosing. The methodology exploits the potencies of varied base learners like Logistic Regression, Multi-layer Perceptron, Support Vector Machine, Decision Tree, K-nearest neighbor and Extra trees classifier. These diverse views are homogenized as input to the meta-learner, the Random Forest, to build a sturdy and trustworthy predictive model. The commissioning of Stacked Ensemble Model with feature engineering produced an accuracy of a cent percent short of 3. This methodology is aimed at fine tuning the prediction of liver ailments, ensuring smooth, effective and timely intervention, and assuring of an effective management.