bluuebunny/crossref_metadata_2025 huggingface dataset:
STRATEGI DAN PENGEMBANGAN PERAN BAITUL MAAL WAT TAMWIL (BMT) PADA INDUSTRI HALAL DI INDONESIA
Authors: Castrawijaya Cecep , Wirianisa Syifani
Published: August 2024

ABSTRAK Artikel ini menjelaskan mengenai industri halal dan peran baitul maal wat tamwil serta perkembangannya; strategi dan pengembangan lembaga dakwah yaitu baitul maal wat tamwil pada industri halal. Metode yang digunakan adalah kualitatif dengan pendekatan studi kasus analisis deskriptif dan teknik pengumpulan data secara kepustakaan (library research) dari berbagai literatur, buku maupun informasi lain yang berkaitan dengan topik penelitian. Hasil penelitian ini adalah adanya digitalisasi BMT dengan menyediakan aplikasi sederhana berbasis teknologi dan kolaborasi dengan perusahaan Fintech Syariah guna berperan untuk Indonesia menjadi pusat halal dunia di tahun 2024. Kata Kunci : Industri Halal, Baitul Maal Wat Tamwil, DigitalisasiABSTRACT This article explains the halal industry and the role of baitul maal wat tamwil and its development; strategy and development of da'wah institutions namely baitul maal wat tamwil in the halal industry. The method used is qualitative with a descriptive analysis case study approach and library research data collection techniques from various literature, books and other information related to the research topic. The results of this study are digitalization of BMT by providing simple technology-based applications and collaboration with Sharia Fintech companiesKeywords : Halal Industry, Baitul Maal Wat Tamwil, Digitalization
STRATEGI BAURAN PROMOSI PEMBIAYAAN CICILAN EMAS DALAM MENINGKATKAN INVESTASI PADA BANK SYARIAH INDONESIA (BSI) KCP TEBET BARAT JAKARTA SELATAN
Authors: Rizal Study , Robby Fadhli
Published: August 2024

ABSTRAKSalah satu perusahaan perbankan terbesar di Indonesia yaitu Bank Syariah Indonesia (BSI) mempunyai produk pembiayaan investasi yaitu cicil emas sesuai fatwa MUI mengenai pembelian emas secara tidak tunai. Produk ini dikategorikan sebagai pembiayaan produktif, yang ditujukan untuk memenuhi kebutuhan produksi dalam artian luas yaitu untuk peningkatan usaha, baik usaha produksi, perdagangan maupun investasi. Produk cicil emas BSI pada saat ini dinyatakan sudah berjalan dengan baik. Salah satu Direktur Retail Banking BSI menjelaskan sejauh ini kinerja Cicil Emas Per Mei 2022 Tumbuh 57,1% atau Rp 435,7 Miliar secara year on year. Di mana secara penyaluran pembiayaan pada triwulan I produk Cicil Emas telah tumbuh sebanyak 8.96%. Pertumbuhan pembiayaan cicil emas tersebut menggambarkan situasi penjualan produk cicil emas berjalan dengan baik. Pada saat yang sama, hal ini juga menunjukkan suatu strategi pemasaran yang baik dan berhasil.Menarik diteliti untuk lebih jauh melihat bagaimana investasi cicilan emas di BSI dalam kasus KCP Tebet Barat, apakah mengalami kemajuan dan bagaimana strategi pemasarannya?Untuk menjawab permasalahan tersebut, penelitian ini menggunakan teori strategi bauran promosi yang dikemukakan oleh Philip Kotler dan Garry Amstrong dengan 5 unsurnya, yaitu: periklanan, promosi penjualan, hubungan masyarakat, penjualan pribadi, dan penjualan langsung dalam meningkatkan investasi cicilan emas pada BSI KCP Tebet Barat.Penelitian ini menggunakan pendekatan kualitatif dengan analisis deskriptif, dan metode pengumpulan datanya dengan cara observasi, wawancara dan dokumen.Dari penelitian ini disimpulkan bahwa di BSI KCP Tebet Barat Jakarta Selatana tercatat bahwa penjualan atau pencairan cicil emas secara perlahan turun dari tahun 2020 hingga per 19 Oktober 2022. Hal ini karena adanya pandemi Covid-19. Dengan keadaan penjualan yang turun dari 3 tahun belakang, BSI KCP Tebet Barat ini tetap bertahan untuk mempromosikan produknya. Hal ini terlihat dari strategi bauran promosi pembiayaan cicil emas yang diterapkan Bank Syariah Indonesia KCP Tebet Barat sudah berjalan cukup baik dan masih bisa dimaksimalkan lagi. Kata Kunci: Strategi Bauran Promosi, Pembiayaan Cicil Emas, Investasi, Bank Syariah Indonesia
EFEKTIVITAS PENERAPAN TEKNOLOGI INTERNET GOOGLE ADS DALAM PENJUALAN PRODUK UMRAH PADA PT JANNAH FIRDAUS
Authors: Mahmud Hamidullah , Zahra Fairuz
Published: August 2024

abstrakEra digital saat ini telah membawa perubahan besar bagi segala aspek kehidupan, salah satunya dalam bidang bisnis. PT Jannah Firdaus merupakan salah satu penyelenggara perjalanan ibadah umrah yang menggunakan konsep digital yakni penerapan platform Google Ads untuk memasarkan produknya.Tujuan penelitian ini dilakukan untuk mengetahui bentuk penerapan dan efektivitas Google Ads dalam penjualan produk umrah pada PT Jannah Firdaus. Metode yang digunakan pada penelitian ini menggunakan metode kualitatif yang bertujuan untuk menggali lebih dalam mengenai objek dengan wawancara, observasi dan mempelajari fenomena yang terjadi.Hasil penelitian menunjukkan bahwa penerapan Google Ads dalam penjulan produk umrah yang dilakukan oleh PT Jannah Firdaus dapat dikatakan efektif. Hasil efetivitas penerapan Google Ads dapat dibuktikan dari perbandingan antara jumlah jamaah yang mendaftar melalui Google Ads lebih banyak dari jumlah jamaah yang mendaftar secara offline. Kata Kunci: Efektivitas, Google Ads, Penjualan, Produk, Umrah
EFEKTIVITAS PENGGUNAAN SISTEM INFORMASI DAN KOMPUTERISASI HAJI TERPADU (SISKOHAT) DALAM PELAYANAN PENDAFTARAN HAJI KHUSUS PADA KEMENTERIAN AGAMA PROVINSI DKI JAKARTA
Authors: Hafiz Abdul , Nurdianty Mitha Sari
Published: August 2024

Perkembangan teknologi informasi dalam bidang komunikasi terjadi kemajuan yang sangat pesat. komputer yang terkoneksi internet muncul sebagai hasil dari perkembangan komputer yang dapat terhubung dengan banyak orang sekaligus menyampaikan pesan kepada siapapun yang memerlukan. Dalam sistem perhajian dikenal dengan aplikasi yang bernama SISKOHAT. Di dalamnya menyimpan seputar informasi mengenai perhajian yang meliputi pendaftaran haji, database Jemaah, waiting list, dan lain sebagainya. SISKOHAT sangat memudahkan bagi penggunanya yaitu petugas PIHK yang mendaftarkan Jemaah nya dengan aman dan nyaman karena data yang diinput untuk pendaftaran haji tersimpan secara langsung didalamnya. Tujuan dari penelitian ini adalah untuk mengetahui bagaimana efektivitas pengunaan SISKOHAT di Kantor Wilayah Kementerian Agama Provinsi DKI Jakarta apakah sudah berjalan efektif atau tidak, dan faktor-faktor pendukung dan penghambat aplikasi SISKOHAT. Penelitian ini menggunakan metode kualitatif deskriptif. Untuk Objek penelitiannya, yaitu Kantor Wilayah Kementerian Agama Provinsi DKI Jakarta. Penulis menggumpulkan data melalui kepustakaan dan wawancara secara langsung di lapangan kepada informan yaitu Kepala Seksi Bina Penyelenggara Umrah dan Haji Khusus. Hasil dari penelitian tentang efektivitas dalam proses pelayanan pendaftaran haji khusus ini telah berjalan dengan efektif, dikarenakan adanya indikator-indikator yang mempengaruhi pengukuran efektivitas dari sistem teknologi informasi dengan pendukung utama SISKOHAT.
Metode Fundraising Dalam Meningkatkan Dana Zakat Infaq Sedekah (ZIS) Pada Kantor Layanan Lazismu Baitul Maal Khairu Ummah
Authors: Muslihin Muslihin , Munawaruzaman Ahmad
Published: August 2024

A good method will certainly produce very good achievements. A goodfundraising method will result in the maximum collection of zakat funds.Therefore, the author conducted a study entitled Fundraising method inincreasing zakat funds at the Muhammadiyah Amil Zakat infaq and almsinstitution (KL LAZISMU Baitul Maal Khoiru Ummah). The purpose of thisstudy is to find out the method applied by KL LAZISMU Baitul Maal KhairuUmmah and to find out the results of ZIS funds at KL LAZISMU Baitul MaalKhairu Ummah. The research method is carried out by means of descriptivequalitative research methods where the data used are secondary and primarydata, with a data collection technique of interviews, observations, anddocumentation. Then the data analysis uses descriptive analysis so that theresearchers describe the fundraising of ZIS funds in KL Lazismu Baitul maalKhoiru Ummah. The results of the research that were carried out showed thatthe fundraising method carried out by the KL Lazismu Baitul Maal KhoiruUmmah institution was direct fundraising and indirect fundraising so that theinstitution always experienced an increase in zakat funds obtained frommuzaki/donors.Metode yang baik tentunya akan menghasilkan pencapaian yang sangat baik. Metode fundraising yang baik maka akan menghasilkan pemghimpunandana zakat dengan maksimal. Maka dari itu penulis melakukan penelitiandengan judul Metode fundraising dalam meningkatkan dana zakat padaLembaga Amil Zakat infak dan sedekah Muhammadiyah (KL LAZISMUBaitul Maal Khoiru Ummah). Tujuan dari penelitian ini yaitu untukMengetahui metode yang di terapkan oleh KL LAZISMU Baitul Maal KhairuUmmah dan untuk mengetahui hasil dana ZIS pada KL LAZISMU Baitul MaalKhairu Ummah. Metode penelitian yang dilakukan dengan cara metodepenelitian kualitatif deskriptif dimana data yang di gunakan yaitu sebuah datasekunder dan primer, dengan sebuah teknik pengumpulan data wawancara,observasi, dan dokumentasi. Kemudian analisis data menggunakan analisisdeksriptif sehingga peneliti mendeskripsikan fundraising dana ZIS di KLLazismu Baitul maal Khoiru Ummah. Hasil penelitian yang di lakukanbahwasannya metode fundraising yang di lakukan lembaga KL LazismuBaitul Maal Khoiru Ummah adalah direct fundraising dan indirect fundraising sehingga lembaga selalu mengalami peningkatan dalam danazakat yang di dapat dari muzaki/donatur.
Mengukur Indeks Kepuasan Nasabah Pada Program Layanan Financial Teknologi Syariah
Authors: Amirudin Amirudin , Nafsiyah Siti
Published: August 2024

Fintech or better known as Financial Technology is an innovation in financialservices that is on the rise in Indonesia. The development of Fintech has greatlyinfluenced the community by being given access to financial products makingtransactions more effective and efficient. This study is to determine whetherthere is an effect of the service of PT Ammana Fintek Syariah South Jakartawith customer satisfaction. This study uses a survey, with a purposive samplingtechnique. The number of samples was 63 respondents, while the author's dataanalysis method used the IPA test and the CSI test. The test results show that there is a significant effect between the services of PT Ammana Fintek Syariah South Jakarta and customer satisfaction. Fintech atau lebih dikenal dengan Finansial Teknologi adalah sebuah inovasipada jasa keuangan yang sedang naik daun di Indonesia. PerkembanganFintech banyak mempengaruhi masyarakat dengan diberikan akses produkkeuangan menjadikan transaksi lebih efektif dan efisien. Penelitian ini untukmengetahui apakah ada pengaruh pelayanan PT Ammana Fintek SyariahJakarta Selatan dengan kepuasan nasabah. Penelitian ini menggunakan survei,dengan teknik sampling purposive sampling. Jumlah sampel sebanyak 63responden, sedangkan metode analisis data penulis menggunakan uji IPA danuji CSI. Hasil pengujian menunjukkan bahwa ada pengaruh yang signifikanantara pelayanan PT Ammana Fintek Syariah Jakarta Selatan dengan kepuasannasabah.
ANALISIS IMPLEMENTASI MMQ PADA BANK UMUM SYARIAH DAN UNIT USAHA SYARIAH DITINJAU PADA ASPEK YURIDIS DAN FATWA SYARIAH
Authors: Setiadi Faizin Rahmad
Published: August 2024

People's demand for housing continues to increase, while people's purchasing power is still low. The Islamic banking industry has a great opportunity to provide an alternative to sharia-based housing finance through Musyarakah Mutanaqisah (MMQ), namely joint financing based on ownership between the bank and the customer. This study tries to uncover the problems of implementing the MMQ contract from the legal and Islamic Shariah aspects. The analysis used in this study is descriptive analysis, namely the analysis carried out to evaluate the data or characteristics of reality. Keywords: housing finance, Musyarakah Mutanaqisah, product development
Technologies for measuring the dynamic parameters of rowing based on strain gauge systems
Authors:   Belarusian State University of Physical Culture, Guseinov Daniil , Permyakov Trofim ,   Belarusian National Technical University, Narkevich Anna ,   Belarusian State Technological University, Luckashevich Dmitrij ,   Scientific and Technological Park of BNTU "Polytechnic", Minchenya Aleksandr ,   LLC "Integrated Devices"
Published: August 2024

One of the criteria for the effectiveness of flatwater rowing biomechanics is the inter-action of the athlete with the surface of the water by an oar, as well as the effectiveness of transferring the efforts developed by the athlete to the boat for its advancement. The qual-ity of an athlete's movements with a paddle in rowing locomotives depends on the level of development of power abilities and can be quantified using strain gauge systems. However, the lack of recommendations describing the specifics of the preparation and use of such systems in training process significantly reduce the degree of their use. The article discusses the technical features and methodological foundations of the use of strain gauge systems to measure the efforts of an athlete when interacting with an oar in flatwater kayaking and canoeing. To substantiate the choice of the scheme of loading and securing the paddle when graduating the strain gauge system, as well as the place of its direct attachment to the forearm, the authors carried out mathematical modeling of diagrams of bending moments arising on the paddle in response to external forces, as a result of which three options for the support and orientation of the paddle are presented, depending on the location of key support points. To substantiate the method of attach-ing the strain gauge system to the forearm, a series of tests with paddle loading was car-ried out, as a result of which the presence of a linear relationship between the values of the external load and the readings of the strain gauge system was assessed, as well as the repeatability of the recorded data. Based on the results of the assessment, recom-mendations are presented for the practical application of the results of the study in as-sessing the speed and strength training of rowers on kayaks and canoes.
Avatares e Realidade Virtual: considerações budistas frente ao êxodo digital
Authors: Donadon Felipe
Published: August 2024

O presente artigo busca propor observações religiosas como possibilidade de anteparo diante das condições de utilização da Realidade Virtual como meio para lidar com situações de ansiedade e solidão. Tomando por base autores como Le Breton para fazer uma análise filosófica e sociológica do fenômeno desse escapismo digital para a realidade virtual, utiliza-se como base para a reflexão religiosa e espiritual as perspectivas do budismo apresentadas segundo o XIV Dalai Lama. Diante desta busca por um caminho do meio entre a tecnologia e as nossas vidas modernas, propõe-se alternativas diante das tratativas que os usuários que se utilizam da Realidade Virtual podem enfrentar.
Review of Deep Learning Techniques for Deepfake Image Detection
Authors: Niveditha Zohaib Hasan Princy , Sharma Saurabh , Paranjape Vishal , Singh Abhishek
Published: August 2024

Deepfake is an advanced synthetic media technology that generates convincingly authentic yet fake images and videos by modifying a person's likeness. The term "Deepfake" is a blend of "Deep learning" and "Fake," highlighting the use of artificial intelligence and deep learning algorithms in its creation. Deepfake generation involves training models to learn the nuances of facial attributes, expressions, motion, and speech patterns to produce fabricated media indistinguishable from real footage. Deepfakes are often used to manipulate human content, especially the invariant facial regions. The spatial relationship between facial attributes is crucial for creating a convincing, hyperrealistic deepfake output. Subtle inconsistencies in facial features, such as eye spacing, skin color, and mouth shape, can serve as indicators for detecting deepfakes. While many techniques have been developed to detect deepfakes, not all are perfectly accurate for every case. As new deepfake creation methods emerge, existing detection strategies must be continually updated to address these advancements. This paper reviews various deepfake image detection methods and deep learning techniques.
Advances in Power System Protection and Control
Authors: Choudhary Manish . , Dixit Akansha
Published: August 2024

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Machine Learning for Cost Estimation in the
Art Market: A Case Study on Sculptures
Authors: Singh Abhishek , Paranjape Vishal , Sharma Saurabh , Hasan Zohaib
Published: August 2024

This research presents a comprehensive analysis and prediction model for the cost of sculptures created
by artists using machine learning techniques. By leveraging the Random Forest Regressor algorithm, we aim to
improve the accuracy of cost predictions based on various features such as material, shipment type, installation
inclusion, and sculpture dimensions. The study addresses data preprocessing challenges including outlier detection and
categorical data encoding. The proposed model achieved a training
Comparative Analysis of Regression Techniques for Retail Sales Forecasting: A Case Study on Walmart
Authors: Singh Abhishek , Hasan Zohaib , Sharma Saurabh , Paranjape Vishal
Published: August 2024

This research paper presents a comprehensive analysis of various regression techniques for forecasting Walmart sales, focusing on Ridge Regression, Lasso Regression, Decision Tree Regression, and Random Forest Regression. Using historical sales data, the study evaluates the performance of these models based on metrics such as Root Mean Squared Error (RMSE) and R-squared (R²). The results indicate that while Ridge and Lasso regressions perform moderately, Decision Tree and Random Forest regressors significantly outperform them, with the latter achieving the highest predictive accuracy. This study provides valuable insights into the effectiveness of different regression models for retail sales forecasting, contributing to the optimization of inventory management and strategic planning in the retail sector.
Optimizing Deep Learning Models for RealTime Applications: Techniques for Improving Inference Speed without Sacrificing Accuracy
Authors: Sharma Saurabh , Paranjape Vishal , Hasan Zohaib
Published: August 2024

In recent years, the rapid advancement of deep learning models has enabled significant breakthroughs in various fields, including computer vision, natural language processing, and autonomous systems. However, deploying these models in real-time applications remains a challenge due to the high computational demands and latency constraints. This thesis explores techniques to optimize deep learning models for real-time applications by improving inference speed without sacrificing accuracy. The study begins by analyzing the fundamental trade-offs between model complexity and performance, and then investigates a range of optimization strategies, such as model pruning, quantization, knowledge distillation, and efficient neural architectures. Experimental evaluations are conducted across several real-world scenarios, including image and video processing, speech recognition, and autonomous navigation, demonstrating the effectiveness of these techniques. The results show that with appropriate optimization, deep learning models can achieve significant reductions in latency and computational load while maintaining high accuracy, making them viable for deployment in real-time environments. This work contributes to the growing body of knowledge in the field of efficient AI and provides a comprehensive framework for practitioners aiming to enhance the performance of deep learning systems in time-sensitive applications.
Precision in DNA Microarray Image Segmentation: A Comparative Analysis of Traditional and Machine Learning Methods
Authors: Sahu Ranu
Published: August 2024

DNA microarray technology has transformed genomics by allowing the simultaneous measurement of thousands of gene expression levels, offering vital insights into gene functions, interactions, and regulatory mechanisms. The precision of image processing techniques, particularly segmentation, is crucial for the accurate interpretation of microarray data. This study offers a comprehensive comparative analysis of various segmentation methods used in DNA microarray image processing. The proposed method achieved an accuracy of 97.6%, a mean absolute error (MAE) of 0.413, and a root mean square error (RMSE) of 0.224. By assessing the strengths and limitations of each method, a clearer understanding of their efficacy and potential applications is achieved. The segmentation techniques evaluated range from traditional methods like threshold and clustering to advanced approaches incorporating machine learning and deep learning algorithms. This analysis aims to improve the accuracy and reliability of DNA microarray data interpretation, thus contributing to the advancement of genomic research and supporting the broader goals of genomics and personalized medicine.
Advancing Agricultural Productivity and Sustainability through Artificial Intelligence: A Comprehensive Review
Authors: Choubey Khushboo , Choubey Khushboo , Dwivedi Mallika
Published: August 2024

The integration of Artificial Intelligence (AI) into agriculture has the potential to revolutionize traditional farming practices by enhancing productivity, efficiency, and sustainability. This study investigates the application of AI technologies in agriculture, focusing on their impact on resource management, crop yield optimization, and environmental sustainability. The proposed method demonstrates notable performance with an accuracy of 92.6%, Mean Absolute Error (MAE) of 0.440, and Root Mean Square Error (RMSE) of 0.200. These metrics highlight the method’s effectiveness in delivering precise and reliable predictions. By examining current trends, case studies, and emerging technologies, this paper provides a comprehensive analysis of AI’s role in modernizing agriculture and its capacity to drive future advancements in the sector.
AI-Enhanced Watermarking for Securing Medical Images: Integration of GANs and Blockchain Technology
Authors: Verma Saurabh , Pali Pankaj , Chourey Saloni , Chourasiya Radhika
Published: August 2024

The adoption of public cloud platforms has significantly transformed data storage, processing, and management paradigms, offering unprecedented scalability and flexibility. However, these advantages come with heightened security risks, including data breaches, unauthorized access, and compromised data integrity. Traditional security measures often prove inadequate in the face of the dynamic and complex nature of cloud environments. This study evaluates the effectiveness of machine learning (ML)-based security frameworks for enhancing data protection in public cloud infrastructures. By leveraging ML algorithms' capabilities to analyze large datasets, detect anomalies, and respond to threats in real-time, these frameworks offer a promising solution for robust cloud security. The methodology involves data collection from public cloud environments, feature extraction, selection of appropriate ML models, training and validation of these models, and performance evaluation against traditional security methods. Experimental results demonstrate that ML-based security frameworks significantly improve the detection and mitigation of security threats, offering superior data protection compared to conventional approaches. This research provides valuable insights into the deployment of ML-driven security solutions, contributing to the advancement of cloud security practices and informing organizational strategies for data protection in public cloud environments.
The Future of Farming: A Review of Artificial Intelligence in Agricultural Practices
Authors: Choubey Jaya , Pandey Divya , Patel Indukant , Bairagi Namrata
Published: August 2024

Artificial Intelligence (AI) is revolutionizing agricultural practices by introducing innovative solutions to enhance productivity, sustainability, and efficiency. This review paper provides a comprehensive analysis of the current state and future prospects of AI in agriculture, integrating the latest advancements in AI methodologies.The proposed method achieved impressive metrics, with an accuracy of 96.6%, a mean absolute error (MAE) of 0.403, and a root mean square error (RMSE) of 0.203. These metrics underscore the efficacy of AI techniques in optimizing various stages of farming—from planting and monitoring to harvesting and post-harvesting processes.The paper explores diverse AI technologies tailored for agricultural applications, showcasing their potential to mitigate challenges such as climate change resilience, resource management, and ensuring global food security. Real-world examples and case studies illustrate how AI-driven solutions are being implemented across different agricultural contexts, emphasizing their tangible benefits and operational challenges.Furthermore, the review discusses strategic recommendations for stakeholders to effectively integrate AI technologies into agricultural practices, ensuring sustainable and resilient farming systems for the future.
Machine Learning-Driven Approaches to Combat
Cybersecurity Challenges in Cloud Environments s
Authors: Choubey Jaya , Choubey Khushboo , Patel Shalu , Nema Mayank
Published: August 2024

In recent years, the rapid advancement of cloud computing has revolutionized the way organizations
store, process, and manage data. The cloud offers numerous benefits, including scalability, cost-efficiency, and
accessibility. However, alongside these advantages, significant concerns about data security have emerged. The
decentralized nature of cloud environments, coupled with the vast amount of sensitive information they handle, makes
them prime targets for cyberattacks and data breaches.Ensuring the security and privacy of data in cloud computing
environments is paramount. Traditional security measures, while still relevant, are increasingly being challenged by
sophisticated cyber threats that exploit vulnerabilities in cloud infrastructure. This necessitates the development and
implementation of more robust and intelligent security solutions.Machine learning (ML) has emerged as a promising
technology in the realm of cybersecurity, offering dynamic and adaptive approaches to threat detection and mitigation.
By leveraging ML algorithms, security systems can analyze large datasets to identify patterns, detect anomalies, and
predict potential security threats in real-time. This proactive approach is essential in an era where cyber threats are
constantly evolving and becoming more complex.This paper explores the current state of data security in cloud
computing, highlighting the challenges and vulnerabilities inherent in these environments. It then delves into the
innovative solutions based on machine learning that are being developed to enhance data security. By examining
various ML techniques and their applications in cloud security, this research aims to provide a comprehensive overview
of how machine learning can be harnessed to protect data in cloud computing.The proposed method demonstrates an
accuracy of 94.6%, significantly improving upon existing methods. Additionally, it achieves a mean absolute error
(MAE) of 0.403 and a root mean square error (RMSE) of 0.303, indicating high precision and robustness in detecting
anomalies and potential security threats. Through a detailed analysis of existing literature and case studies, this paper
will demonstrate the effectiveness of machine learning-based solutions in addressing key security concerns. It will also
discuss potential future directions for research and development in this critical area. Ultimately, this paper seeks to
contribute to the ongoing discourse on cloud security, offering insights into how machine learning can be utilized to
safeguard data and ensure the integrity of cloud computing systems.
Exploring Reinforcement Learning For Dynamic and Complex Scenarios in Robotics and Autonomous Driving
Authors: Anjum Gulafsha , Pandey Divya , Bose Sonali , . Akanksha
Published: August 2024

The integration of Reinforcement Learning (RL) into real-world applications such as robotics and autonomous vehicles has shown significant promise in enhancing the capabilities of these systems. RL offers a robust framework for developing intelligent systems that can learn and adapt through interactions with their environment, making it well-suited for dynamic and complex scenarios. This paper presents a detailed exploration of RL applications in robotics and autonomous vehicles, highlighting recent advancements, practical challenges, and future prospects.The proposed method achieves an accuracy of 96.6%, with a mean absolute error (MAE) of 0.401 and a root mean square error (RMSE) of 0.202. These results underscore the effectiveness of RL in improving the precision and reliability of robotic tasks and autonomous driving systems. By providing a comprehensive overview of RL principles and methodologies, and examining case studies and research breakthroughs, this paper elucidates the transformative potential of RL in driving innovation and efficiency in these critical areas. The findings contribute to the advancement of intelligent systems capable of seamless integration and enhancement of daily life.
Evaluating Machine Learning-based Security Frameworks for Public Cloud Data Protection
Authors: Tiwari Shivam , Soni Kuldeep , Yadav Nikita , Singhai Suyash
Published: August 2024

The rapid adoption of cloud computing services has fundamentally transformed data management and storage within organizations. Public cloud computing offers numerous advantages, including cost efficiency, scalability, and accessibility. However, these benefits come with significant concerns regarding data security. As sensitive data increasingly migrates to public cloud environments, the potential risks associated with unauthorized access, data breaches, and other cyber threats become more pronounced. Traditional security measures often prove insufficient to address the dynamic and complex nature of threats in public cloud settings, driving interest in advanced technologies such as machine learning (ML) to enhance data security. This paper explores the challenges associated with data security in public cloud computing and proposes a solution framework based on machine learning. The proposed method demonstrates an accuracy of 97.6%, a mean absolute error (MAE) of 0.403, and a root mean square error (RMSE) of 0.203. Various machine learning models and techniques are integrated into comprehensive security architecture capable of adapting to evolving threats and providing robust data security. The effectiveness of the proposed solution is evaluated through a series of experiments and case studies. The findings highlight the potential of machine learning in transforming data security practices in public cloud computing. As organizations continue to rely on cloud services, adopting advanced security measures will be critical to ensuring the confidentiality, integrity, and availability of their data. This paper contributes to the growing body of knowledge on cloud security and provides a foundation for future research and development in this domain.
Enhancing Sustainable Urban Development:
Evaluating IoT Technologies with Advanced
Performance Metrics
Authors: Anjum Gulafsha , Singh Abhishek , Daheriya Kriti , Mahanti Mahanti
Published: August 2024

The rapid growth of urban areas in the 21st century has led to unprecedented pressures on infrastructure,
resources, and environmental sustainability. To address these challenges, the concept of "smart cities" has emerged as a
critical strategy, utilizing advanced technologies to improve urban living, enhance operational efficiency, and promote
sustainable development. Central to this vision is the integration of Internet of Things (IoT) technologies, which are
poised to revolutionize urban environments by creating interconnected, efficient, and responsive systems. This study
investigates the future trajectory of smart cities with a focus on the role of IoT technologies in achieving sustainable
urban development. The research involves a proposed method evaluated through various performance metrics. The
method demonstrated high accuracy, achieving a value of 97.2%. Additionally, it exhibited a mean absolute error
(MAE) of 0.405 and a root mean square error (RMSE) of 0.204. This performance indicates the effectiveness of the
proposed approach in optimizing urban resource management and service delivery. By reviewing current smart city
initiatives, identifying prevailing challenges and opportunities, and analyzing case studies and emerging trends, this
study provides a comprehensive understanding of how IoT technologies can drive sustainable urban transformation and
contribute to the creation of resilient, livable cities.
Enhancing Plant Disease Diagnosis Integrating Machine Learning with Remote Sensing for Improved Accuracy and Reliability
Authors: Pali Prof. Pankaj , Sharma Prof. Saurabh , Paranjape Prof. Vishal , Hasan Prof. Zohaib
Published: August 2024

Ensuring crop health and maximizing agricultural productivity hinge on effective plant disease management. Traditional diagnostic methods, often based on manual inspection and laboratory tests, are labor-intensive and limited in their ability to detect diseases early. Recent advancements in machine learning (ML) and remote sensing technologies present promising solutions to improve the accuracy and efficiency of diagnosing plant diseases. This research explores the use of advanced ML techniques for diagnosing plant diseases through remote sensing data. Technologies such as satellite imagery, drones, and multispectral sensors provide detailed, high-resolution data on crop health and environmental conditions. By combining these technologies with sophisticated ML algorithms, the proposed method achieves a diagnostic accuracy of 96.9%. Additionally, the method's performance metrics include a mean absolute error (MAE) of 0.407 and a root mean square error (RMSE) of 0.306, highlighting its precision and effectiveness in early disease detection and severity assessment. The results underscore the significant potential of integrating ML with remote sensing to achieve more accurate and timely plant disease diagnosis. This paper describes the methodologies used, addresses the challenges and limitations encountered, and proposes future research directions to further enhance plant disease management through these innovative technologies.
Innovative Techniques for Object Detection
and Recognition: A Study on Accuracy and
Efficiency Improvements
Authors: Pateriya Nidhi , Hasan Zohaib , Mehra Jyoti , Sahu Akash
Published: August 2024

In the domain of computer vision, object detection and recognition play essential roles in applications
such as autonomous driving and surveillance. This paper presents a new approach aimed at improving both accuracy
and efficiency in these technologies. The proposed method utilizes cutting-edge algorithms and structural
enhancements to achieve substantial improvements. It demonstrates an accuracy of 95.8%, indicating its superior
capability in detecting and classifying objects. The method also achieves a Root Mean Squared Error (RMSE) of 0.206,
signifying low prediction errors, and a Mean Absolute Error (MAE) of 0.405, highlighting its precision. These metrics
underscore the method’s potential to advance object detection and recognition technologies by offering enhanced
accuracy and dependability.
Advancing Cloud Security: A Comprehensive Survey of Machine Learning Approaches for Threat Detection and Data Protection
Authors: Sharma Prof. Saurabh , Pali Prof. Pankaj , Singh Prof. Abhishek
Published: August 2024

The swift expansion of cloud computing has revolutionized how businesses and individuals handle data storage, management, and processing, providing unparalleled flexibility, scalability, and cost efficiency. Nonetheless, this growing dependence on cloud services has brought about considerable security challenges, demanding sophisticated solutions to safeguard data confidentiality, integrity, and availability. Conventional security measures frequently prove inadequate in managing the intricate nature of cloud environments, thereby increasing interest in the application of machine learning (ML) techniques to enhance cloud security.This paper presents an extensive survey of contemporary ML approaches for cloud security, encompassing supervised, unsupervised, and reinforcement learning methods. The survey assesses the effectiveness of these techniques in various security areas, including threat detection, anomaly identification, intrusion prevention, and data protection. To demonstrate the practical effectiveness of these methods, the proposed approach in this study achieves a notable accuracy of 96%, with a mean absolute error (MAE) of 0.485 and a root mean square error (RMSE) of 0.203. These metrics underscore the proposed method’s capability to deliver precise and dependable security solutions.By evaluating the strengths and weaknesses of various ML strategies, this study aims to provide a thorough overview of the current state of cloud security and pinpoint future research opportunities in this developing field of the findings are discussed, and future research directions are proposed.
"Block-chain-Driven Machine Learning: A
Novel Framework for Enhancing Digital
Security and Predictive Analytics"
Authors: Vishwakarma Abhishek , Paranjape Vishal , Hasan Zohaib
Published: August 2024

The integration of Blockchain technology and Machine Learning has garnered significant attention due
to its potential to address complex challenges across various domains. This study explores the development of a novel
framework that synergizes Blockchain’s decentralized, immutable ledger with the predictive capabilities of Machine
Learning to enhance digital security and data privacy. The proposed method achieves an impressive accuracy of 97.6%,
demonstrating its effectiveness in predictive analytics. Furthermore, the method exhibits a mean absolute error (MAE)
of 0.403 and a root mean square error (RMSE) of 0.203, indicating a high level of precision and reliability. These
results underscore the potential of the proposed framework to contribute to advancements in intelligent systems,
offering robust, efficient, and secure solutions across multiple applications.
The Role of AI in Sentiment Analysis for
Restaurant Quality Prediction: A
Methodological Approach
Authors: Tiwari Shivam , Sharma Saurabh , Singh Abhishek
Published: August 2024

In today’s digital age, sentiment analysis of user-generated content has become a critical tool for
businesses, particularly in the restaurant sector, where online reviews play a pivotal role in shaping consumer choices.
This study explores the use of sentiment analysis and artificial intelligence (AI) to predict top restaurants by analyzing
customer feedback from various online sources. The proposed method employs advanced AI techniques, achieving a
remarkable accuracy of 97.6% in assessing restaurant quality. The model also exhibits strong error performance, with a
Mean Absolute Error (MAE) of 0.403 and a Root Mean Square Error (RMSE) of 0.203, underscoring its efficiency in
reducing prediction errors. These results highlight the model’s enhanced ability to derive valuable insights from
complex datasets and deliver precise, data-driven recommendations for diners. By systematically analyzing sentiment
scores from user reviews, the study introduces a novel approach to improving consumer decision-making and
motivating restaurants to elevate their service quality. This research contributes to the field by showcasing the potential
of AI-driven sentiment analysis in enhancing dining experiences and deepening the understanding of consumer
preferences.
Enhancing Heart Disease Prediction Accuracy through a Novel Hybrid Machine Learning Model
Authors: Pali Prof. Pankaj , Sharma Prof. Saurabh , Singh Prof. Abhishek , Paranjape Prof. Vishal
Published: August 2024

Heart disease continues to be one of the leading causes of death globally, resulting in millions of fatalities each year. Despite substantial progress in medical science, the early detection and accurate prediction of heart disease remain significant challenges. Traditional diagnostic methods, though effective, often fail to identify at-risk individuals early enough, leading to delayed treatment and higher mortality rates.This research paper introduces a novel hybrid machine learning model aimed at enhancing the accuracy of heart disease prediction. By integrating various machine learning techniques, the proposed model harnesses their complementary strengths to provide a robust and reliable tool for early diagnosis. The study assesses the performance of the hybrid model against traditional singlemodel approaches using a comprehensive dataset. The proposed method achieved an accuracy of 95.5%, a mean absolute error (MAE) of 0.403, and a root mean square error (RMSE) of 0.203, demonstrating its potential to significantly improve prediction accuracy.The paper presents a detailed analysis of the proposed hybrid model, including its design, implementation, and evaluation. The results indicate that the hybrid approach not only surpasses individual models but also offers a scalable solution for clinical applications. The implications of this research are farreaching, as it can improve patient outcomes through timely and accurate diagnosis, ultimately contributing to better healthcare management and reduced healthcare costs.
High-Accuracy Machine Learning Model for Predicting Diabetes Mellitus Progression
Authors: Pali Prof. Pankaj , Verma Prof. Saurabh
Published: August 2024

Diabetes mellitus, a chronic metabolic disorder marked by persistent hyperglycemia, presents a major global health challenge, affecting over 463 million adults worldwide. Timely and accurate prediction of disease progression is crucial for mitigating complications and improving patient outcomes. This research paper details the development and validation of an advanced machine learning model designed to predict diabetes progression. The proposed model integrates various machine learning algorithms, such as regression analysis, decision trees, and neural networks, to enhance predictive accuracy. Feature selection techniques are employed to identify the most relevant predictors, ensuring a comprehensive yet streamlined approach. The model achieves a high accuracy of 94.4%, with a mean absolute error (MAE) of 0.404 and a root mean square error (RMSE) of 0.206, highlighting its effectiveness in predicting diabetes progression. The primary objectives of this study are to construct a machine learning model capable of accurately predicting diabetes progression, validate the model with real-world patient data, and compare its performance against existing predictive models. The findings of this research hold significant implications for diabetes care, facilitating personalized treatment plans and proactive interventions, ultimately enhancing patient quality of life. Additionally, the insights gained from this study may guide future research and development in the field of predictive analytics for chronic disease management.
Enhancing IoT Data Processing Efficiency with AI: A Study on Edge Computing
Authors: Pandey Prof. Divya
Published: August 2024

The rapid advancement of Internet of Things (IoT) devices has led to an exponential increase in the volume of data generated at the network edge. Edge computing, which processes data closer to its source rather than relying solely on centralized cloud computing, has emerged as a pivotal solution to address the latency, bandwidth, and privacy concerns associated with IoT data management. However, the integration of Artificial Intelligence (AI) into edge computing presents unique challenges, particularly in terms of resource constraints, energy efficiency, and realtime processing capabilities.This paper presents a machine learning-based solution tailored for edge computing environments, designed to enhance data processing efficiency and accuracy. The proposed method demonstrates significant improvements, achieving an accuracy of 97.6%, a mean absolute error (MAE) of 0.403, and a root mean square error (RMSE) of 0.203. By leveraging lightweight algorithms and optimizing resource allocation, the solution effectively bridges the gap between the computational demands of AI and the constraints of edge devices.The implications of this research are far-reaching, with potential applications across various sectors, including healthcare, smart cities, and industrial automation. The proposed solution not only improves operational efficiency but also contributes to the overall scalability and sustainability of IoT ecosystems.
AI-Powered Predictive Analytics for Semiconductor Manufacturing: Case Studies and Industry Applications
Authors: Niveditha Prof. Kalukuri Princy , Patel Prof. Abhishek , Sahu Prof. Ranu
Published: August 2024

The semiconductor manufacturing industry, a foundation of modern technology, grapples with significant challenges due to the intricacies and precision required in fabrication processes. Traditional methods often prove inadequate in managing the complexities and rapid changes in semiconductor processes. This paper explores the transformative potential of AI-powered predictive analytics in semiconductor manufacturing, particularly in defect detection, process optimization, and maintenance scheduling.Utilizing machine learning and deep learning techniques, AI-powered predictive analytics can process vast amounts of manufacturing data, uncover patterns, and predict outcomes with high accuracy. The study encompasses a thorough literature review, pinpointing current techniques and identifying knowledge gaps. Customized AI algorithms were developed and validated through simulations and realworld case studies, demonstrating notable enhancements in process efficiency, yield, and cost reduction.Results show that AI-enhanced models achieved up to 95% accuracy in defect detection, a 30% improvement in process optimization, and a 20% reduction in maintenance costs compared to traditional methods. Detailed case studies illustrate specific industry applications, highlighting tangible benefits such as improved spectrum utilization, reduced interference, and enhanced communication reliability.The findings offer valuable insights and practical recommendations for incorporating AI technologies into semiconductor manufacturing workflows. By addressing both technical and practical aspects, this research adds to the growing body of knowledge on AI's impact in the semiconductor sector and provides a comprehensive framework for future implementation.
Rapid screening of fibrous plaster ceilings health status using a laser Doppler vibrometer
Authors: Cacciola Pierfrancesco , Mayfield R. Sarah
Published: August 2024

The pressing necessity to determine a fast, efficient and rigorous approach to assess the health status of fibrous plaster ceilings across the UK motivated the research presented in this paper. Health monitoring techniques commonly used in the civil engineering sector are generally based on the measurement of the dynamic response of structures, such as buildings or bridges, using sensors (mainly accelerometers). This approach can certainly be adopted for suspended ceiling damage identification studies as well; however, the use of sensors directly connected to the ceiling might be prohibitive and time consuming and, therefore, more suitable for permanent monitoring systems. In this regard, in this study we explore the use of the laser Doppler vibrometer for the rapid screening of fibrous plaster ceilings health status. First, rigorous detailed studies have been conducted in the laboratory to assess damage using an ad hoc manufactured fibrous plaster panel using different sources of excitation in conjunction with the laser vibrometer. Damage indicators for the fibrous plaster ceiling have been introduced and validated through the laboratory experiments. Finally, the proposed approach based on laser vibrometer measurements and damage indicators has been applied to the Institution of Civil Engineers library ceiling in London, UK.