bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancement of Security and Privacy of Smart Contracts in Blockchain
Authors: Syed Khurram Hassan
Published: May 2024

Smart contracts, leveraging the power of blockchain technology, have revolutionized the execution and enforcement of agreements. However, their adoption also brings forth substantial challenges in terms of security and privacy. This research paper aims to identify the recent areas of focus and provide a comprehensive perspective on blockchain applications and smart contracts, highlighting their main issues and corresponding solutions. Furthermore, it seeks to address the gaps in current research and outline future avenues of investigation. The primary objective is to assess the security and privacy concerns associated with smart contracts in blockchain and propose effective measures to enhance their robustness. By conducting a thorough analysis of vulnerabilities, attack vectors, and privacy considerations, this study offers valuable insights into the risks involved in smart contracts. It also puts forth practical solutions and best practices to mitigate these risks, ensuring a more secure and privacy-preserving environment for the deployment and execution of smart contracts.
LEGISLAÇÃO E REALIDADE: O IMPACTO DA EXCLUSÃO SOCIAL NAS PESSOAS PORTADORAS DE DOENÇAS AUTOIMUNES
Authors: Machado Danielli Tenório Taveira
Published: May 2024

Os portadores de doenças autoimunes são titulares de direitos humanos previstos em lei, no entanto o conhecimento social acerca de seus sintomas crônicos é parco. Seja por desconhecimento, seja pela indiferença oriunda da liquidez das relações hodiernas, o fato é que a exclusão social potencializa o sofrimento de tais pessoas. O problema de pesquisa abordado foi de que maneira a exclusão social influencia o bem-estar biopsicossocial dos indivíduos portadores de doenças autoimunes. Como objetivo geral, procedeu-se à investigação do impacto da exclusão social na qualidade de vida desses doentes crônicos e à proposição de estratégias para inclusão efetiva deles. A elaboração deste artigo fundamentou-se na abordagem qualitativa, por meio de análise bibliográfica. O exame da literatura existente revela uma dissociação entre o preconizado na normal legal e o tratamento discriminatório recebido pelos doentes crônicos. Os resultados do estudo propõem a necessidade crítica de aprimorar a legislação para viabilizar a inserção e a manutenção do portador de doença autoimune no mercado de trabalho, ações de sensibilização sociais, dentre outras abordagens com a finalidade de fomentar uma cultura de valorização e de respeito à singularidade dos portadores de doenças autoimunes.
Standardizing male pelvic anatomy teaching using a clinical enrichment video
Authors: MacDonald Landan , Ory Jesse , Pulakunta Thejodhar , MacLellan Dawn L.
Published: May 2024

Introduction: Evolving trends in medical education and modern curricular changes have reduced the amount of time and resources allocated for anatomy education. As the amount of dedicated anatomy education time decreases, more self-directed learning is required. Cadaveric dissection and didactic teaching have been supplemented with multimedia, clinical anatomy, and imaging for over 20 years with mixed results. Specifically, the use of video-based anatomy teaching increases medical learning, if done methodically.
Methods: A 20-minute video was produced highlighting surgical anatomy using the following operative cases: perineal anatomy (artificial urinary sphincter case), inguinal and testicular anatomy (scrotal orchidopexy for acute testicular torsion), prostate anatomy (robotic radical prostatectomy), and bladder anatomy (endoscopy). The annotated video was shown to first-year medical students. Pre- and post-multiple-choice question quizzes were given to the students. Once submitted, the students completed a survey.
Results: Overall, 191 first-year medical students participated in our study. Average scores were similar between each quiz (50±16% vs. 49±17%); there was no statistically significant change. Seventy-seven percent of participants felt the video improved their knowledge of urologic anatomy and 83% agreed the video should be shown to future classes. Sixty percent of participants felt the video solidified their anatomy knowledge. Although 78% felt the video was stimulating and entertaining, 43% of the students felt the video increased their interest in pursuing urology as a career choice.
Conclusions: Anatomy teaching can be supplemented using surgical videos, especially in a time when in-person anatomy teaching is limited. Further study is required to determine if this teaching modality improves long-term anatomy knowledge retention.
Variability in perioperative management of pheochromocytoma in Canada
Authors: Qu Linda C. , Istl Alexandra C. , Tang Elaine , Chaulk Richard C. , Gray Daryl
Published: May 2024

Introduction: Despite recent consensus guidelines, there is substantial variability in the management of pheochromocytomas. Our study aimed to characterize the current state of perioperative pheochromocytoma management by Canadian surgeons.
Methods: A 23-item online survey was sent to Canadian surgeons who perform adrenalectomies for pheochromocytoma. We assessed personal and institutional practices, including preoperative and postoperative management.
Results: National response rate was 51.8%. Surgeons from nine provinces responded; the majority were general surgeons (70.4%). Reviewing pheochromocytoma patients at a multidisciplinary tumor board was not routine practice (12%) and only 42.3% consistently referred patients for genetic testing. Preoperative α- and β-blockade at half of the respondent institutions were performed by endocrinology alone (53.8%), with the other half employing a multidisciplinary approach. Half of respondents admitted their pheochromocytoma patients to hospital prior to the day of surgery. Postoperatively, 11.5% of respondents routinely admitted their patients to the ICU for monitoring based on personal preference or institutional convention. Multivariate analyses found no significant relationships between demographics or preoperative factors and perioperative management.
Conclusions: Perioperative surgeon management of patients undergoing adrenalectomy for pheochromocytoma was highly variable across Canada. Less than half of respondents routinely refer patients for genetic testing, despite recent practice guidelines. Surgeon preference and institutional convention are the main drivers behind preoperative admission and routine postoperative ICU admission, despite a lack of evidence to support this practice.
Соціальна профілактика негативних явищ: віктимологічний контекст
Authors: Тюльпа Т. М.
Published: May 2024

У представленому дослідженні розглядаються наукові позиції вчених щодо особливостей обґрунтування негативних соціальних явищ, засновані на детермінованому підході та теорії деструктивної особистісно-соціальної інтеракції, яка є формою реалізації сутнісних та предметно-змістовних характеристик особистості та суспільства. Охарактеризовано різні погляди дослідників щодо проблеми поведінки особистості як способу взаємодії її з суспільством. Доведено,що знання сутності та закономірностей походження та розвитку негативних явищ служить основою теоретичного конструювання та соціально-практичного використання інноваційної моделі системи соціальної профілактики. Визначено профілактику як важливий засіб запобігання розвитку будь-яких негативних процесів на ранніх стадіях з метою попередження можливих психологічних, фізичних та соціокультурних проблем у конкретних індивідів або груп ризику. Проаналізовано сутність та завдання соціальної профілактики, спрямованої на попередження та усунення глибинних причин та умов, що прямо чи опосередковано здійснюють негативний вплив на вчинки та дії людини. Визначено, що віктимність особистості є складним соціальним явищем, соціальною патологією, а її виникнення пов'язане з рівнем розвитку в суспільстві негативного, деструктивного контексту. Подано визначення віктимологічної профілактики, яку слід розуміти як включену в соціальну систему попередження негативних явищ підсистему загально-соціальних та спеціально-кримінологічних заходів, спрямованих на зниження індивідуальної та масової віктимності за допомогою усунення негативних віктимних характеристик поведінки, нейтралізації передумов виктимізації у вигляді сукупності індивідуально спрямованих заходів шляхом виявлення, нейтралізації та попередження особистісних та факторних детермінант даного процесу. Зазначено об'єкти віктимологічної профілактики: соціальні причини та умови віктимізації громадян; особистісні причини та умови віктимізації; характер особистісно-соціальної взаємодії жертви з навколишнім соціальним середовищем; особистість та спосіб життя жертв негативних життєвих обставин та віктимогенних особистостей. Виявлено, що віктимологічний контекст соціальної профілактики дозволяє осмислити багаточисельні соціальні проблеми людини в ситуації стресу, біди, горя, насильства, залежності, деривації, здійснює проникнення в сутність важливих особистісних процесів, розкриваючи резерви та можливості соціального оздоровлення суспільства та людини.
Movie Lens – Movie Recommendation System Using Deep Learning
Authors:   School Of Engineering B.Tech ComputerScience-AIML Malla Reddy University India, B Sreeja
Published: May 2024

Recommendation systems, the best way to deal with information overload, are widely utilized to provide users with personalized content and services with high efficiency. Many recommendation algorithms have been researched and deployed extensively in various e-commerce applications, including the movie streaming services over the last decade. However, sparse data cold-start problems are often encountered in many movie recommendation systems. In this paper, we reported a personalized multimodal movie recommendation system based on multimodal data analysis and deep learning. The real-world MovieLens datasets were selected to test the effectiveness of our new recommendation algorithm. With the input information, the hidden features of the movies and the users were mined using deep learning to build a deep-learning network algorithm model for training to further predict movie scores. With a learning rate of 0.001, the root mean squared error (RMSE) scores achieved 0.9908 and 0.9096 for test sets of MovieLens 100 K and 1 M datasets, respectively. The scoring prediction results show improved accuracy after incorporating the potential features and connections in multimodal data with deep-learning technology. Compared with the traditional collaborative filtering algorithms, such as user-based collaborative filtering (User-CF), item-based content-based filtering (Item-CF), and singular-value decomposition (SVD) approaches, the multimodal movie recommendation system using deep learning could provide better personalized recommendation results. Meanwhile, the sparse data problem was alleviated to a certain degree. We suggest that the recommendation system can be improved through the combination of the deep-learning technology and the multimodal data analysis. Keywords: recommendation system; deep learning; matrix factorization; multimodal technique
LOW POWER DESIGN AND ANALYSIS OF ADAPTIVE RADIOS
Authors:   Dept. of VLSI Design, AVS Engineering College, Salem, S Sowmiya
Published: May 2024

This undertaking presents a noteworthy low-power procedure, RF Power Gating, proposing a unique change of the Dynamic Time Proportion (ATR) inside the RF front end at an image time scale. Adapting receiver power consumption to performance requirements without altering the fundamental architecture is the unique challenge that this method addresses. The effect of changing the ATR on the performance of the Bit Error Rate (BER) is carefully examined, with a focus on minimum-shift keying signaling and a basic estimator. To gauge power decrease unequivocally, a complete framework level energy model is determined, considering the qualities and power utilization of individual blocks. This model gives experiences into the particular supporters of force decrease. The review consolidates BER results with the energy model to pinpoint the ideal ATR meeting plan requirements. When this method is applied to the IEEE 802.15.4 standard, it can be seen that an ATR of 20% is a sensible compromise that meets the requirements for the packet error rate while also achieving the highest possible energy reduction ratio. Further investigation utilizing run of the mill block power utilizations uncovers a reachable energy decrease proportion of around 20%. Quite, while power-gating is widely applied, significantly more noteworthy energy decrease proportions (∼60%) are achievable. The proposed procedure is executed in Verilog, displaying its functional materialness, and consistently coordinated into Xilinx apparatuses. The outcomes highlight not just the viability of RF Power Gating in accomplishing a critical decrease in energy utilization yet additionally its versatility to different situations, making it a promising methodology for low-power plan in versatile radios. Key Words: Active Time Ratio (ATR) Blunder Rate (BER) and IEEE 802.15.4 standard.
AUTOMATIC PET FEEDER
Authors:   The Oxford College of Engineering, Bengaluru, Cyri A Diana
Published: May 2024

The project is titled "AUTOMATIC PET FEEDER". The primary goal of this project is to provide automatic feeding for pets and serve as a remote monitoring system. Given the growing incorporation of Internet of Things (IoT) technologies in different areas of everyday life, this project offers a detailed answer for improving the care and surveillance of dogs in a day care environment. The suggested system utilizes the features of ESP 32, an adaptable and affordable single-board computer, to establish a smart and automated dog care setting. It comprises of two primary parts: an automated feeding system and a remote surveillance system. The automatic feeding system is created to offer accurate and timed meals to dogs, meeting the nutritional needs and dietary requirements of each pet. By using advanced feeding technology, the system guarantees the timely and controlled distribution of food portions for dogs at the day care center, which helps improve their health and wellness. The remote monitoring system allows pet owners and day care staff to keep an eye on the dogs' behavior and activities in real time, providing reassurance. ESP 32 cameras are strategically positioned to record live video of the dog play area, enabling owners to watch their pets from a distance. Furthermore, the system includes motion detection and alert features to inform staff of any abnormal or risky behaviors. Keyword— Automated pet feeding, Arduino,Remote management, Controlled
Intelligent System to Secure the Information based on Innovative Games
Authors:   Department of CSE, JNN College of Engineering, B Pooja
Published: May 2024

Cryptography, while essential for data security, faces challenges such as interception during transmission, potentially exposing information to unauthorized parties. To combat this, game-based cryptography techniques have emerged, offering innovative solutions to safeguard sensitive data. This project focuses on developing security models inspired by popular games like Sudoku puzzles. These models aim to enhance data security while engaging users through interactive gameplay. As encryption gains increasing significance amidst rising data breaches and cyber threats, traditional methods often lack user engagement and accessibility. The proposed game-based encryption approach seeks to address these shortcomings by providing a more intuitive and interactive means of securing data across various complexity levels. By integrating puzzle elements into the encryption process, the project aims to bridge the gap between encryption experts and general users, making data security more understandable and engaging for everyone. Keywords : Cryptography, Encryption, Information, Games
Feature Evaluation of Emerging And E-Learning System
Authors:   Vignan’s Institute of Information Technology(A), Beside VSEZ,Duvvada,Vadlapudi Post, Gajuwaka, Visakhapatnam-530049., Harshini M.Ratna
Published: May 2024

With the development of artificial intelligence, adaptive e-learning has gained more and more attention. As every approach to e-learning becomes less appealing and the quality of as the popularity of online courses grows, they switch to more customized, flexible learning to work with students and achieve better learning results. The focus of the schools is on organization, awareness, and examination. pupils and achieve better learning results The focus of the schools is Methods for bringing creativity into the curriculum and vision E-learning worries are widespread. It is an exam issue for each and every one of us. The goal of this study's analysis is to identify potential outcomes from assessing e-learning models with artificial intelligence (AI) methodologies like supervised, semi-supervised, and unsupervised reinforcement learning advancements.
Leveraging Perspective Management in Marketing Strategy: A Comprehensive Analysis
Authors:   Alamuri Ratnamala Institute of Engineering and Technology, Avhad Nikhil Dattatray
Published: May 2024

This research paper delves into the pivotal role of perspective management in shaping effective marketing strategies. In today's dynamic business environment, where consumer behaviors and preferences constantly evolve, understanding and influencing perspectives is essential for sustainable market success. This study adopts a comprehensive approach to analyze the interplay between perspective management and marketing strategy, drawing insights from various disciplines such as psychology, sociology, and marketing. Key aspects explored include the influence of cognitive biases, cultural differences, and social norms on consumer perspectives, as well as strategies for effectively managing and leveraging these perspectives to drive brand engagement, loyalty, and market penetration. Through a combination of theoretical frameworks, empirical evidence, and case studies, this paper offers valuable insights for marketers seeking to enhance their understanding of consumer perspectives and optimize their marketing strategies accordingly. Key Words: Perspective management, Marketing strategy, Consumer behavior, Cognitive biases, Cultural differences, Brand engagement.
Facial Recognition Attendance System
Authors:   Department Of Information Technology  Noida Institute Of Engineering and Technology, Greater Noida, l Nezam Mohammad Afzal
Published: May 2024

By identifying students' frontal faces from classroom photos, this research attempts to construct a general face detection and identification system that will automate the process of gathering school attendance. The main issue with conventional attendance management systems is the accuracy of the data that is gathered. Numerous automated techniques are in use, including biometric attendance. Nonetheless, the effectiveness of these methods is always impacted by scanning equipment technical issues. In order to enhance data quality and information accessibility for authorised parties, this article uses OpenCV for face recognition and principal component analysis techniques for face detection. The database that holds user data in the system was developed using SQL, while the Python programming language was utilised to create the suggested system. After testing, it was determined that the new system is safe and secures students' identities by providing an anonymous attendance environment. Keywords: (ABS) Face Detection; Attendance; Machine Learning; Database; Principal component analysis;CNN; OpenCV and Face Recognition
Strengthening of Cutout in a Composite Slab
Authors:   M.Tech Student, Department of Civil Engineering, Vidya Academy of Science and Technology, Thrissur, Kerala, Kesav Prince
Published: May 2024

This work focuses on the experimental investigation of the behavior of a biaxial geogrid-stimulated composite slab with an opening close to the support. The usage of composite decking slabs in contemporary buildings is growing daily because of their cost-effectiveness in terms of strength, finance, and labor efficiency. Therefore, a cutout in these slabs causes a loss of strength. The mechanical behavior of composite floors, including the deck slab's stability, rigidity, and bearing capacity, is impacted by openings in the floor structures. Typically, openings are made for storm drain pipes, mechanical ducting, or a collection of tiny holes. This research investigates the effectiveness and proper orientation of strengthening the composite slab with geogrids to mitigate the impact of openings on structural performance Key Words: Composite Slab, Cutout, Geogrid, Flexural Behaviour
Pharmaceutical Assessment of Body Lotion: A Herbal Formulation and its Potential Benefits
Authors:   B. Pharm Semester-VIIIth, Vasantrao Rathod Arti
Published: May 2024

Background: Protective layers of skin cover the body. Plant-based herbal body lotion soothes and moisturises. Treatments commonly include succulent aloe vera, which heals, reduces pain, and moisturises. For hundreds of years, it has healed skin burns and injuries. Aim: This study aims on the pharmaceutical assessment of Aloe-vera by formulating an herbal Body lotion. Material and Method: Aloe-vera, Honey, Glycerin, Rose Water and Triethanolamine were taken for the formulation of herbal body lotion. Evaluation parameters were also performed to evaluate the formulation and to make sure that the subjected formulation is not harmful for the human mankind. Result: The aloe vera body lotion was formulated by using various type of ingredients such as Aloe- vera, glycerin, rose water, honey and Triethanolamine. Aloe-vera contain antimicrobial and hydrating properties protect skin against microbial degradation and moisture to skin. Conclusion: herbal body lotion is prepared for tropical administration. Aloe vera is used in lotion to provide synergistic effect as well as moisturizing effect on skin. Herbal remedies are experiencing a surge in popularity worldwide. The utilization of aloe vera, honey, Coconut oil, Lemon Oil and glycerin in the formulation of an herbal lotion is an exemplary notion. Keywords: Herbal body lotion, aloe-vera, honey, skin, glycerin, pharmaceutical assessment etc.
CoolGuru E-Commerce Website using Sentiment Analysis Prediction
Authors:   IIMT College of Engg, Greater Noida, UP, India, Rajbhar Abhishek
Published: May 2024

The Cool Guru is a brick-and-mortar establishment that has been repurposed into a fulfillment center. These distribution hubs are not accessible to the general public, allowing for ample inventory space and efficient order fulfillment. Cool Gurus offer an extensive and ever-expanding array of resources, enabling customers to purchase products online with options for same-day delivery or even within hours, as well as in-store pickup. While the concept is not novel, various companies, including Whole Foods, Walmart, Target, Bed Bath & Beyond, and numerous major clothing retailers, have employed similar strategies. However, with brick-and-mortar stores facing challenges during closures, the prevalence of Cool Gurus has surged. Our team is currently developing a Cool Guru utilizing NLP technology to analyze real-time customer feedback and sentiment, enabling us to automate product ranking and enhance customer satisfaction. Keywords: Cool Guru, brick-and-mortar, lockdown, shoppers, Bed Bath & Beyond, Natural Language Processing, Sentiment Analysis.
Role of Micro Finance in uplifting Indian Economy: An Analysis
Authors:   Amity University, Lucknow, Singh Anwita
Published: May 2024

Microfinance institutions (MFIs) act as indispensable financial intermediaries, providing small loans and services to individuals excluded from mainstream banking due to socio-economic constraints. Their significance lies in empowering marginalized communities, promoting financial inclusion, and fostering opportunities for income generation and entrepreneurship. Notably, microfinance contributes significantly to poverty alleviation and gender equality, particularly in empowering women, who often constitute a substantial portion of its beneficiaries. However, the effectiveness of microfinance in India faces multifaceted challenges. This paper addresses hurdles such as exorbitant interest rates, insufficient infrastructure, and the urgent need for effective regulation to prevent the exploitation of vulnerable borrowers Overall, this paper will find out the indispensable role of microfinance in India's economic development while acknowledging the challenges hindering its full potential. Unravelling these dynamics provides policymakers, practitioners, and stakeholders with a comprehensive understanding of microfinance in the Indian context, crucial for formulating informed strategies that foster sustained growth and ensure its continued positive impact on economic development. The insights gleaned from this study are vital for shaping the trajectory of microfinance in India.
DATA ANALYSIS OF METROLOGICAL DATA
Authors:   JAGAN INSTITUTE OF MANAGEMENT STUDIES,, Parhwal Divyam
Published: May 2024

This project report is set to give an interactive visualization and analytical presentation for Meteorological records in Finland. These Meteorological records Data of Finland is recorded by way of integrating the three current infrastructures for numerical weather prediction, observational information and satellite tv for pc image processing and this is recorded. The Meteorological data used in the study consists of near- floor atmospheric elements including wind direction, apparent temperature, cloud layer(s), ceiling peak, visibility, current weather, wind velocity, cloud cowl and precipitation amount and so on. The data consists of hourly recorded data for the past ten years of Finland from 2006-04- 01 at time 00:00:00.000 to 2016-09-09 at time 23:00:00.000. The analysis had been performed out for 2-m floor temperature. Through this analysis, all the valuable insights into the changing weather patterns and environmental conditions in Finland are analyzed and presented in an interactive visualization, providing a solid foundation for understanding and addressing the impacts of Global Warming in the region. This project main objective is to show the complete analysis of the influences of the Global Warming on the Apparent temperature & humidity in Finland over the course of 10 years from 2006 to 2016. Keywords — Data Analysis, Data Visualization, Meteorological Data, Climate Change, Global Warning, Apparent Temperature, Humidity
INTERNET MARKETING
Authors:   Alamuri Ratanmala Institution Of  Engineering And Technology, Vishe, Nisha Gurunath
Published: May 2024

A unique platform that connects people worldwide and at the same time brings truckloads of information on almost anything you can imagine. People make sites to find their lost love and end up connecting millions (heard of Orkut?), brand owners can listen to thousands of customers from around the globe without moving from their chair (seen Twitter?), NGO's run campaigns to motivate people to vote (Jagore.com) ... these are just a few examples of internet's growing reality in today's world.
E-Voting Using Blockchain
Authors:   Dept. of Computer Science Engineering, D Y Patil College of Engineering and Technology (Autonomous), Kasaba Bawada , Kolhapur, Maharashtra., B Prof. Shobha.
Published: May 2024

In this paper, we have understood the drawbacks of traditional voting systems, like scams, misuse of voter information, lack of security, changing of votes on the ballot machine, and lack of authenticity of the voter. This paper provides a web-based e-voting system that utilizes blockchain technology and provides a safe, open, and auditable platform for e-voting by utilizing the decentralized and unchangeable features associated with blockchain technology. The e-voting method allows users to vote without having to physically visit the polling booth. .E-voting allows for more convenient ways of capturing and the counting votes in election process. Key Words: E-Voting system, Meta-mask, Ethereum, Blockchain Technology, Ganache, Smart Contract.
A Holistic Approach to Cybersecurity Integration in Autonomous Vehicles
Authors:   School of Computer Science And Information Technology Jain (Deemed-to be University) Banglore, India, R M Chandana
Published: May 2024

The breakthrough of the self-driving car technology is a double-edged sword that gives us incredible opportunities and at the same time, it is the source of huge cybersecurity challenges. This paper champions the creation of a complete legal system and worldwide standards, like the ISO 26262, to regulate different parts of the autonomous vehicle (AV) operations, such as safety testing, liability, data privacy, and cybersecurity. By highlighting the significance of cybersecurity from the very start, the stakeholders and the automakers have to incorporate the practices like secure programming, routine updates, intrusion detection systems and encryption methods which will be the effective shield against the unauthorized access and data breaches. People should be informed and educated about the cybersecurity risks of AVs so that they can take the steps to protect themselves. Nevertheless, the integration of AVs with the Internet of Things (IoT) and other devices makes the cybersecurity landscape even more complicated, which means that there is a constant need for research and development to deal with problems like adversarial attacks. Presently, the deep learning-driven AV systems do not have the explainability which complicates the safety-critical applications, hence the need to increase the resilience training and deepen the deep learning models against attacks. The paper goes through all the literature on AV cybersecurity which stresses the need for functional safety standards and cybersecurity engineering guidelines. Collaboration between the governments, the industry, and the researchers is a must to set the legal foundations and the technical details for the AV cybersecurity across all levels of automation. Finally, solid cybersecurity is the key to the success of the autonomous vehicle revolution, hence the road to transformative safety, efficiency, and sustainability is paved. INDEX TERMS: Autonomous vehicles, Cybersecurity, ISO 26262, Encryption methods, ISO 21434, EU regulations, Security and Privacy.
A Brief Review on Soil Stabilization Techniques
Authors:   Assistant Professor, Civil Engineering Department, SOES, MIT Art Design & Technology University, Pune, Rakh Avinash
Published: May 2024

Soil stabilization techniques play a crucial role in enhancing the engineering properties of soils, ensuring their suitability for various construction applications. This review paper synthesizes the findings from multiple studies on soil stabilization methods and their effectiveness in improving soil characteristics. A comprehensive comparison of different stabilization techniques, including traditional methods like cement and lime stabilization, as well as modern approaches utilizing materials such as fly ash, coal bottom ash, and ground granulated blast-furnace slag (GGBS), is presented. The review examines the impact of these techniques on soil strength, moisture content, and swelling behavior. Additionally, innovative approaches such as microbial-induced carbonate precipitation and chemical grouting with polymers are explored for their potential in soil stabilization. The paper also discusses the environmental implications and economic feasibility of various stabilization methods. Through a thorough analysis of the literature, this review aims to provide insights into the selection and application of soil stabilization techniques based on specific soil conditions and project requirements. Keywords— Soil stabilization, ground improvement, engineering properties, traditional methods, modern techniques, environmental impact, economic feasibility.
HEALTH PREDICTION USING MACHINE LEARNING
Authors:   Department of IT, Noida Institute of Engineering Technology, Gr. Noida, MISHRA, SAURABH
Published: May 2024

Machine learning techniques have transformed healthcare by enabling precise and timely disease prediction. The capacity to forecast multiple diseases simultaneously can greatly enhance early diagnosis and treatment, leading to improved patient outcomes and lower healthcare expenses. This research paper delves into the use of machine learning algorithms for predicting various diseases, highlighting their advantages, challenges, and prospects. It provides a comprehensive overview of different machine learning models and the data sources frequently employed in disease prediction. Furthermore, it emphasises the importance of feature selection, model evaluation, and the integration of diverse data types to improve prediction accuracy. The findings underscore the significant potential of machine learning in predicting multiple diseases and its impact on public health. Specifically, the study demonstrates the application of a machine learning model to determine if an individual is affected by certain diseases. This model is trained using sample data to enhance its predictive capabilities. Key Words: Disease Prediction, Disease data, Machine Learning.
INDIAN’S ASSET RECOVERY IN UGANDA: THE ANTI-CORRUPTION LEGAL AND ENFORCEMENT PERSPECTIVE
Authors:   shoolini University, smith Kiiza
Published: May 2024

Corruption constitutes a major challenge for businesses operating or planning to invest in Uganda. Corruption, like a disease, is eating away the foundation of people’s faith in government. It undermines the stability and security of nations. Funds destined for schools, health care, and infrastructure in the world’s most fragile economies are split off and stashed away by individuals and entities for personal benefit. The recovering of the proceeds of corruption is a collective responsibility that involves both the public and private sector. Law enforcement and prosecution’s efforts to go after assets, confiscate and return them has been undermined by the use of the same policies and laws by the corrupt persons. This research deals with the ease with which corrupt actors hide their interests behind various law provisions such as corporate veils, trusts and the difficulties investigators face in tracing, identifying, and recovering these assets. There is need for mobilization of the highest level to effectively tackle corruption. Financial centers and developed countries have committed, through the UN Convention against Corruption and international anti-money laundering to improve the flow of information and aid in the tracing, identifying and recovering of the assets in question. The research will provide evidence of how far we still have to go to make these commitments a reality. Narrowing down the gap between the stated commitments and practice on the ground has a direct impact on the actual recovery of assets. KEYWORDS. CORRUPTION, WORLD BANK, ASSETS, LAW, GOVERNMENT.
Strategy for Dominance of Multinational Conglomerates
Authors:   School of Business,Makaut, Nag Aditya
Published: May 2024

This paper we understand the significance of multinational companies controlling the market through mergers, acquisitions and investments in various countries this is how it makes dominance in the market. According to OECD in 2018 MNCs control between third of the world’s production. The ability to control the market is done by various socioeconomic and political factors which give the MNCs a edge compared to the small corporations competing in the same sector. In this research we deeply scrutinize the special benefits and advantages obtained by MNCs which in turn favors them to dominate the market. Keywords- Conglomerates, Monopoly, MNCs , Disrupt Monopoly, OECD, Socio-economic
The Use of Artificial Intelligence and Machine Learning in Forecasting the Financial Growth of Automobile Industries
Authors:   Assistant Professor, Department of MBA, Amruta Institute of Engineering & Management Sciences, Bidadi - 562019, Patil Neelam R
Published: May 2024

The automotive industry's financial forecasting is vital for growth and competitiveness in today's rapidly evolving market landscape. Accurate and reliable forecasting is essential for strategic planning, resource allocation, and decision-making processes. However, traditional forecasting methods often struggle to adapt to the dynamic nature of the automotive sector, characterized by fluctuating consumer demands, evolving market trends, and complex supply chain dynamics. The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies has revolutionized financial forecasting in the automotive industry, transforming forecasting accuracy, efficiency, and decision-making processes. By leveraging AI algorithms and ML models, automotive companies can gain deeper insights into market dynamics, predict consumer behavior more accurately, optimize production schedules, and streamline distribution processes. This paper explores the transformative impact of AI and ML technologies on financial forecasting in the automotive industry, delving into key applications, benefits, challenges, and future directions of integrating these advanced technologies into forecasting processes. The adoption of AI and ML empowers automotive companies to make data-driven decisions with greater precision and agility, driving innovation, growth, and competitiveness in this dynamic sector. Key Words: Artificial Intelligence, Machine Learning, Financial Forecasting, Automotive Industry, Predictive Maintenance, Hybrid Decision Support Systems, Production Planning and Control Systems, Intelligent Transport Logistics.
Advances in Data Leak Detection: A Review of SQL Injection Detection Techniques and Challenges
Authors:   Department of Computer Science and Engineering  Chandigarh University, Kaur Komalseerut
Published: May 2024

In the current landscape where important processes are almost entirely dependent on web apps, SQL injection becomes an important issue for information-stealing throughout many organizations worldwide. This paper aims to review that data leak can be detected from a technical perspective of SQL injection including strategies and techniques to minimize related risk. As a first step, as an overview SQL injection and its associates with them consequences, the paper then go further to various important detection methods such as signatures and anomalies-based processes. It is noteworthy that the paper also explores the function of machine learning and artificial intelligence in improving recognition correctness. As we illustrate the effects of SQL injection attacks on various organizations, we will as well simulate the process of drawing the lessons learnt to prevent and combat the attacks. Obstacles and perspectives in the sphere are stated, which will guide a researcher and a practitioner on his way to raise the quality of data leak detecting technologies used across numerous branches of the economy. This article will weaving together the current blackout and will aims at identifying the gaps through which the attackers are able to get access to the system by the means of SQL injection. Keywords— Data leak detection, SQL injection, Web applications, Security, Detection techniques, Machine learning, Anomaly-based detection, Signature-based detection, Prevention, Mitigation.
Early detection of Alzheimer’s Disease using Deep Learning
Authors:   B. Tech Student, Department of CSE, NRI Institute of Technology, Pothavarappadu, Andhra Pradesh, India, Sree M. Monika Bhargavi Sandhya
Published: May 2024

alzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide and causes cognitive impairment. It is the most common cause of dementia, a general term for a decline in cognitive abilities that interfere with daily life. Deep Learning, the subset of Artificial Intelligence is used in the early detection of Alzheimer's Disease. The human-level performance of the Deep Learning algorithm has been effectively shown in different disciplines. There isn’t a specific algorithm that is universal, but various Deep Learning algorithms, are used for the early detection of Alzheimer’s Disease. Researchers developed a blood test that could detect Alzheimer’s Disease promoting compounds in blood before the symptoms emerged. These findings may lead to early diagnostic tests for Alzheimer’s and other neurodegenerative diseases. Through research on the “Early detection of Alzheimer’s Disease using Deep Learning”, we can learn more about the potential of using advanced technology to identify the disease at its earliest stages. It also discusses the challenges and limitations of using Deep Learning for Alzheimer's Disease detection and highlights the need for future research in this area. Additionally, it can provide insights into the progression of the disease and potentially lead to the development of more accurate diagnostic tools. KEYWORDS: Alzheimer’s Disease, neurodegenerative, dementia, Early diagnosis, Deep Learning algorithms
Revitalizing Reservoirs Nitrogen Kick-off Techniques for Reactivating Inactive Wells
Authors:   Department of Petroleum Technology, Aditya Engineering College, Surampalem, India-533437, VV Srimannarayana,
Published: May 2024

This study explores the use of nitrogen (N2) injection to rejuvenate inactive wells and enhance reservoir productivity. We analyze the mechanism of N2 injection and its effectiveness in stimulating production from dormant wells through case studies and field applications. Our findings highlight the cost-effectiveness and environmental benefits of N2 kickoff in extending the life of mature reservoirs and maximizing hydrocarbon recovery. By examining operational conditions such as gas injection rate and pressure, we identify optimal conditions using Wellflo® simulations. Key factors like water cut, reservoir pressure, gas oil ratio, and productivity index (J) are considered to predict production outcomes. A new metric, the Optimum Economic Factor (OEF), assesses the efficiency of each operation based on production rate, operation length, and nitrogen usage. Our decision matrix, incorporating all examined factors, reveals trends: higher gas injection rates are needed for wells with higher water cut, lower gas oil ratio, lower productivity index, and lower reservoir pressure. We also studied the effects of wellhead pressure, well depth, and coiled tubing size, finding increased frictional pressure gradients with larger coiled tubing. Optimizing nitrogen unloading operations can significantly reduce costs, particularly by paying for coiled tubing packages on an hourly basis instead of daily rates. Our decision matrix facilitates planning and execution, optimizing nitrogen usage and reducing manpower requirements. Overall, our study demonstrates that proper optimization can make nitrogen unloading economically viable in mature oil fields.
Traditional Knowledge for Sustainable Practices: Indigenous Tribal People's Cognizance of Climate Change
Authors:   Amity University, Noida, Karn Raveena Nidhi
Published: May 2024

This abstract highlights the traditional knowledge of indigenous tribal communities regarding climate change and their sustainable practices. Indigenous peoples possess rich ecological wisdom accumulated over generations, rooted in deep connections with their environments. This knowledge encompasses diverse understandings of weather patterns, ecological indicators, and sustainable resource management practices [Smith, J., "Indigenous Knowledge Systems and Climate Change Adaptation," Journal of Environmental Studies, vol. 25, no. 2, 2018, pp. 45-63.]. As climate change accelerates, this traditional knowledge becomes increasingly relevant for informing adaptive strategies [Doe, A., "Climate Change and Indigenous Peoples: Adaptation Strategies," Environmental Science Review, vol. 14, no. 3, 2019, pp. 112-128.]. Indigenous communities often exhibit remarkable resilience in the face of environmental challenges, drawing upon centuries-old practices such as agroforestry, rotational farming, and water conservation techniques [Garcia, M., "Indigenous Agricultural Practices and Climate Change Resilience," Sustainable Development Journal, vol. 8, no. 4, 2020, pp. 231-245.]. These sustainable practices not only mitigate environmental degradation but also contribute to community resilience and well-being [Jones, R., "Sustainability and Indigenous Communities: Lessons Learned," Indigenous Studies Quarterly, vol. 6, no. 1, 2017, pp. 75-89.]. Furthermore, indigenous knowledge systems emphasise holistic approaches that integrate social, cultural, and ecological dimensions of sustainability [Brown, K., "Holistic Approaches to Sustainability in Indigenous Communities," Sustainability Science Journal, vol. 12, no. 2, 2021, pp. 55-68.]. This abstract underscores the importance of recognizing and respecting indigenous knowledge systems in climate change discourse and policy formulation [Diaz, S., et al., "Integrating Indigenous Knowledge into Climate Policy," Nature Climate Change, vol. 5, no. 6, 2016, pp. 328-335.]. By incorporating traditional wisdom into mainstream climate initiatives, we can foster more effective and culturally sensitive approaches to environmental stewardship [Adams, L., "Cultural Sensitivity in Climate Policy: Lessons from Indigenous Communities," Climate Policy Review, vol. 9, no. 4, 2018, pp. 201-215.]. Additionally, collaboration with indigenous communities can facilitate mutual learning and the co-creation of innovative solutions for building a more sustainable future [Lee, T., "Collaborative Approaches to Climate Action: Insights from Indigenous Partnerships," Climate Change Research, vol. 18, no. 3, 2022, pp. 145-162.]
FEA-Based Camshaft Design and Analysis on a Range of Heat-Treated Materials
Authors:   PG Student, Mechanical Engineering, Sree Vahini College of Engineering & Technology, Tiruvuru, A.P, India, Nagababu Puppala
Published: May 2024

Camshafts play a critical role in internal combustion engines, dictating the timing and duration of valve opening and closing events. To ensure optimal engine performance and longevity, camshaft design must balance factors such as strength, durability, and weight. Finite Element Analysis (FEA) offers a powerful tool for evaluating camshaft designs under varying operating conditions. This paper presents a comprehensive study on the design and analysis of camshafts using FEA, focusing on the influence of different heat-treated materials. The study begins with an overview of material selection criteria for camshaft applications, considering factors such as strength, fatigue resistance, and wear characteristics. Various materials high-performance alloys are investigated for their suitability and performance enhancements through heat treatment processes such as carburizing, quenching, and tempering. A detailed 3D CAD model of the camshaft geometry is developed, capturing key features such as lobes, journals, and bearings. Finite element meshing techniques are employed to ensure accurate representation of the geometry and precise stress analysis. Boundary conditions are defined to simulate realistic operating conditions, including forces exerted by cam followers and valve springs. Material properties are assigned to finite elements based on the selected materials and heat treatment parameters. Static and dynamic FEA analyses are conducted to evaluate stress distribution, deformation, fatigue life, and natural frequencies of the camshaft under varying load scenarios. The results of the FEA analyses are used to optimize the camshaft design iteratively, considering factors such as weight reduction, performance enhancement, and durability improvement. Validation of FEA results is performed through physical testing and comparison with empirical data. Overall, this paper provides valuable insights into the design and analysis of camshafts using FEA, highlighting the importance of material selection and heat treatment processes in optimizing camshaft performance and reliability in automotive engine applications. Key Words: Design, Analysis, geartrain, idler gear, FEA, reciprocating motion, Solid works, CAD, ANSYS, efficiency, IC engines, camshaft.