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Flexural Behavior of RCC Beam Using Confined Reinforcement with Helical Stirrups
In the present condition the cost of construction goes on increasing due to usage of steel. In order to improve the tensile strength in concrete and reducing the usage of steel, a new type of reinforcement is essential for making an economical building. The main objective of our project is to reduce the usage of steel by providing helical stirrups in the reinforcement recommended as per IS 456:2000. By this method of reinforcement, we can achieve flexure in reinforced concrete beams which opposes conventional method of reducing the neutral axis depth of the beam. The under reinforced section of the beam can be achieved by this method of helical stirrups in the reinforcement. Steel is introduced in the tension zone to take the tensile stress, but as strength of concrete is ignored in tension zone with respect to compression zone. The objective of this research is to investigate the behavior of singly, doubly and confined reinforced beams. Ultimate load, deflection load, compressive stresses and tensile stresses were compared with the controlled plain reinforced helical stirrups beams. Single and double reinforced concrete beams were cast and tested for this purpose of investigation. The results obtained have indicated that enhanced the strength of concrete.
Solar Powered Tricycle for Specially Abled Person
Mobility of physically disabled persons is a concerning social issue nowadays. The basic Tricycle is a three-wheeled design, pedaled by disabled persons in the side and seat in the middle for sitting arrangement. They use only one hand to steer the handle because other-hand is used to rotate the pedal. Our aim is to design a low-cost tricycle that will allow person to accelerate and steer the tricycle from a single place. The way the Bike Chair works is the back is essentially a regular bicycle, but the front turns it into a tricycle, and sort-of makes it a wheelchair and bicycle combo. The disabled passenger sits on the front two wheels, as the driver uses their front wheels to steer the bike. The passenger has buckles and strap to secure the min place and top even them from falling out of the chair during the bike-ride. Furthermore, the tricycle's compact size, maneuverability, and zero emissions make it an attractive option for reducing traffic congestion and pollution in densely populated urban areas. Future research directions include optimization of battery and motor technologies for enhanced performance and efficiency, as well as integration of smart features for improved user experience and safety.
Advancing Privacy and Confidentiality in the Digital Healthcare Era: The Image Encryption Solution
The rapid digitization of health care services has spurred exponential growth in medical image generation and exchange. Safe guarding the confidentiality and integrity of these sensitive E-Health care images is crucial for protecting patient privacy. This project proposes a robust solution within the AWS Cloud, implementing an efficient image encryption model. Advanced cryptographic techniques ensure that only authorized personnel can access and decrypt images, minimizing the risk of unauthorized data breaches. The project aims to advance secure E-Healthcare image sharing, facilitating improved collaboration among providers while prioritizing patient privacy and data integrity.
A Method of Skin Disease Detection Using Image Processing and Machine Learning
Skin conditions are prevalent health issues globally. The dangers posed by these infections are unseen, leading to physical discomfort and mental anguish. In severe cases, skin diseases can even progress to skin cancer. Consequently, the identification of skin diseases from clinical images remains a significant challenge in medical image analysis. Furthermore, manual diagnosis by medical professionals is time-consuming and subjective. Therefore, there is a need for automated skin disease prediction to expedite treatment planning for both patients and dermatologists. These studies presents a digital hair removal method and de-blurs or denoise the images. To extract essential patterns from the skin imagesand statistical features techniques are utilized. Two efficient machine learning algorithms, namely Support Vector Machine (SVM), and Convolutional Neural Network (CNN) classifiers, are employed with the extracted features to accurately classify skin images as melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), vascular lesion (VASC), and Squamous cell carcinoma (SCC). The models are validated using the ISIC 2019 challenge and HAM10000 datasets, with SVM demonstrating slightly superior performance compared to the other classifiers. Furthermore, a comparison with state-of-the-art methods is conducted to evaluate the effectiveness of the proposed approach.
Computational Tracking and Estimating the Covid-19 Dynamic Broadcast Based on Machine Learning Model
The COVID-19 pandemic has reshaped global health and societal norms, necessitating effective tracking and forecasting methods. We propose a novel machine learning model that integrates diverse datasets, including epidemiological, demographic, and environmental variables. By employing advanced techniques such as deep learning and time-series analysis, our model accurately predicts the pandemic's trajectory, considering factors like non-pharmaceutical interventions and variant emergence. Through comprehensive data integration and feature engineering, our approach provides actionable insights for policymakers, aiding in proactive response strategies and adaptive tracking of COVID-19 propagation dynamics.
Survey on MapReduce Scheduler Algorithms in Hadoop Framework
Hadoop serves as a robust framework tailored for the storage and processing of vast data volumes across clusters. Its foundation lies in the Hadoop Distributed File System (HDFS) for data storage, complemented by the MapReduce paradigm for data processing. MapReduce, functioning as a parallel computing framework, operates adeptly on distributed clusters to manage large-scale data processing tasks. Within this framework, scheduling emerges as a pivotal aspect, influencing the overall system's performance and efficiency. The essence of scheduling in MapReduce lies in enhancing performance, minimizing response times, and optimizing resource allocation. This paper undertakes a systematic exploration of existing scheduling algorithms, offering a fresh classification and detailed examination of each category. Furthermore, the analysis delves into the core principles, objectives, as well as the strengths and weaknesses of these scheduling algorithms.
Detecting Diabetic Foot Ulcer with CNN and CapsNet: A Comparative Investigation of Model Performance
Diabetic foot ulcer (DFU) poses a substantial health risk to patients and healthcare resources, necessitating accurate and efficient diagnosis. This study harnesses the power of convolutional neural networks (CNNs) and capsule networks (CapsNets) to redefine DFU classification, aiming for enhanced accuracy and efficiency. By integrating state-of-the-art deep learning architectures, we address the limitations of existing diagnostic methodologies. Through comprehensive data pre-processing, feature extraction, and classification, our system seeks to improve diagnostic precision and contribute to better patient outcomes. A comparative analysis between CNNs and CapsNets evaluates key performance metrics, guiding future advancements in medical image diagnosis. Furthermore, computational timing assessments ensure practicality in medical settings. This research underscores the potential of deep learning in revolutionizing medical image diagnosis and emphasizes the critical role of accurate DFU detection in diabetic care.
The Synergy of Artificial Intelligence and the Internet of Things: Transforming Connectivity and Intelligence
Artificial Intelligence (AI) and the Internet of Things (IoT) represent two powerful technological forces that, when combined, have the potential to revolutionize numerous aspects of our daily lives. This research article explores the transformative impact of AI on the IoT domain, examining how AI techniques enhance connectivity, intelligence, and decision-making capabilities within IoT systems. Through a comprehensive analysis of current research, applications, and future trends, this article illuminates the synergistic relationship between AI and IoT, offering insights into its implications for various industries and society as a whole.
Content Based Image Retrieval Using Support Vector Machine
A significant issue in multimedia systems is image retrieval. It is identified as the procedure for looking through and obtaining pictures from a dataset. One important and difficult area of study in digital image processing is content-based image retrieval (CBIR). Retrieving pertinent data from a vast picture database after a query image with better system output is a fundamental requirement of the CBIR system. Regretfully, not all techniques can be applied to achieve high retrieval accuracy. Thus, the purpose of this research is to use Support Vector Machine (SVM) to classify query picture data with image database data in order to get similar images. Based on the classification, the accuracy of the image retrieval will be verified.
Two Level Security for Key Access Using Cloud Computing
In this work, the author constructs a key access management scheme that seamlessly transitions any hierarchical-like access policy to the digital medium. The proposed scheme allows any public cloud system to be used as a private cloud. We consider the data owner an entity consisting of several organization units. We provide a secure method for each user of this entity to access the public cloud from both inside and outside the company’s network. The idea of the key access control scheme, which is based on Shamir’s secret sharing algorithm and polynomial interpolation method, is suitable especially for hierarchical organizational structures. It offers a secure, flexible, and hierarchical key access mechanism for organizations utilizing mission-critical data.
Enhancing Agricultural Efficiency through Robotic Systems and AI Integration
Robotic systems have progressed and grown more available in recent years. They have been gradually but steadily integrated and used in several procedures and industries, particularly farming, as a consequence. Nowadays, agriculture machines are being developed to take the place of humans in labor-intensive, thing or hazardous activities. Depending on the kind of vehicle, its detectors, motors, and telecommunication networks, agro mobile robots provide a number of advantages. The most significant profession on the globe is gardening. The fundamental main objective of the task is to construct an automated multifunctional agricultural robot car. It is used to boost farming efficiency while decreasing the number of individuals who labor in fields and improving the accuracy of the task.
Prediction of Air Quality Data with Feature Selection and Machine Learning Algorithms
Predicting air quality is essential for managing public health and the environment. This study looks into how well machine learning (ML) algorithms forecast air quality data and incorporates feature selection strategies for better model performance. Air pollution is one of the biggest issues facing humanity today. Every year, air pollution causes millions of deaths due to direct or indirect effects. These days, there is a critical need for effective measures to mitigate the negative impacts of air pollution. Usually, the answers to the issues with air pollution are hasty decisions that don't end up helping. It is vital to concentrate efforts on the pollutants that cause the majority of air pollution in order to create an efficient counter-strategy. In many industrial and urban regions today, monitoring and safeguarding air quality has emerged as one of the government's top priorities. The combustion of fossil fuels, traffic and weather conditions, and industrial parameters
Deep Learning Based Deforestation Prediction and Classification
Human-induced deforestation has a major impact on forest ecosystems and therefore its detection and analysis methods should be improved. Where, this type of detection or classification helps us to degrade the deforestations in future. In this project we are mainly focusing on the problem of deforestation, performing the classification of deforestation and the healthy forests with the help of deep learning model. CNN is the algorithm that which are been using here for the classification process. We are preparing a dataset which is trained using the algorithm and classification will be performed in testing.
Development of an Android Application for Smart Parking System
Now-a-days, vehicle parking has become a major problem in urban areas with the shortage of parking spaces. It is very difficult and frustrating to find a parking space in most metropolitan areas, especially during the rush hours to solve this problem. The project entitled smart parking system using android application, the major motivation of this project is to reduce the traffic congestion in roads, multi storied buildings and malls due to unavailability of parking spaces. The proposed application provides an easy way for reservation of parking slot. In this application user can view various parking areas and also view whether space is available or not. The project displays the nearest empty slot if present with respect to user location. This system gives a further feature of cancelling the bookings. Users may even make price online primarily based totally at the time taken for the reserved area the quantity might be calculated and the person can make charge. This project provides a good insight into the guidance, monitoring and reservations components of the smart parking and directions to the future development.
IOT- Based Human Friendly Smart Trolley
Shopping and buying is an integral part of our daily lives. Big mega markets have a wide variety of items and different stores can have different deliveries of goods. It is difficult for many consumers to stand in the long queue for the billing of goods purchased. This causes wastage of money and a poor bill for the wrong customer. Trolleys are used in supermarkets or grocery stores to make shopping simpler. However, it is difficult for customers to control the trolley while shopping. An automated customer following trolley was implemented which calculates the total sum of grocery items carried in the trolley by the customers. This reduces the customer's effort to pull the trolley and keep the line for the payment of the grocery products. The customer who has a specific tag and a web camera installed in front of the trolley will recognize the tag and move the trolley to the customer. Using the RFID tag and the Raspberry Pi receiver, the item bill inserted has been obtained in the trolley. This results in an ideal solution to all these problems.
Machine Learning Based Spam Comments Detection on Youtube: A Comparison of Regression Models
The rise of spam comments on platforms like YouTube has become a significant concern, as they not only hinder genuine user engagement but also pose serious risks to users' safety and privacy. Machine Learning (ML) offers a powerful solution to combat spam comments by automating the process of detecting and preventing them. With the ability to analyze vast amounts of data and patterns, ML algorithms can effectively distinguish between legitimate comments and those that are spam. One of the commonly employed approaches in ML for spam comment detection is the Naive Bayes classification algorithm. Naive Bayes is a probabilistic algorithm that calculates the likelihood of a comment being spam based on its characteristics and the occurrence of specific keywords or phrases that are typical of spam content. By training the algorithm on a labeled dataset of spam and non-spam comments, it can learn to recognize patterns and generalize its understanding to new, unseen comments. Achieving a detection accuracy of 92.78% is indeed promising, but researchers and developers continue to explore other ML techniques and combinations to further improve the accuracy and robustness of spam comment detection systems. Ensemble methods, deep learning, and Natural Language Processing (NLP) techniques are among the advanced approaches gaining attention in this domain. One crucial aspect of an effective spam detection system is its adaptability and responsiveness to emerging spam tactics.
Intelligent Environment using Extended Reality and Machine Learning to Improve Healthcare
Intelligent Environments (IEs) represent a paradigm shift in the way we interact with our surroundings, leveraging Ambient Intelligence (AmI) to create responsive and adaptive spaces that seamlessly integrate technology into the built environment. By harnessing sensors, actuators, and artificial intelligence, IEs have the potential to enhance human experience, improve efficiency, and promote sustainability across various domains, including smart homes, workplaces, cities, and healthcare facilities. This paper provides an overview of IE concepts, discusses key technologies and applications, explores design considerations, and outlines future directions for research and development in this dynamic field. This paper focuses on the study of the potential use of Extended Reality technologies in order to promote Green Health as well as their use in Smart Hospitals, one of the variants of Smart Environments, incorporating Machine Learning and Computer Vision, as a tool to support and improve healthcare, both from the point of view of the health professional and the patient, through a literature review and analysis of the current situation. Resulting in the elaboration of a conceptual model with the suggestion of technologies that can be used to achieve this scenario selected for their potential, and then the development of prototypes of parts of the conceptual model for Extended Reality Headsets as concept validation.
Automated Lead Qualification Using RPA and Sales Force
Lead qualification is a critical process in sales and marketing, determining which leads have the highest potential for conversion into customers. This paper explores the integration of Robotic Process Automation (RPA) with Salesforce Customer Relationship Management (CRM) to automate lead qualification processes. By leveraging the capabilities of RPA bots to extract, process, and analyze data from Salesforce, businesses can achieve faster and more accurate lead qualification, leading to increased sales efficiency and revenue generation. This paper discusses the benefits, challenges, and implementation considerations of this integration, supported by real-world examples from companies that have successfully implemented automated lead qualification using RPA and Salesforce.
Decoding Posture: Distinguishing Between Good and Bad Body Alignment
On the grounds of artificial intelligence sensors, the research focuses on achieving precise detection of intelligent posture to minimize the risk of injuries resulting from incorrect body positioning and to mitigate the negative impact on both performance and physical well-being. Utilizing an attitude analysis and recognition system that relies on the nertial measurement unit enables not only the measurement of human body motion information but also the acquisition of data pertaining to motion characteristics and body movement states through the examination of posture data. A proposition is presented for intelligent posture training that is centered on accuracy and real-time feedback. Furthermore, it is elucidated that the accuracy of posture recognition through machine learning is significantly influenced by variations in user BMI. This paper will introduce the most recent advancements in posture recognition techniques, providing an overview of the various methodologies and algorithms that have emerged in recent years. Additionally, it delves into enhanced approaches such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The study thoroughly dissects and consolidates the general procedures and datasets involved in posture recognition, comparing several enhanced CNN methodologies alongside three principal recognition techniques. Moreover, it delves into the utilization of advanced neural networks in posture recognition, including but not limited to transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks. Researchers have observed that CNN has made significant strides in the realm of posture recognition and has garnered favor, yet emphasize the necessity for further exploration into areas such as feature extraction and information fusion.
Adaptive Workload Scheduling Design in Parallel Computational Framework
In the recent years, many researchers have showed a great deal of interest to improve the scheduling of workload in the cloud platforms. On the other hand, to carry out the execution of the scientific workloads in the cloud environment, it consumes much time and is expensive; hence, it is neither time efficient nor cost-efficient. Due to this reason, many research studies have been carried, by which the researchers tend to reduce the processing time and make a cost-efficient method as the users are charged based on the usage. Very few studies have been done to optimize the cost with processing time and energy parameters together in order to meet the Service Level Agreement (SLA) and Quality of Service (QoS) of the workload task. Hence, in this paper, we present an Adaptive Workload Scheduling (AWS) model that ensures the Task Level SLA prerequisites in a heterogeneous distributed-computing environment. This AWS- model approach reduces the amount of energy and time needed to execute a given workloads. Cybershake scientific workload has been utilized for the studying proposed AWS model. When model was compared with the standard workload scheduling approach, our model reduced the consumption of cost, energy and time.