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Real-time Traffic Prediction in 5G Networks Using LSTM Networks
This research explores the application of Long Short-Term Memory (LSTM) networks for real-time traffic prediction within 5G networks, aiming to address the critical need for accurate prediction models in dynamic network environments. Leveraging the sequential learning capabilities of LSTM networks, the proposed methodology encompasses dataset preparation, model architecture design, training, and evaluation. Experimental results demonstrate the effectiveness of the LSTM-based prediction model in capturing temporal dependencies and providing reliable predictions across various prediction horizons. While promising, further research is warranted to enhance the model's performance and address remaining challenges. This study contributes to advancing the state-of-the-art in traffic prediction methodologies, facilitating more efficient network management and optimization in 5G environments. 2024 IEEE. -
Real-Time Traffic Sign Detection Under Foggy Condition
Traffic congestion becomes high in urban areas and using public and private transportation services. The image of traffic signs gets affected by fog, and the detection of traffic signs has become difficult. To solve this issue, the machine learning technique has been used. Convolution neural network helps to solve real-time problems; hence, it can be used in the study for detecting traffic signs under foggy condition. The study results revealed that the model network has accuracy of 99.8%, and the proposed algorithm detects a traffic sign under foggy conditions in 2s per frame. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Real-World Application of Machine Learning and Deep Learning
The world today is running on the latest computer technologies and one of those is machine learning. The real life example that most of us know is speech recognition. Google Assistant is the common example for this Speech recognition. This google assistant is not only limited till 'Ok Google', but it responds to all your questions in a smart way. It can manage all your calls or can book appointments. Imagine you fell down while de-boarding a bus. So, Next time you take care so that you don't fall that is something that your brain has interpreted from your past experience. This is what exactly deep learning is, it imitates human brain works. Deep learning is sub-branch of machine learning. It is able to build all new things based on its previous experiences. Many of us have heard about driverless cars and medical diagnosis. Recently google has developed a new technology where all your cardiovascular events can be predicted by eye scan so, that doctors can get a clear view of what is inside the body of a patient. These all are developed using machine learning. It has a capability to change the human world into a complete robotic world. Anyways, it also has its own disadvantages. This article discusses about those, Scope of machine learning, its Market potential, financial growth and Current applications of machine learning. 2019 IEEE. -
Rebuilding the Capabilities for Post COVID-19 Pandemic: Issues and Challenges of Bangalore Model of Development
The pace of urbanization has achieved considerable momentum in recent years with 34.93 per cent of India's population living in urban areas. However, the COVID - 19 pandemic has severely affected urban development with adverse effects on people's mobility, consumption level, health and poverty. Bangalore, the capital of Karnataka and the third largest city in India, has a population of 11 million and contributes more than one third of the state's GDP. The expansion of certain sectors including Information Technology, infrastructure and spread of educational institutions has fueled Bangalore's rapid growth in the past three decades which has made it a regional superpower in India, if not South Asia. This paper explores the unique features of the 'Bangalore Model of Development' as a regional development model and provides a systematic introspection of its capabilities. It discusses the impact of the pandemic on the key driving forces of Bangalore Model and assesses the current government measures. The situation analysis with the policy prescriptions would help to strengthen and sustain the urban system during the postpandemic times. 2022 IEEE. -
Recent Advances in Pedestrian Identification Using LiDAR and Deep Learning Methods in Autonomous Vehicles
The myriad benefits of autonomous vehicles (AVs) encompassing passenger convenience, heightened safety, fuel consumption reduction, traffic decongestion, accident mitigation, cost-efficiency and heightened dependability have underpinned their burgeoning popularity. Prior to their full-scale integration into primary road networks substantial functional impediments in AVs necessitate resolution. An indispensable feature for AVs is pedestrian detection crucial for collision avoidance. Advent of automated driving is swiftly materializing owing to consistent deployment of deep learning (DL) methodologies for obstacle identification coupled with expeditious evolution of sensor and communication technologies exemplified by LiDAR systems. This study undertakes exploration of DL-based pedestrian detection algorithms with particular focus on YOLO and R CNN for purpose of processing intricate imagery akin to LiDAR sensor outputs. Recent epochs have witnessed DL approaches emerge as potentially potent avenue for augmenting real-time obstacle recognition and avoidance capabilities of autonomous vehicles. Within this scholarly exposition we undertake exhaustive examination of latest breakthroughs in pedestrian detection leveraging synergy of LiDAR and DL systems. This discourse comprehensively catalogues most pressing unresolved issues within realm of LiDAR-DL solutions furnishing compass for prospective researchers embarking on journey to forge forthcoming generation of economically viable autonomous vehicles. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Recognition of Green Colour Vegetables' Images Using an Artificial Neural Network
Image processing is used in all the domains including agriculture. In this paper, we have introduced a computationally simple and small feature vector, as a tool for the recognition of green colour vegetable images. The RGB colour system is used and the feature set is computationally economic and performs well on locally available vegetable images. For recognition of vegetable images, an ANN-based classifier is deployed. The recognition percentage is in the scale of 74-100 for 15 vegetable types. This work finds application in the packing of vegetables, food processing, automatic vending. 2019 IEEE. -
Recognition of Signature Using Neural Network and Euclidean Distance for Bank Cheque Automation
Handwritten signature recognition plays significant role in automatic document verification system in particularly bank cheque authorization. The proposed method focuses on A novel technique for offline signature recognition approach for bank cheque based on zonal features and regional features. These combined features are used to find genuinety of signature using Euclidean distance as a metric. Extensive experiments are carried out to exhibit the success of the recommended approach. 2019, Springer Nature Singapore Pte Ltd. -
Recommendation System using Clustering and Comparing Clustering and Topic Modelling Techniques
In this paper, we have used a technique called clustering to recommend the products to the customer and also tried to compare clustering and Topic modelling to find out which technique is better for our purpose. From all the papers that have been reviewed, we observed that the greater part of the proposal approaches applied content-based filtering (55%). Collaborative-based filtering was applied by just 18% of the looked into approaches, and hybrid based by 16%. Other suggestion ideas included generalizing, thing driven proposals, and crossover suggestions. The content-based filtering approaches overwhelmingly utilized papers that the clients had made, marked, examined, or downloaded [1]. To begin with, it stays muddled which suggestion ideas and approaches are the most encouraging. For instance, analysts demonstrated different results on the presentation of content based and collaborative filtering. A portion of the time content-based filtering performed better contrasted with collaborative filtering sand a portion of the time it performed all the more regrettable. 2022 IEEE. -
Reconceptualizing Empowerment And Autonomy: Ethnographic Narratives From A Self Help Group In South India
The paper revisits academics' conceptualizations of women empowerment as stopping short of autonomy. It departs from the general observation that women empowerment movements by and large have failed to translate the new agency of women outside the domains of socio economy; that women empowerment movements' capacity to re-engage with patriarchal structures and ideologies is seriously contained. Through an ethnography of Kudumbashree, an SHG in the South Indian state of Keralam, we question the neat distinctions between empowerment and autonomy that prevail in the academic common sense. The transition of agency from the economic to the political domain is a subtle enterprise and is mediated by a number of factors including the economic independence, decision making capability and political participation. Socio -economic - political implications of women empowerment could be the first step in challenging and overcoming the relations of oppression in any society. The stereotypical assumptions can be negotiated by solely apportioning responsibilities and re-engaging with the system through everyday practices. The nuances of empowered women's re-engagement with local gender/power regimes lead to changes at the conceptual level that cuts beyond the individual and group level material transformations. The Electrochemical Society -
Rectangular microstrip antenna for WLAN application
This paper deals with the design of rectangular microstrip patch antenna for Wireless applications. In this paper a modified slotted microstrip antenna design for 2.5GHz operation is proposed. This provides improved performance in terms of lower return loss and higher gain. This is possible by inclusion of slots appropriately on the patch shape. The substrate material used in this design is Duroid5880 with permittivity 2.2 and size 47.43mm 39.65mm 1.6mm. ANSOFT HFSS EM simulator has been used for design and simulation of the microstrip antenna. The various antenna parameters such as frequency, VSWR, gain and directivity are analyzed to characterize the proposed antenna. 2015 IEEE. -
Rectangular Microstrip Patch Antenna Array Based Sectored Antenna for Directional Wireless Sensor Networks
Directional wireless Sensor Network (DSN) outperforms Wireless Sensor Network (WSN) over different parameters such as transmission range, interference, spatial reusability and energy efficiency. In this paper, a Rectangular Patch antenna Array (RPA) based sectored antenna is proposed for DSN. The individual sector is composed of two-element rectangular patch antenna array with a measured peak gain of 5.2 dBi and half-power beamwidth of 45. Single Pole 8 Throw (SP8T) Radio Frequency (RF) switchboard is designed to connect the sectored antenna to MICAz WSN mote. The antenna performance analysis carried out in simulation and real-time measurement via Ansys High Frequency Structure Simulator (HFSS) and Vector Network Analyzer (VNA) exhibits higher gain, lower return loss, half-power beamwidth and Voltage Standing Wave Ratio (VSWR). 2020 IEEE. -
Recurrent Neural Networks in Predicting the Popularity of Online Social Networks Content: A Review
An online social network is a web platform that individuals use to make social relationships with people who share similar interests, activities, connections, and backgrounds. All online social networks differ in the number of features they provide and their format. In recent years, drastic growth has been seen in the users of online social networks like Flickr, Instagram, Pinterest, Twitter, etc. Among all the features of online social networks, content sharing is the one being widely used by individual users and large organizations. Due to this, content popularity prediction has been extensively studied nowadays, considering various aspects related to it. The study throws light on the use of machine learning techniques in this field. Various algorithms have been used to handle popularity prediction, including classification, regression, and clustering techniques. It is feasible to extract the essential information from such content using machine learning algorithms and utilize the retrieved information in a variety of ways, the majority of which are commercial in nature. The goal of this study is to review and analyze various recurrent neural network (RNN) approaches for predicting the popularity of social media content. The Electrochemical Society -
Reflector Backed Conical Dielectric Resonator Antenna with Enhanced Gain
This paper reports a wideband, high gain, slot coupled reflector backed conical dielectric resonator antenna (DRA). The key findings of the work are as follows; i) the antenna operates over 7.73-8.3 GHz, with peak gain of 10.32 dBi, ii) an gain enhancement > 5dBi achieved by placing a reflector below the ground plane, iii) the measured results best matches with their measured counter parts, iv) the antenna deals with many advantages, including performance, volume, and fabrication feasibility. From application point of view the developed model can be successfully used for X-band wireless communication. 2018 IEEE. -
Regression Analysis as a Metric for Sustainability Development: Validation of Indian Territory
The 2030 Development Agenda styled' Transforming our world The 2030 Agenda for Sustainable Development' was hugged by the transnational locales of the UN General Assembly in 2015. Monitoring the progress of countries towards achieving these pretensions is pivotal for sustainable development. This exploration paper offers an innovative stance toward foretelling the SDG Index of Indian states for the near future times using machine learning ways, logical and visualization tools. The paper focuses on India's sweats towards achieving the SDGs and investigates the factors impacting the SDG performance of individual Indians states. A comprehensive dataset is collected, encompassing a wide range of socio-profitable pointers, demographic data, and environmental criteria applicable to each SDG target. Literal SDG Index scores and corresponding state-specific data are collected to assay and find some trends. The study demonstrates the eventuality of vaticination ways in vaticinating the unborn SDG Index scores of Indian states. The time series graph showcases varying degrees of delicacy across different SDGs, indicating the complexity and diversity of experimental challenges. 2024 IEEE. -
Regression Analysis on Macroeconomic Factors and Dividend Yield on Bank Nifty Index Returns
The study has examined an impact of macroeconomic variables and dividend yield on Bank NIFTY Index. It analyses the relationship amongst macroeconomic variables and dividend yield. The study used quarterly data from 1 January 2010 to 31 December 2019. It employed statistical measures like regression analysis to analyse the impact of independent variables (macroeconomic factors and dividend yield) on the dependent variable (Bank NIFTY returns) and multicollinearity tests to understand the relationship amongst the independent variables. The observations concluded that GDP, government bond yield and dividend yield have a significant impact on Bank NIFTY returns but CPI does not have a significant impact on Bank NIFTY returns. We can also conclude that all the independent variables are not correlated to each other. The study suggested to policy makers, in India, that they should maintain economic stability through policies of growth that will eventually boost the banking sector and the economy. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Regression Analysis using Machine Learning Algorithms to Predict CO2 Emissions
Precise measurement of fuel consumption and emissions plays an important role in evaluating the environmental effects of materials and stringent emission control methods, especially within the transportation sector. This sector represents a substantial contributor to both global greenhouse gas emissions and the release of hazardous pollutants, making accurate assessment imperative for addressing climate change. The primary objective is to construct accurate predictive models that estimate CO2 emissions based on vehicle attributes, fostering a deeper understanding of the environmental impact of vehicular activities. Leveraging the 'CO2 Emissions-Canada.csv' dataset, the paper embarks on an extensive journey of data preprocessing, exploratory data analysis, and model training. These algorithms are meticulously fine-tuned and evaluated through metrics such as R-squared and mean absolute percentage error, rendering insights into their predictive accuracies. In essence, this paper pioneers a pathway towards environmentally responsible mobility solutions, capitalizing on the fusion of data science and environmental conservation. 2024 Bharati Vidyapeeth, New Delhi. -
Regression Test List Sharding in a Distributed Test Environment
One of the major issues during the regression test of the new version of Real Time Operating System (RTOS) is the time involved in test case execution. The main reason being a single embedded system device under test (DUT) is used to execute the test list containing several test cases. This traditional method of regression test also leads to wasted productivity of the other devices at hand that could be otherwise used during this regression test. Hence, in this paper, we propose a technique that aims at reducing the overall regression test cycle time of a newer version of a Real Time Operating System (RTOS) by employing a method known as "test-list sharding"in a distributed test environment. In the proposed work, multiple DUTs are connected to the test server via a communication network. The test server executes the test list containing several test cases and performs the test-list sharding, that is, distributing test cases to different DUTs and executing them in parallel. After the test is executed on the DUT, the test results are sent back to the test server which will summarize all the results. In the proposed work, the sharding is done by distributing the test cases without overloading or under loading any of the DUTs. Test list is sharded in such a way that the same tests are not sent to multiple DUTs. The main advantage of the proposed method is that the test sharding can be easily scalable to accommodate any number of devices that can be connected to the test server. Also, the test list sharding is done in a dynamic way so that the tests are distributed to an idle DUT that has completed a test execution and ready for another test to execute. The comparison study of executing a sample test list sequentially on a single DUT and distributed test system with multiple DUTs is performed. Results obtained showed the performance gain in terms of test cycle time reduction, scalability, equal load distribution and effective resource utilization. 2023 EDP Sciences. All rights reserved. -
Reimagining the Digital Twin: Powerful Use Cases for Industry 4.0
Novel cohorts of information technologies are transformation and upgrading the global manufacturing sector. The analysis of product procedure in discrete globe might furnish significant perceptions resting on scheme routine which may change manufacturing product design. Digital twin predictive analysis on both historical and future performances of an organizations physical resources leading to proficient industry functioning. In digital twin, cloud-based virtual image of industrial asset is maintained throughout the lifecycle which can be accessed at any time. Digital twin enhances the degree and functions of manufacturing world by integrating with the physical world. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Reinforcement Learning based Autoscaling for Kafka-centric Microservices in Kubernetes
Microservices and Kafka have become a perfect match for enabling the Event-driven Architecture and this encourages microservices integration with various opensource platforms in the world of Cloud Native applications. Kubernetes is an opensource container orchestration platform, that can enable high availability, and scalability for Kafkacentric microservices. Kubernetes supports diverse autoscaling mechanisms like Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA) and Cluster Autoscaler (CA). Among others, HPA automatically scales the number of pods based on the default Resource Metrics, which includes CPU and memory usage. With Prometheus integration, custom metrics for an application can be monitored. In a Kafkacentric microservices, processing time and speed depends on the number of messages published. There is a need for auto scaling policy which can be based on the number of messages processed. This paper proposes a new autoscaling policy, which scales Kafka-centric microservices deployed in an eventdriven deployment architecture, using a Reinforcement Learning model. 2022 IEEE. -
Reinforcement Learning for Language Grounding: Mapping Words to Actions in Human-Robot Interaction
Within the domain of human-robot communication, effective communication is paramount for seamless and smooth collaboration between humans and robots. A promising method for improving language grounding is reinforcement learning (RL), which enables robots to translate spoken commands into suitable behaviors. This paper presents a comprehensive review of recent advancements in RL techniques applied to the task of language grounding in human-robot interaction, focusing specifically on instruction following. Key challenges in this domain include the ambiguity of natural language, the complexity of action spaces, and the need for robust and interpretable models. Various RL algorithms and architectures tailored for language grounding tasks are discussed, highlighting their strengths and limitations. Furthermore, real-world applications and experimental results are examined, showcasing the effectiveness of RL-based approaches in enabling robots to understand and execute instructions from human users. Finally, promising directions for future research are identified, emphasizing the importance of addressing scalability, generalization, and adaptability in RL-based language grounding systems for human-robot interaction. 2024 IEEE.