bluuebunny/crossref_metadata_2025_split huggingface dataset:
Simulation study of droplet formation in inkjet printing using ANSYS FLUENT
Authors: Thakur Neha , Murthy Hari 
Published: January 2022

Abstract
Flow simulations of jetting of inkjet drops are presented for water and ethylene glycol. In the inkjet printing process, droplet jetting behaviour is the deciding parameter for print quality. The multiphase volume of fluid (VOF) method is used because the interaction between two phases (air and liquid) is involved in the drop formation process. The commercial inkjet printer has a nozzle diameter of ∼73.2μm. In this work, a simulation model of inkjet printer nozzles with different diameters 40μm, 60μm, and 80μm are developed using ANSYS FLUENT software. It is observed that when water is taken as solvent then the stable droplets are generated at 60μm nozzle diameter till 9μs because of its low viscosity. For higher diameter, the stamen formation is observed. Ethylene glycol stable droplets are achieved at 80μm nozzle diameter till 9μs because of their high viscosity (∼10 times that of water). Along with the droplet formation, the sustainability of the droplet in the air before reaching the substrate is also important. The simulation model is an inexpensive, fast, and flexible alternative to study the ink characteristics of the real-world system without wasting resources.
Crop prediction using machine learning
Authors: Shripathi Rao Madhuri , Singh Arushi , Subba Reddy N.V. , Acharya Dinesh U 
Published: January 2022

Abstract
For most developing countries, agriculture is their primary source of revenue. Modern agriculture is a constantly growing approach for agricultural advances and farming techniques. It becomes challenging for the farmers to satisfy our planet’s evolving requirements and the expectations of merchants, customers, etc. Some of the challenges the farmers face are-(i) Dealing with climatic changes because of soil erosion and industry emissions (ii) Nutrient deficiency in the soil, caused by a shortage of crucial minerals such as potassium, nitrogen, and phosphorus can result in reduced crop growth. (iii) Farmers make a mistake by cultivating the same crops year after year without experimenting with different varieties. They add fertilizers randomly without understanding the inferior quality or quantity. The paper aims to discover the best model for crop prediction, which can help farmers decide the type of crop to grow based on the climatic conditions and nutrients present in the soil. This paper compares popular algorithms such as K-Nearest Neighbor (KNN), Decision Tree, and Random Forest Classifier using two different criterions Gini and Entropy. Results reveal that Random Forest gives the highest accuracy among the three.
Word Sense Disambiguation Models Emerging Trends: A Comparative Analysis
Authors: Tyagi Nemika , Chakraborty Sudeshna , Jyotsna  , Kumar Aditya , Katasohire Romeo Nzanzu 
Published: January 2022

AbstractWord Sense Disambiguation (WSD) arises due to the presence of ambiguity in the text during the semantic analysis of natural languages. It is a major unsolved problem in the area of Natural Language Processing (NLP) and its applications. This paper explores and reviews WSD algorithms that have contributed to, or created state-of-art solutions in recent years. Moreover, this paper also aims to analyze the recent technological trends in the domain of WSD which can give us leverage to identify the possible future trajectory of the search for better WSD solutions.
Compressive Sensing Magnetic Resonance Image Reconstruction and Denoising using Convolutional Neural Network
Authors: Singh Ram , Kaur Lakhwinder 
Published: January 2022

Abstract
Restoration of high-quality brain Magnetic Resonance Image (MRI) from the sparse under-sampled complex k-space signal is a widely studied ill-posed inverse transform problem. A deep learning-based data-adaptive and data-driven convolutional technique has been proposed for high-quality MRI recovery from its under-sampled complex domain k-space signal. The uniform subsampling process is very slow in phase-encoding to generate high-resolution images. The longer scan times degrade the perceptual image quality. Various factors contribute to image degradation during data acquisition such as the inception of body motion artifacts, the thermal energy effects of the body, and random noise artifacts due to voltage fluctuations. Keeping in view the patient’s critical condition and comfort, longer scan times are not preferred in practice. To reduce the image acquisition time, noise levels, and motion artifacts in the MR images, Compressive Sensing (CS) provides an accelerated way to reconstructs the high-quality MR image from very limited signal measurements acquired much below the Nyquist rate. However, such data acquisition strategies require advanced computer algorithms for the reconstruction of high-quality MRI from the undersampled MRI data. An improved CNN-based MRI reconstructed algorithm has been presented in this paper which shows better performance to reconstruct high-quality MRI than similar other MR image reconstruction algorithms. The performance of the proposed algorithm is measured by image quality checking tools such as normalized-MSE, PSNR, and SSIM.
Autism Spectrum Disorder Therapy: Analysis of Artificial Intelligence integrated Robotic Approach
Authors: Ananth Rao Ananya , Qien Yeau Tan Shaun , Raghavi R , Srivastava Archit , Renumadhavi C H 
Published: January 2022

Abstract
Autism Spectrum Disorder is a developmental disorder that may manifest in a myriad of ways such as difficulties in social interaction and a tendency to engage in repetitive patterns of behaviour. Over the years, several kinds of treatment protocols have been proposed and implemented. One such area that is attracting the attention of researchers in the field is a robot-based approach in the treatment of children diagnosed with the disorder. Here we propose a viable method via the integration of apex technological methods like Artificial Intelligence, Machine Learning and Medical Robotics, coupling it with problem specific algorithms in OpenCV along with principles of Applied Behavioural Analysis to help possibly alleviate a key symptom displayed by children in terms of level of social interaction - that of eye-contact. This would be achieved via an AI-integrated Robotic Framework. The project also considers the possibility of inclusion of the growing research field of Quantum Computing to realize the process and investigates its viability as a potential source of innovation in the future.
Motorsport Data Acquisition System and Live Telemetry using FPGA based CAN controller
Authors: Banerjee A , Jindal A V , Shankar A , Sachdeva V , Kanthi M 
Published: January 2022

Abstract
The paper describes the design and working of a motorsport data acquisition, logging, live telemetry, and display system developed using the Controller Area Network (CAN) communication protocol as the backbone of the arrangement. The main controller of the CAN system is the myRIO which was programmed using LabVIEW. A Formula One car hosts over a hundred sensors during each of its races. The data acquisition/logging system, although does not directly affect the car’s performance, is indispensable when it comes to the testing and design phase of the car. Designers can validate their assumptions and calculations, real-time data during testing can be a safety indicator and it provides insight to the driver about the performance of the vehicle. The FPGA-based controller for CAN is designed for data acquisition and live telemetry system with the interest of the formula car team in mind. The design choices were made to improve and deliver a more effective system than the pre-existing ones. All choices of controllers, sensors, formatting were custom made for the requirements of the team. All programmable devices were coded individually to suit the system and the graphical user interface was designed internally. Data acquired by the proposed system helps in making sure that the car achieves the goals that were envisioned when it was designed.
Network intrusion detection: a comparative study of four classifiers using the NSL-KDD and KDD’99 datasets
Authors: Devarakonda Ananya , Sharma Nilesh , Saha Prita , Ramya S 
Published: January 2022

Abstract
As most of the population acquires access to the internet, protecting online identity from threats of confidentiality, integrity, and accessibility becomes an increasingly important problem to tackle. By definition, a network intrusion detection system (IDS) helps pinpoint and identify anomalous network traffic to bring forward and classify suspicious activity. It is a fundamental part of network security and provides the first line of defense against a potential attack by alerting an administrator or appropriate personnel of possible malicious network activity. Several academic publications propose various artificial intelligence (AI) methods for an accurate network intrusion detection system (IDS). This paper outlines and compares four AI methods to train two benchmark datasets- the KDD’99 and the NSL-KDD. Apart from model selection, data preprocessing plays a vital role in contributing to accurate solutions, and thus, we propose a simple yet effective data preprocessing method. We also evaluate and compare the accuracy and performance of four popular models- decision tree (DT), multi-layer perceptron (MLP), random forest (RF), and a stacked autoencoder (SAE) model. Of the four methods, the random forest classifier showed the most consistent and accurate results.
Smart Farming enabled by IoT and Spectral Imaging
Authors: Mohanty Pratik , Valagadri Vivek , Ramya S 
Published: January 2022

Abstract
Smart Farming System is an emerging concept which utilizes sensors in the field enabled through IoT to get live data from the farm. This paper aims at developing such a Smart Farming system using the highly advanced technology of Texas instruments microcontrollers, MSP430 and TIVA C Series TM4C1294. Along with IoT the system uses Multispectral Imaging in conjunction with Wireless Soil Embedded Sensor Networks. The goal of the system is to provide reliable live data which is obtained from the multiple sensor nodes placed throughout the farm, that use the sink nodes to transfer the data to the cloud. The farmer can access this data using the Blynk Mobile app and can thus take further calculated actions towards maintaining the farm and further monitor the soil/crop health to increase the ultimate yield from his farm.
Formal Verification of CHAP PPP authentication Protocol for Smart City/Safe City Applications.
Authors: Pradeep R , Sunitha N R 
Published: January 2022

Abstract
A smart city is a technologically advanced metropolitan region with several connected devices that collects data using various electronic technologies, voice activation methods, and sensors. The information obtained from the data is utilised to efficiently manage assets, resources, and services; in turn, the data is used to enhance operations throughout the city. Achieving security for smart cities is one of the major challenges as the number of connected devices increases the vulnerability also increases. The security of a smart city system depends on the reliability of the security protocols used by the security systems. To design and develop a highly secure system for a smart city the security protocols used must be highly reliable. To prove the reliability of a security protocol the validation technique is not desirable because of its several drawbacks, these drawbacks can be overcome using the formal verification technique which provides the mathematical proof for its correctness. In this work, The Challenge-Handshake Authentication Protocol Point-to-Point (CHAP PPP) which is more commonly used in PPP authentication of smart cities is formally verified using the well-known verification technique known as the model checking technique. The Scyther model checker is the tool used to build the abstract security protocol model.
An approach framework of transfer learning, adversarial training and hierarchical multi-task learning - a case study of disinformation detection with offensive text
Authors: Chandra Shravan , Das Bhaskarjyoti 
Published: January 2022

AbstractWith society going online and disinformation getting accepted as a phenomena that we have to live with, there is a growing need to automatically detect offensive text on modern social media platforms. But the lack of enough balanced labeled data, constantly evolving socio-linguistic patterns and ever-changing definition of offensive text make it a challenging task. This is a common pattern witnessed in all disinformation detection tasks such as detection of propaganda, rumour, fake news, hate etc. The work described in this paper improves upon the existing body of techniques by bringing in an approach framework that can surpass the existing benchmarks. Firstly, it addresses the imbalanced and insufficient nature of available labeled dataset. Secondly, learning using relates tasks through multi-task learning has been proved to be an effective approach in this domain but it has the unrealistic requirement of labeled data for all related tasks. The framework presented here suitably uses transfer learning in lieu of multi-task learning to address this issue. Thirdly, it builds a model explicitly addressing the hierarchical nature in the taxonomy of disinformation being detected as that delivers a stronger error feedback to the learning tasks. Finally, the model is made more robust by adversarial training. The work presented in this paper uses offensive text detection as a case study and shows convincing results for the chosen approach. The framework adopted can be easily replicated in other similar learning tasks facing a similar set of challenges.
Identification of Diabetic Retinopathy (DR) using Image Processing
Authors: Vikramathithan A C , Pooja P , Bhaskar M S , Navya S , Rakshith K B 
Published: January 2022

Abstract
Diabetes appears in two varieties: Type-1 and Type-2. The former is chronic and can last for years together, whereas the latter can be cured if identified and treated at a premature stage. The symptoms of diabetes affecting the eyes appear very subtle and hence, identifying irregularities in retinal images is a demanding process for medical practitioners. Thus, there was a need to find a method to detect these abnormalities by observing the retinal images non-invasively. After going through research projects and recent developments in identifying DR, we found various techniques/strategies employed, their advantages and drawbacks followed by the objective of overall findings, and the importance of a good DR detection system. Our proposed method calls to attention the importance of early screening, using geometrical relations, multiple thresholding methods and usage of convolutional neural networks as means of overcoming the factors that stand as obstacles in timely detection.
Bus Travel Time Prediction: A Comparative Study of Linear and Non-Linear Machine Learning Models
Authors: Ashwini B P , Sumathi R , Sudhira H S 
Published: January 2022

Abstract
Congested roads are a global problem, and increased usage of private vehicles is one of the main reasons for congestion. Public transit modes of travel are a sustainable and eco-friendly alternative for private vehicle usage, but attracting commuters towards public transit mode is a mammoth task. Commuters expect the public transit service to be reliable, and to provide a reliable service it is necessary to fine-tune the transit operations and provide well-timed necessary information to commuters. In this context, the public transit travel time is predicted in Tumakuru, a tier-2 city of Karnataka, India. As this is one of the initial studies in the city, the performance comparison of eight Machines Learning models including four linear namely, Linear Regression, Ridge Regression, Least Absolute Shrinkage and Selection Operator Regression, and Support Vector Regression; and four non-linear models namely, k-Nearest Neighbors, Regression Trees, Random Forest Regression, and Gradient Boosting Regression Trees is conducted to identify a suitable model for travel time predictions. The data logs of one month (November 2020) of the Tumakuru city service, provided by Tumakuru Smart City Limited are used for the study. The time-of-the-day (trip start time), day-of-the-week, and direction of travel are used for the prediction. Travel time for both upstream and downstream are predicted, and the results are evaluated based on the performance metrics. The results suggest that the performance of non-linear models is superior to linear models for predicting travel times, and Random Forest Regression was found to be a better model as compared to other models.
A Comparative Analysis of Machine Learning Algorithms in Design Process of Adaptive Traffic Signal Control System
Authors: Savithramma R M , Sumathi R , Sudhira H S 
Published: January 2022

Abstract
In recent decades machine learning technology has proved its efficiency in most sectors by making human life easier. With this popularity and efficiency, it is applied to design traffic signal control systems to mitigate traffic congestion and distribute waiting delays. Hence, many researchers around the world are working to address this issue. As a part of the solution, this article presents a comparative analysis of various machine learning models to come up with a suitable model for an isolated intersection. In this context, eight machine learning models including Linear Regression, Ridge, Lasso, Support Vector Regression, k-Nearest Neighbour, Decision Tree, Random Forest, and Gradient Boosting Regression Tree are selected. Shivakumara Swamiji Circle (SSC), one of the intersections in Tumakuru, Karnataka, India is selected as a case study area. Essential data is collected from SSC through videography. The selected models are developed to predict green time based on traffic classification and volume in Passenger Car Units (PCU) for each phase on the PyCharm platform. The models are evaluated based on various performance metrics. Results revealed that all the selected models predict green splits with 91% accuracy using traffic classification as input, whereas, models showed 85% accuracy with PCU as input. And also, Gradient Boosting Regression Tree is the best suitable model for the selected intersection, whereas, Decision Tree is not referred model for this application.
Airlight (A UAVs based light guidance and search light system)
Authors: Paleja Aman , Kumar Shreyash , Choraria Amit , Hariharan Arjun , Tendolkar Atharv , Pai M M Manohara 
Published: January 2022

Abstract
Airlight, an UAV (Unmanned Aerial Vehicle) based real time focus and searching light system is a proposed solution which ensures guiding people safely on their path during a dark environment is one less thing to worry about. It is designed to identify a person through algorithms of object detection and also through usage of IoT and sensorics to enable power efficient use of spotlight and hence autonomously guiding people on their path during night-time. Airlight proves to be useful in various sectors such as helping farmers to look after their fields during night-time, security purposes, guiding construction workers while lifting heavy objects, military applications etc.
Aerodock (a smart, autonomous charging and docking station for unmanned aerial vehicles)
Authors: Mukerjee Laaboni , Yadav Mukul , Choraria Amit , Tendolkar Atharv , Hariharan Arjun , Manohara Pai M M 
Published: January 2022

AbstractThe COVID-19 pandemic has laid bare the need for contactless operations. While unmanned aerial vehicles (UAVs) are being developed to aid humans in countless domains, the need for effective battery management and performance optimization remains a huge task. The proposed solution, the “AeroDock”, aims to tackle these challenges by using wireless power transfer (WPT) technology coupled with smart monitoring of the drone’s health. The performance and hardware checks are assessed at the user end via cloud computing and IoT technology. This system is contact-less, safe, reliable and its usage is not affected by external factors. Thus, the AeroDock is a smart docking station for UAVs which eliminates the need for human intervention in effective charging and maintenance.
Smart Aero-Spider: An adaptive and selective smart switching aerator system
Authors: Yadav Shubham , Bharadwaj Yashas , Hariharan Arjun , Choraria Amit , Tendolkar Atharv , Manohara Pai M M 
Published: January 2022

Abstract
The recovery from the COVID-19 pandemic hit shows the emergence of increase in quality of life across various parts of the world. With this lifestyle change, people are looking towards high quality food. Fish being a major source of protein, the industry producing fish from aquaculture is booming. The proposed smart aerator system provides an integrated array of underwater systems for selective aeration of the water body. The smart system ensures targeted aeration to guarantee optimal levels of dissolved oxygen at all times. This is beneficial for perfect survival, growth, and reproduction of fishes. The strategically placed spider aerators are turned on when readings from the dissolved oxygen level at the location is below the optimum range of values. The air blower system consists of an intelligent switching system to activate the right aerator based on the requirement. The sensor data is relayed to the cloud with a wireless communication module. This data can be used for useful insights and all-round monitoring of the water body. The respective aerators have IR sensor to detect movement alongside on-board LEDs to indicate functioning status. Overall, this ensures maximum accelerated growth of healthy fishes. Thus, the solution aims at efficiently boosting the ability of the aquaculture industry to meet the ever-growing demand of consumers.
Aqua-Vision (An underwater panoramic camera with computer vision and analytics)
Authors: Sudhakar Siddharth , Yadav Shubham , Dhelia Manav , Taysheti Pranav , Hariharan Arjun , Choraria Amit , Tendolkar Atharv , Manohara Pai M M 
Published: January 2022

AbstractIn the present social media-bound lifestyle, capturing memories and keeping them accessible is gaining a significant demand globally. For this purpose, a robust, portable camera system for recreational or commercial purposes can be of substantial advantage to society. Aqua-Vision intends to bring an affordable underwater camera system with various innovative features to the hands of consumers. The smart module consists of a waterproof gimbal camera that can be used underwater, providing a hassle-free and reliable user experience and offers rotary motion along two axes. The camera features various general modes like panorama, burst shot, and smart modes using inbuilt computer vision algorithms. The gimbal camera setup can be controlled and switched remotely between all possible modes using a mobile application. All the above features will allow the user to capture photos/videos in any possible setup and use the camera module for various applications. The advent of such innovative, convenient, and robust modules will help cater to the market demands effectively.