bluuebunny/crossref_metadata_2025 huggingface dataset:
A 1.2v ΔΣ ADC Modulator Using 4-bit SAR Quantizer for Biomedical Applications by using 65nm CMOS Technology
Authors: Lakshmi Bhavani G. , Muzafar Patan , Usha Rani T. , Siddarath Reddy K. , Prathusha K. , Pavani Pasupuleti , Kumar Manish
Published: March 2024

This research focuses on enhancing Sigma-Delta ADC modulators for biomedical applications by leveraging the working principle of Successive Approximation ADC circuits. The proposed modulator includes a comparator, DAC, successive approximation register, and control circuit. The operational process begins with the sample and holds circuit-initiating conversions by sampling the input signal, and then compared with the DAC output. Using a 4-bit example, the successive approximation register refines the DAC output through iterative bit adjustments until the closest digital code approximation to the input is voltage independent of input voltage, occurring incrementally one bit at a time. The proposed enhancement aims to optimize Sigma-Delta ADC modulator performance for biomedical applications, ensuring high resolution and speed. Typical conversion speeds range from 2 to 5 MSPS, with resolutions varying from 8 to 16 bits, contributing to improved precision and efficiency in capturing and digitizing biomedical signals.
Real-time Soldier Health and Location Tracking System
Authors: Aysolal Sai Chander , Addetla Preethi , Arige Venkatesh Surender Rahul , Banrapolu Bharath Reddy , Renatla Pranay Raj , Lakshmi Prasanna Y. , Ivanovich Vatin Nikolai
Published: March 2024

To improve military personnel's overall health and operational efficiency, the Soldier's Health Monitoring and Position Tracking System integrates innovative technology in a comprehensive way. Military commanders are guaranteed complete situational awareness with this system's combination of real-time health monitoring and exact position tracking. Vital physiological indicators including heart rate, temperature, and blood oxygen levels are continuously collected and analysed as part of the health monitoring element. The measurements are sent wirelessly to a command centre, so any possible health problems may be identified quickly and responded to quickly. In order to keep meticulous records of each soldier's exact whereabouts in the field, the system simultaneously makes use of cutting-edge GPS and INS technology. The seamless integration of geo-spatial information with health data gives commanders a comprehensive picture of the troops' physical state and where they are. Military authorities may make educated judgements based on the collective health and positioning data using the system's user interface, which provides an intuitive dashboard for real-time monitoring. The data will be sent to the command centre via the Internet of Things. Components of the suggested system include gearbox modules, small wearable physiological equipment, and sensors. As a result, the suggested gear may be used to provide a cheap method of protecting the precious human life on the battlefield. Using global positioning system (GPS) and cellular service mobile phone (GSM) technology, this device can wirelessly monitor the soldier's vitals, including his or her heart rate and temperature.
Blood group determination using fingerprint
Authors: Nihar T. , Yeswanth K. , Prabhakar K.
Published: March 2024

The fingerprint pattern stands out as the most authentic and unique characteristic defining human identity. This unique pattern is immutable and persists unaltered until an individual's demise. In various circumstances, particularly in legal proceedings, fingerprint evidence is highly regarded. The distinctive minutiae pattern of each person is unparalleled, with the probability of resemblance being exceedingly low, nearly one in sixty-four thousand million. This distinctiveness holds true even for identical duplet. The individualistic ridge pattern persists unchanged from birth, serving as a constant aspect of personal identity. This paper presents a method involving the comparison of specific feature patterns derived from fingerprints for personal identification systems. Fingerprint data is employed in the investigation of blood group determination as well. In the process of fingerprint matching, ridge frequency is assessed, and spatial features are extracted using a Gabor filter for this specific purpose. Consequently, blood group determination can be performed using fingerprint analysis.
Automated grass cutter using renewable energy
Authors: Lingappa J. , Raghavender V. , Aparna G. , Manideep Ch.
Published: March 2024

Traditional grass cutting relies on manpower, consuming time and often resulting in an uneven grass height structure. To circumvent these challenges, it is imperative to develop a system capable of grass cutting without human intervention. This study introduces a grass-cutting robot powered by a solar-charged battery. The operation of this robot is conveniently controlled through an Android phone. Notably, this system is cost-effective compared to alternative solutions, boasting a rugged, durable, and maintenance-free design. Additionally, its reliance on solar energy for battery charging renders the system environmentally friendly and free from pollution.
Classification of intracranial hemorrhage (CT) images using CNN-LSTM method and image-based GLCM features
Authors: Mucha Swetha , Ramesh Babu A.
Published: March 2024

A hybrid is used, combining feature-based method transformed-based features with image-based grey level co-occurrence matrix features. When it comes to classifying cerebral hemorrhages CT images, the combined feature-based strategy performs better than the image-feature-based and transformed feature-based techniques. Natural language processing using deep learning techniques, particularly long short-term memory (LSTM), has become the go-to choice in applications like sentiment analysis and text analysis. This work presents a completely automated deep learning system for the purpose of classifying radiological data in order to diagnose intracranial hemorrhage (ICH). Long short-term memory (LSTM) units, a logistic function, and 1D convolution neural networks (CNN) make up the suggested automated deep learning architecture. These components were all trained and evaluated using a large dataset of 12,852 head computed tomography (CT) radiological reports.
Multi-Modal Biometric Recognition for Face and Iris using Gradient Neural Network (Gen-NN)
Authors: Sai Satyanarayana Reddy S. , Bommala Harikrishna , Sakthidharan G.R. , Ivanovich Vatin Nikolai
Published: March 2024

In recent years, Biometric system are the one, which is widely used method for the recognition and identification of an individual that are highly demanded approach for its absolute security and accuracy which plays a vital roles in banking, commercials, business and other fields. Moreover this research is based on the multimodal biometrics which is recommended for its high recognition performances and it overcome the demerits of unimodal biometric approach. This research concentrate two multi-modal biometric traits such as face and iris, and propose Gradient Neural Network (Gen-NN) method to improve the biometric authentication by using the VISA face and iris multi-modal biometric database also used ResNet-101 and WaveNet for the feature extraction where the input of face and iris can be extracted.
Android accident detection and alert system
Authors: Bharath Reddy Sadda , Vivek Reddy Pulakandla , Indra Reddy Kaveli , Devraj Kalpure , Sravanthi Jajimogga , Maksudovna Vafaeva Khristina
Published: March 2024

Android smartphone app will automatically detect the accident that will be occurred. The app immediately retrieves the GPS location and activates an alarm screen. With one tap, the user can request emergency assistance. The app calls the local emergency number and sends SMS alerts to predefined contacts. The alerts provide accident details including location coordinates, number of passengers, and crash characteristics. Development challenges include accurately identifying crashes across varying conditions while minimizing false positives, integrating with onboard vehicle systems for additional data, addressing cybersecurity risks, optimizing for minimal battery usage, and easing privacy concerns over data collection. If technical obstacles can be overcome, such an automated crash detection and emergency alert app could help expedite emergency responses and improve road safety outcomes. Intended as an low-cost, scalable supplementary crash alert system, it has the potential to save lives worldwide.
Advancements in IoT Technology: A Comprehensive Approach to Accident Detection and Emergency Response
Authors: Kumar T.V.V. Pavan , Vaishnavi Tiwari , Lakshmi V. , Gupta Gaurav
Published: March 2024

Infrastructure and also technological advancements have actually made life less complex for individuals, causing huge demand for vehicles. This results in an increase in road mishaps. An accident is a vulnerable and unintended occasion, as well as it can additionally occur because of the carelessness of the vehicle drivers. With the increasing number of road accidents and the high mortality rate associated with them, there is a growing need for a system that can detect accidents and provide timely assistance to the victims. In this project, we propose an IoT based Accident Detection and Rescue System that leverages various sensors,
GSM, GPS, fire sensor, IR sensor, MQ135 Gas sensor, I2C LCD, DC Motor modules, to detect an accident and alert the concerned authorities. The proposed system has the potential to reduce the response time in the event of an accident, thereby improving the chances of survival for the victim. The project combines the latest advancements in IoT technology and Thingspeak to store the data.
Improve air quality tracking through a fully intelligent IoT-based program with AERO Virtual Assistant
Authors: Yanamadni Venkata Rao , Shesagiri T. , Swathi Palamakula , Naveen Kumar Pattipati , Bommala Harikrishna , Vijendar Reddy G. , Usanova
Published: March 2024

Have you thought about the quality of the air we breathe? Living a healthy and happy life is so important in the wake of the COVID-19 pandemic. To live a healthy life, everyone needs to be aware of the quality of the air they breathe and how their actions impact current and future air quality. This project presents his IoT-based system for measuring, monitoring, communicating, and predicting future air quality. The system was developed using a combination of IoT and machine learning to monitor, analyze and predict air quality in real-time. Air quality can be measured using various gas sensors, temperature and humidity sensors. It also measures the number of hazardous gases emitted in industrial areas. Machine learning models are applied to data values from various sensors to predict future air quality. The proposed solution monitors the current air quality on the device through a small smart application, observes fluctuations using graphs, and sends SMS alerts if air quality parameters exceed acceptable values. can. Another important feature is the addition of AERO, a virtual assistant that allows anyone to communicate atmospheric details. This system allows businesses, organizations, industries, farmers, gardeners, healthcare workers, city dwellers, etc. to easily connect, communicate, and monitor the current air quality in their daily lives and lead healthier lives. You will be able to. This will allow you to perform the necessary actions.
Effective integration of the internet of things and ensemble learning approaches for enhancing sudden topple recognition assisted by cloud computing technology
Authors: Reddy K. Ramakrishna , Kumar Roy Dharmendra , Murthy P.L. Srinivasa , Sethy Abhisek , Selvam K. , Sharath M.N. , Gurnadha Gupta Koppuravuri , Nagendra Kumar Y.J. , Singh Harminder
Published: March 2024

Falls provide a significant public health hazard globally for the senior population. Untreated Sudden Topple in the elderly leads to functional loss and a notable decline in mobility, autonomy, and quality of life. Early identification of Sudden Topple is essential for a person's well-being or to provide needed care. Telehealth data centers need scalable processing and storing resources to accommodate the increasing number of individuals. Specialized methods that enable the transfer of just pertinent data are necessary. This study presents a Hybrid System composing Cloud Computing and the Internet of Things (IoT) (HS-CC-IoT) to monitor many elderly individuals, identify Sudden Topple, and alert caretakers. The experiments were conducted to reveal the necessary criteria for facilitating the operation of large-scale systems. The research assessed many machine learning algorithms for their appropriateness in detection. The experimental tests to identify sudden topples are in cloud-based data centers and on an Edge IoT gadget with an Ensemble Learning Algorithm. Experiments on the user-to-cloud data transfer showed that a substantial decrease in the quantity of saved and transferred data is possible when conducting Sudden Topple identification on the Edge.
IoT-based parking surveillance scheme: Emerging a smart, effective, and secured solution for urban parking management and performance improvement
Authors: Shankar S. Siva , Srinivas S. , Kodati Sarangam , punati Kondalarao , Purushotham P. , Gurnadha Gupta Koppuravuri , Manikandan G. , Nagendra Kumar Y.J. , Dutt Amit
Published: March 2024

Parking spots have become a prevalent concern in urban growth. The number of automobiles is increasing faster than accessible parking spaces. This problem is addressed by implementing an Internet of Things (IoT)-based Parking Surveillance Scheme (IoT-PSS) for the smart campus. The system aims to efficiently match vehicles with available parking spaces, resulting in time savings, improved parking space usage, reduced management expenses, and decreased traffic congestion. Edge computing provides a new answer to data processing in monitoring systems by using advancements in IoT, artificial intelligence, and transmission technologies to analyze data regionally at the edge. This research explores the viability of using edge computing for smart parking monitoring, focusing on detecting parking occupancy via a real-time video stream. The structure operating pipeline to prioritize adaptability, online surveillance, data transfer, detection precision, and system dependability. The experimental findings show that the final detection approach achieves more than 95% accuracy in real-world situations with outstanding effectiveness and reliability. The IoT-PSS system is essential for smart cities and is a strong basis for future use in smart transportation networks.
Comparison study of clinic radiological profile of primary lung cancer using EDAS method
Authors: Kamalakannan Maithili , Rajaseker Subha , Shankar Siva , Bharathi P. , Sharma Vishal
Published: March 2024

When bodily cells transform and proliferate out of control, it is called cancer. Cells are the minuscule building blocks that make up your body. Normal cells develop as required by the organism and degenerate when no longer required. The aberrant cells that make up cancer develop even if your body doesn't require them to. The aberrant cells in the majority of malignancies develop into a bump or mass known as a tumor. Long enough for cancer cells to exist in the body, they can spread (invade) into surrounding regions. They may even spread to various body regions (metastasis). Two key turning points for the practical application of targeted therapy in lung cancer patients were the sequencing of the malignant tumors genome and the development of medicines that target driver mutations. Understanding euplastic cells and mutational mechanisms, along with how they change as tumors form and how different cancer cells differ genetically, are all necessary for this. It is hoped that ongoing global initiatives to systematically identify the most pertinent genetic alterations for each subgroup of pulmonary cancer will raise the proportion of tumors that always respond better to novel medications targeting different genetic profiles. Oral medicines with significantly higher responder rates and lower toxicity than chemotherapy can be used to target many genetic changes. Methology: Alternative: Brain, Bone, Liver, Contra lateral lung, Adrenal gland. Evaluation Preference: Squamous cell carcinoma, denocarcinoma, Small cell lung carcinoma, undifferentiated carcinoma Result: As a result, Adrenal gland and top ranking, whereas the Brain rating received a low ranking received Conclusion: The value of the dataset for Lung cancer in EDAS method shows that it results in Adrenal gland and top ranking.
An Efficient IoT-Based Novel Approach for Fire Detection Through Esp 32 Microcontroller in Forest Areas
Authors: Subbarayudu Yerragudipadu , Vijendar Reddy Gurram , Bhargavi Jammi , Latha Kadavath
Published: March 2024

Forest fires have become a major threat these days. The hazard posed by forest fires has increased globally. It has a detrimental effect on both human and forest ecosystems. Forest fires can be due to natural changes such as changes in the climate and the Greenhouse effect is due to human activities. Human activities are the major reason for forest fires interestingly. It is important to spot the fires early to reduce the major destruction and loss. In this project, we have proposed a system and methodology which is a wireless sensor network to identify forest fires in early stage. Our project will help to detect the fire in the early stage by using the sensor and intimate through a buzzer and a message to the administration of the forest, therefore the immediate necessary actions can be taken in time and can stop the fire. Administrative officers are constantly monitored. This system has many benefits and protects the environment, forest survivors, infrastructure, lives, and ecosystem. Saving our environment is a major thing in this developing country and world. Forest fire is a biggest natural and man-made calamity int he world. It’s a environment tragedy. Once the fire is not detected and it spreads over a huge area destroying everything, such destruction should be avoided and should save our forest habitats effort of this project is to create and implement a IoT based system that can sense and detect forest fires in early and notify to the responsible officials.
Cipher Craft: Design and Analysis of Advanced Cryptographic Techniques for Secure Communication Systems
Authors: Subbarayudu Yerragudipadu , Vijendar Reddy Gurram , Shankar Merugu , Ven Mailagani , Abhilash P.K. , Sehgal Archana
Published: March 2024

The name “cryptography” comes from a Greek word that refers to the art of securing data by organizing it in a disorganized and unintelligible way. It combines software engineering with math. The explosive expansion of the Internet has led to a greater awareness of intriguing uncertainty concerns. Although security is the biggest concern when it comes to the internet, many apps have been developed and designed without taking confidentiality, authentication, and protection—the three essential components of data security—into account. Knowing these kinds of security problems and challenges is going to be more important as our daily activities rely more and more on data networks. Cryptography is necessary to prevent some unwanted customers or persons from gaining access to the data. This study presents a novel hybrid security cipher that combines the three most significant ciphers, such as the Caesar, Rail Fence and Vigenère ciphers. When compared to traditional ciphers, this hybrid encryption cipher offers more security.
Investigating the abnormalities of deep learning with customized architecture using deep learning 4J
Authors: Madhu Bhukya , Nethra Betgeri Sai , Pavan G. , Aerranagula Veerender , Vijaya Rama Raju V. , Gupta Gaurav
Published: March 2024

You In most cases, doctors and the use of image processing tools can assess the placenta—the actual site of physical birth—during pregnancy. Models built using Machine Learning produce performance metrics such as Accuracy, ROC, Precision, Recall, and F-Measure, which quantify this support. This research makes use of the tailored strategy built into the Weka tool, namely the DeepLearning4j package.Using common architectures such as LeNet, VGGnet, ResNet, and Alexnet yields results that are comparable. By iterating over the input layer's Loss functions and the output layer's errors, DeepLearning4j evaluates and optimizes the suggested architecture based on its quality. The experimental results show that out of seven distinct loss functions, the one with the abbreviation "MCXENT" (meaning "Multi-class Cross Entropy") produces the most accurate and least error-prone results. Additionally, a maximum accuracy of 95.7% is obtained, which is considered the best performance. These findings lend credence to an additional machine learning strategy that employs an interactive development tool for gynecologists and offer fresh assurance in its efficacy.
Applying machine learning to soil analysis for accurate farming
Authors: Venkateswara Reddy L. , Ganesh D. , Sunil Kumar M. , Gogula Sreenivasulu , Rekha M. , Sehgal Archana
Published: March 2024

A crucial component of agriculture is soil. Soil analysis is critical for optimizing agricultural practices and ensuring sustainable crop production. Traditional methods are often time-consuming and labor intensive, limiting their scalability and real-time applicability. The application of machine learning techniques in soil nutrient analysis has emerged as a promising solution. There is a lot of complicated soil data, but algorithms that use machine learning can handle it all, enabling accurate prediction and assessment of soil nutrient content. Integration with remote sensing technologies enhances the capabilities of soil nutrient analysis, allowing for rapid assessment at different scales. Machine learning facilitates personalized recommendations for fertilizer application, irrigation strategies, and soil amendments, tailored to the specific needs of learning and adaptive individual fields or crops. The continuous capabilities of machine learning models ensure up-to-date nutrient management recommendations. Challenges include the availability of representative interpretability of models. Nevertheless, the integration of machine learning in soil nutrient analysis offers improved resource utilization, enhanced crop productivity, and sustainable soil management practices. Ongoing research and collaboration with domain experts will further advance the application of machine learning in this field.
IoT-based centralized wireless patient tracking and surveillance system with a cutting-edge approach for advanced healthcare oversight and data-driven analysis
Authors: Ganesh Bande , Gangopadhyay Amit , Kumar Lingamallu Raghu , Devika S.V. , Balasubramani Pradeep , Sharath M.N. , Gurnadha Gupta Koppuravuri , Gobinda Padhan Dola , Bhalla Lalit
Published: March 2024

The construction of smart cities has been progressing quickly due to advancements in 5G and Internet of Things (IoT) technology. Smart transportation systems and smart medical ideas are also being explored alongside smart city initiatives. Integrating the two mentioned technologies, a smart navigational method for ambulances is being developed. When severe patients arrive via ambulance, significant time is wasted in relaying data, and the hospital needs to gain prior knowledge of the patient's condition. An efficient system called the IoT-based Wireless Patient Tracking Method (IoT-WPTM) is proposed to diagnose patients quickly by recording vital metrics such as heartbeat, temperature, and breathing rate using detectors. These variables are transmitted wirelessly to the healthcare before the ambulance is dispatched, allowing doctors to begin medical care promptly. If necessary, the patient monitors the ambulance's position to anticipate arrival and get immediate directions to the closest hospital. The user carries the gadget and is also placed in the ambulance. The gadget allows IoT devices and Android apps to enhance user interaction and streamline information delivery. IoT-WPTM can be surpassed with ease and in a more effective manner for smart medical surveillance systems.
Prediction and efficient installation of solar panel using machine learning
Authors: Sujith A. , Ashwith A. , Sai Kiran reddy K. , Kansal Shruthi
Published: March 2024

Utilizing a comprehensive dataset featuring daily information such as sunrise time, sunset time, minimum and maximum temperatures, this study's primary aim is to ascertain the duration necessary for a solar panel installation to become profitable and reach the breakeven point. Users provide input parameters, including the solar panel area to be installed, location, cost per unit of current, and installation charges. Employing time series analysis and forecasting techniques, the system takes into account various environmental factors, energy generation potential, and local energy prices. The ultimate goal is to predict when the cumulative income from the solar panel installation will surpass the initial installation costs, facilitating an estimation of the point at which the solar panel system will break even and commence generating a net profit. This solar panel profitability analysis holds considerable significance individuals and organizations for contemplating investments in renewable energy. It equips stakeholders with the information required to make informed decisions regarding the feasibility and financial viability of solar panel installations at specific locations. This, in turn, promotes a sustainable and cost-effective transition to renewable energy sources, aligning with the broader goal of reducing our reliance on non-renewable energy and fostering environmentally responsible practices.
Enhancing breast cancer detection from histopathology images: A novel ensemble approach with deep learning-based feature extraction
Authors: Sundar R. , Srinivasulu Ch. , Ramasamy Fellow Jayaraj , Anusha M. Baby , Brahmaiah Madamanchi , Srikanth T. , Gurnadha Gupta Koppuravuri
Published: March 2024

Effective detection and diagnostic procedures are necessary to enhance patient results for the common and life-threatening illness of breast cancer. Current approaches have limits in scalability and efficiency, highlighting the need for more study. This work introduces a hybrid Breast Cancer (BC) detecting approach that merges Deep Learning (DL) with pre-trained modeling of Histopathology Images (HPI) and an ensemble-based Machine Learning (ML) approach. DL integration allows learning and identifying hidden trends in intricate BC pictures, while ML techniques provide interpretability and generalization skills. Contrast Limited Adaptive Histogram Equalization (CLAHE) was used on HPI as a pre-processing technique to improve picture quality. The ResNet50V2 model was used for deep feature extraction. The Ensemble Learning (EL) model combines predictions from four basic ML approaches using soft voting. The research attained a superior accuracy, precision, recall, and F1 score compared to the most advanced models. This study provides substantial advancements in breast cancer diagnosis, thorough performance evaluation, and reliable assessment. Furthermore, it helps medical personnel make well-informed choices, enhance patient care, and improve results for BC sufferers.
Faster abnormality localization and recognition in a secured video bitstream by implementation of video encryption techniques
Authors: Sivalakshmi Mallepogu , Prasad K. Rajendra , Bindu Chigarapalle Shoba
Published: March 2024

Cloud video storage uses an encrypted format to protect user data. It means encrypted video processing is an essential part of secured cloud storage. In order to detect suspicious or anomalous behavior, video surveillance must have encrypted cloud access. The primary goals of this research are to estimate parameters and detect abnormalities in an encrypted video bitstream. Various typical properties of video encoding frameworks and format-compliant encryption algorithms are investigated to identify abnormalities in an encrypted video bitstream using format-compliant encryption. The encrypted bitstream is decrypted to get three different kinds of enhancement features: the sizes of macroblocks, partitions of macroblocks, and the magnitude of the motion vector. The identification and localization methods now do not include video decryption or complete decompression. The proposed strategy has been created to implement the video encryption scheme efficiently and is compatible with various video encryption techniques. The experimental findings demonstrate that, in comparison to other methods, the proposed approach provides optimal running time and detection rate performance.
Novel methods in utilizing wireless sensor networks with blockchain technology: A review
Authors: Mounika Bhargavi Giridi A. , Jhansi L. , Trishika Sharon D. , Harish Goud B.
Published: March 2024

Wireless Sensor Networks (WSNs) play a vital role in achieving quick and efficient results. These networks comprise small, affordable sensors that collect valuable data from their surroundings. The core objective of this project is to optimize the pathways for data transmission, improve communication among sensor nodes, and filter out irrelevant or erroneous data prior to sending it to the central station. This project introduces a new method by implementing blockchain intelligence, which stores and provides optimal data transmission routes. This empowers sensor nodes to identify the most efficient paths for data sharing, eliminating unnecessary routes. Additionally, the blockchain acts as a filter, ensuring that only relevant data is transmitted to the central station. This approach not only saves time by streamlining data routes but also secures data security through the validity of blockchain technology. Furthermore, increases data quality by discarding unnecessary information. This project showcases a smart solution that tackles common WSN challenges, promising to elevate technological capabilities for a variety of applications.
Hybrid model for comprehensive covid-19 regional safety, risk assessment, and advanced vaccine analysis
Authors: Ila Chandana Kumari P. , Swapna Rani K. , Parasa Gayatri , Sridhar P. , Sharath M.N. , Gurnadha Gupta Koppuravuri , Manikandan G.
Published: March 2024

Advancements in transportation infrastructure, shifts in consumption tendencies, and factors like COVID-19 have raised the need and burden on freight transportation. Various firms are assessing freight transportation systems' long-term viability, speed, and robustness worldwide, requiring data and measurement instruments for freight fluidity. This research attempts to provide a hybrid model to analyze the effects of COVID-19 and the subsequent production methods for the manufacturing sector. This work introduces a new and robust integration method by combining the Ordinal Priority Model (OPM) and Fuzzy-based Distance from Average Solution (F-EDAS) for the first time. The OPM approach was used to assess and measure the adverse effects of the epidemic. Production plans were thoroughly considered utilizing the F-EDAS approach. Digitalization and on-site renewable energy are identified as the most crucial recovery methods. The multi-scenario ranking findings assist managers in making resource-allocation choices for implementing post-COVID-19 production plans.
Social media spammer identification using deep learning
Authors: Santhosh Kumar Ch.N. , Kiran Aryan , Kumar Vinay , Somnath  , Suneel Sajja
Published: March 2024

Social media has become a part of our lives with countless individuals actively sharing content and connecting with others. However this open platform also presents a challenge, in dealing with misinfor- mation and the spread of information by individuals. Social media platforms face the task of identifying these users and preventing the dissemination of content. In this study the focus is on Twitter as a case example. Explore the use of deep learning techniques to detect fake accounts. We analyze factors such as account age, follower to following ratio, tweet frequency and URL to tweet ratio. Through comparing algorithms the aim is to determine the effective approach, for identifying and controlling fake accounts on Twitter. Ultimately our goal is to provide insights that empower social media platforms to safeguard their users from misinformation and harmful content. By creating an environment that can encourage more responsible and well informed social interactions.
Machine Vision for Efficient Electric Vehicle Charging Station Deployment
Authors: Viktorovna Nemova Darya , Arun K.
Published: March 2024

This research examines the optimization of the deployment of electric vehicle (EV) charging stations by using machine vision technology, which involves analyzing real-time data and geographical information. Geospatial data analysis reveals prospective sites for charging stations by considering population density and accessibility to roads, hence identifying regions with increased demand for electric vehicle charging. The assessment of electric vehicle (EV) traffic patterns highlights the ever-changing charging requirements at various times and places, underscoring the need of flexible deployment techniques. Furthermore, evaluating the costs of implementing the deployment and the capabilities of charging, it becomes apparent that there are compromises to be made between the initial expenditures of installation, the amount of power generated, and the quantity of charging stations. These trade-offs are essential for optimizing resources. The usage study of charging stations using machine vision reveals variations in the number of available charging points at different stations and the need for adaptive resource distribution timestamps, techniques. The examination of percentage change reveals notable fluctuations in population density, installation costs, and the availability of charging points. This information is crucial for making well-informed decisions about the deployment of charging infrastructure. Combining machine vision insights with geographical and traffic analyses presents a promising method to create data-driven strategies for the placement of EV charging stations. This approach addresses the changing needs of electric mobility and provides guidance to stakeholders for efficient and flexible charging solutions.
Comparative analysis of lithium-ion and flow batteries for advanced energy storage technologies
Authors: Maksudovna Vafaeva Khristina , Sanjeeva P.
Published: March 2024

This research does a thorough comparison analysis of Lithium-ion and Flow batteries, which are important competitors in modern energy storage technologies. The goal is to clarify their unique characteristics and performance measures. Lithium-ion batteries demonstrate superior energy density (200 Wh/kg) and power density (500 W/kg) in comparison to Flow batteries (100 Wh/kg and 300 W/kg, respectively), indicating their ability to store more energy per unit mass and provide higher power outputs. Flow batteries have a competitive advantage in terms of cycle life, providing a longer duration of 1000 cycles compared to Lithium-ion batteries, which only offer 500 cycles. In addition, although Lithium-ion batteries have a higher efficiency of 90% compared to 80% in Flow batteries, the latter exhibit a lower environmental impact with decreased CO2 emissions (30 g/kWh) and a lower toxicity rating. Lithium-ion batteries have lower material costs, amounting to $200 per kilowatt-hour (kWh). However, they entail higher installation costs of $5000 and maintenance costs of $200, when compared to Flow batteries, which have material costs of $150/kWh, installation costs of $8000, and maintenance costs of $300. In addition, Lithium-ion batteries demonstrate superior charging capabilities of 50 kW and discharging rates of 70 kW, surpassing Flow batteries which have charging rates of 30 kW and discharging rates of 40 kW, respectively. The findings of this study highlight the subtle advantages and compromises of Lithium-ion and Flow batteries in terms of different performance parameters. This information is crucial for making well-informed decisions when choosing energy storage technologies that meet specific application needs in a wide range of sectors, including portable electronics and grid-scale applications.
Decentralized control strategies for resilient power systems using multi-agent systems
Authors: Singh Navdeep , Kiran Kumar M.
Published: March 2024

The study examines the use of Multi-Agent Systems (MAS) in decentralized control mechanisms to improve the resilience of power systems. The analysis included simulations, agent interactions, and system dynamics. The results demonstrate the durability of Generator 001 in City A, as it constantly maintains an average power production of 46.5 MW. On the other hand, Generator 002 in City B regularly generates around 29 MW. Storage Unit 003 efficiently fulfills requests by distributing 15 MW upon receiving a prompt from Generator 001. The system is notably influenced by consumer behavior, as seen by Consumer 004 in City C consistently demanding 31 MW. Significantly, when Consumer 005 is activated, it increases demand to 27.5 MW. An examination of percentage variations indicates little swings in generator outputs at peak times, underscoring their stability. Nevertheless, consumer engagement during periods of high demand results in significant surges in the need for resources. The agents demonstrate effective communication by swiftly acknowledging and fulfilling requests for extra power. The results emphasize the potential of MAS as a viable framework for effectively distributing resources and coordinating various agents. This research provides useful insights into adaptive solutions for effectively managing changing power system circumstances. It highlights the crucial role of Multi-Agent Systems (MAS) in maintaining stability, maximizing the use of resources, and meeting the developing energy needs.
Securing electric transportation networks: A machine learning-driven cyber threat detection
Authors: Ivanovich Vatin Nikolai , Sundari Rama
Published: March 2024

The study examines the cybersecurity environment of electric transportation networks using a machine learning-based methodology. It analyzes the behaviors of electric vehicles, charging patterns, cyber threat occurrences, and the performance of machine learning models. An analysis of electric vehicle (EV) data shows that there are differences in battery capacity and distances covered, suggesting the presence of possible weaknesses across different cars. Cyber threat logs provide a comprehensive view of the various levels of threat severity and the time it takes to discover them, illustrating the ever-changing nature of cyber threats in the network. Machine learning models have varying performance; ML003 and ML005 exhibit excellent accuracy and precision in threat identification, whilst ML002 shows significantly lower metrics. These results highlight the need of implementing flexible cybersecurity solutions to handle different electric vehicle behaviors and effectively reduce cyber risks. This research emphasizes the need of using proactive threat detection tactics in order to effectively address high-severity attacks. It also highlights the need for ongoing improvement of machine learning models to strengthen network security. This study enhances our comprehension of cybersecurity obstacles in electric transportation networks, highlighting the crucial significance of machine learning-based analysis in strengthening network resilience against ever-changing cyber threats.
Computer Vision Applications in Intelligent Electric Vehicle Charging Infrastructure
Authors: Chhabra Soosan , Sreevani V.
Published: March 2024

The study examines the use of computer vision technologies into intelligent electric vehicle (EV) charging infrastructure. The objective is to increase station capabilities, maximize resource usage, and enhance user experiences. An examination of the data from charging stations indicates that there are differences in their capacities and capabilities. Certain stations can handle a greater number of cars at the same time because they have higher power outputs and numerous charging connections. The vehicle identification data illustrates the efficacy of computer vision in precisely recognizing various electric vehicle types, hence optimizing authentication procedures for efficient charging. An analysis of charging session data reveals variations in energy use and durations across sessions, underscoring the impact of charging practices on the utilization of charging stations. An examination of use reveals discrepancies in the number of sessions and energy usage among stations, highlighting the need for adaptive infrastructure. Percentage change study management solutions for demonstrates the fluctuating patterns of resource usage, emphasizing the need for flexible resource allocation techniques. The results emphasize the significant impact that computer vision may have on improving the efficiency and flexibility of electric vehicle charging infrastructure. The research highlights the significance of optimizing the allocation of resources, improving algorithms for various contexts, and applying adaptive solutions for optimal management of charging infrastructure. In essence, the study helps to further our knowledge of how computer vision contributes to the development of intelligent EV charging systems. It provides valuable insights into improving the efficiency of infrastructure and enriching user experiences in the field of electric mobility.
Adaptive control of electric vehicle drives through neural network ensembles
Authors: Singla Tara , Sruthi P.
Published: March 2024

This study examines the use of neural network ensembles in adaptive control for electric vehicle (EV) propulsion systems, using simulated data to evaluate their efficacy. The research aims to evaluate the collective performance of a group, analyze the characteristics of electric vehicle drives, examine the feedback from adaptive control systems, and analyze the data used to train neural networks in order to get a thorough understanding of the subject. The results demonstrate the resilience of neural network ensembles in predictive modeling, with mean squared error values ranging from 0.0028 to 0.0042 and R-squared scores between 0.979 and 0.992. An examination of electric vehicle (EV) driving characteristics reveals differences in battery capacity (ranging from 60 to 85 kWh) and motor efficiency (ranging from 85% to 95%). Notably, there are correlations that demonstrate the influence of weight on the needs for battery capacity. An analysis of the feedback parameters in adaptive control reveals speed inaccuracies ranging from -1.8 to -3.2 km/h, battery voltage errors between 1.5 and 2.8 V, temperature mistakes ranging from 1.2 to 2.5°C, and variations in the control signal. This highlights the significant impact these factors have on the adjustments made by the control system. Moreover, examination of the training data for neural networks emphasizes the significance of having a wide range of inputs (0.3-0.9) and the intricate connections between inputs and outputs (0.6-0.95). In summary, these findings highlight the ability of neural network ensembles to improve predictive accuracy, comprehend the dynamics of EV systems, and emphasize the importance of accurate feedback and high-quality training data for effective adaptive control strategies in electric vehicles. These insights are valuable for advancing EV technology and control methodologies.
IoT-Enabled predictive maintenance for sustainable transportation fleets
Authors: Kansal Lavish , Ediga Poornima
Published: March 2024

This study explores the use of Internet of Things (IoT) based predictive maintenance techniques for sustainable transportation fleets. It utilizes various datasets to enhance operational efficiency and reduce environmental consequences. An examination of the fleet data uncovers interesting findings: the average mileage of the fleet is about 28,400 miles, indicating that different vehicles have been used to different extents. Notably, vehicle 002 stands out with the greatest mileage of 32,000 miles. Varying sensor measurements reveal discrepancies in tire pressure, brake pad thickness, and oil levels, suggesting different patterns of wear across the fleet. The historical maintenance data highlight the differences in maintenance intervals among automobiles. Based on predictive maintenance analysis, it is projected that vehicle 001 will need its next oil change after covering 27,000 miles, which is an increase of 2,000 miles compared to its last service. Percentage change study demonstrates the ever-changing nature of maintenance needs, highlighting the need of customized maintenance interventions that are specifically designed for each vehicle's unique characteristics. The combination of these discoveries clarifies the potential of IoT-enabled predictive maintenance in customizing tailored maintenance plans, increasing fleet efficiency, and reducing environmental impact. This research offers practical insights for adopting proactive maintenance techniques, promoting sustainability, and improving operational efficiency in transportation fleets.
Numerical study of impact energy absorption of auxetic structure
Authors: Govardhan Dussa , Rana Davinder , Jayahari L.
Published: March 2024

3D printing technologies have helped researchers fabricate cellular structures and have lightweight and desirable mechanical properties. One such structure is called “auxetic” structures. “Auxetics” is the term used for structures that exhibit a negative Poisson’s ratio (NPR) and offer a wide range of wide range of potential applications in automotive, aerospace and military industries due to their great load-bearing capabilities in association with excellent energy dissipating performance. PLA (Poly Lactic Acid) material is used to 3D print eight different unit cell design. For each unit cell design with three different core thickness of 1.00 mm, 1.50 mm and 2.00 subjected to projectile impact with 10J of kinetic energy were studied by means of explicit nonlinear finite element simulations using LS-DYNA. Studies were carried out. The energy absorbed by the sample, contact force and projectile kinetic energy time history are compared and the damage on each sample is also compared. It was found that the Chiral unit cell absorbs the max energy and have less damage compared to other unit cell design.
Des phonèmes aux graphèmes
Authors: Dister Anne , Moreau Marie-Louise
Published: March 2024

Nous analysons les correspondances phonèmes-graphèmes dans un corpus de près de 12 000 dictées produites lors d’un championnat d’orthographe. Cette analyse montre qu’en fin de scolarité primaire, les élèves maitrisent bien ces correspondances, le pourcentage des écarts, calculé sur le nombre de phonèmes, étant toujours inférieur à 1 %. Cela ne veut pas dire qu’ils écrivent selon la norme orthographique, mais qu’ils utilisent bien les graphèmes-cibles correspondant aux phonèmes-sources. Si le français était une langue transparente comme l’italien ou le finnois, les élèves seraient des champions en orthographe. Notre attention se porte également sur trois règles spécifiques de l’écrit qui concernent la combinaison des graphèmes : n qui devient m devant b ou p dans les voyelles nasales, q+u et la transcription du phonème /ε/ en syllabe écrite fermée. Si les deux premières sont bien maitrisées, c’est moins le cas du phonème /ε/. Que ce soit pour les correspondances phonèmes-graphèmes ou les règles spécifiques qui gouvernent l’écrit, les achoppements se condensent autour de certains mots, plus rares, qui semblent ne pas faire partie du lexique mental des élèves.