bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mapping Activity Area Localization in Functional MRI Imaging with Deep Learning based Automatic Segmented Brain Tumor for Presurgical Tumor Resection Planning
Authors: Ingle Archana , Roja Mani , Sankhe Manoj , Patkar Deepak 
Published: September 2023

Functional Magnetic Resonance Imaging (fMRI) determines small blood flow variations that arise due to brain activity. fMRI major study is about functional anatomy which determines the area of the brain controlling vital functions such as hand and foot motor movements for both left and right, speech mantra, and speech word activities. For this instinctive localization of activity areas for specific tasks is very important. This paper appropriately describes the fMRI paradigm timeline with a modified fMRI paradigm timeline due to the hemodynamic response function (HRF). Efficient activity area localization of thirty-three patients for fMRI data acquired from the hospital is achieved with dynamic thresholding. Dynamic thresholding is also effective in removing excess highlighted areas which helps in the reduction in expert efforts and time required to generate the patient report. The localize activity area is further mapped with deep learning-based automatic segmented brain tumor regions to find overlapping regions. The exact location of the overlapping region is recovered which helps with preoperative counseling and tumor resection planning. All the results are verified and validated by two expert radiologists from the Hospital.
ETMS: Efficient Traffic Management System for Congestion Detection and Alert using HAAR Cascade
Authors: Indira D.N.V.S.L.S. , Tenali Nagamani , Kumar J N V R Swarup , Karthik Jayavarapu , Lakshmi M. Venkata , Jelciya Ch. , Srinivas G. Sai 
Published: September 2023

Rapid social development has resulted in the emergence of a new major societal issue: urban traffic congestion, which many cities must address. In addition to making it more difficult for people to get around town, traffic jams are a major source of the city's pollution crisis. In order to address the problems of automobile exhaust pollution and congestion, this paper uses the system dynamics approach to develop a model to study the urban traffic congestion system from the perspectives of trucks,private cars, bikes and public transportation. This project proposes a system for detecting vehicles and sending alerts when traffic levels rise to dangerous levels using Haar Cascade and Fuzzy Cognitive Maps (FCP). The proposed system uses Haar Cascade to detect moving vehicles, which are then classified using FCP. The system can make decisions based on partial or ambiguous information by utilising FCP, a soft computing technique, which allows it to learn from past actions. An algorithm for estimating traffic density is also used by the system to pinpoint active areas. In congested areas, the system will alert the driver if it anticipates a collision with another vehicle and also Experiments show that the proposed system is able to accurately detect vehicles and provide timely alerts to the driver, drastically lowering the probability of accidents occurring in heavily travelled areas.
The importance of introducing such a system cannot be overstated in today's transportation system. It's a big deal for the future of intelligent urban planning and traffic control. Congestion relief, cleaner air, and increased security are just some of the long-term benefits that justify the high initial investment. To add, this system is adaptable to suburban and rural areas, which can also experience traffic congestion issues.
Hidden Markov Model Deep Learning Architecture for Virtual Reality Assessment to Compute Human–Machine Interaction-Based Optimization Model
Authors: Zhu Chaoyang 
Published: September 2023

Virtual Reality (VR) is a technology that immerses users in a simulated, computer-generated environment. It creates a sense of presence, allowing individuals to interact with and experience virtual worlds. Human-Machine Interaction (HMI) refers to the communication and interaction between humans and machines. Optimization plays a crucial role in Virtual Reality (VR) and Human-Machine Interaction (HMI) to enhance the overall user experience and system performance. This paper proposed an architecture of the Hidden Markov Model with Grey Relational Analysis (GRA) integrated with Salp Swarm Algorithm (SSA) for the automated Human-Machine Interaction. The proposed architecture is stated as a Hidden Markov model Grey Relational Salp Swarm (HMM_ GRSS). The proposed HMM_GRSS model estimates the feature vector of the variables in the virtual reality platform and compute the feature spaces. The HMM_GRSS architecture aims to estimate the feature vector of variables within the VR platform and compute the feature spaces. Hidden Markov Models are used to model the temporal behavior and dynamics of the system, allowing for predictions and understanding of the interactions. Grey Relational Analysis is employed to evaluate the relationship and relevance between variables, aiding in feature selection and optimization. The SSA helps optimize the feature spaces by simulating the collective behavior of salp swarms, improving the efficiency and effectiveness of the HMI system. The proposed HMM_GRSS architecture aims to enhance the automated HMI process in a VR platform, allowing for improved interaction and communication between humans and machines. Simulation analysis provides a significant outcome for the proposed HMM_GRSS model for the estimation Human-Machine Interaction.
Grapes Quality Prediction Using Iot & Machine Learning Based on Pre Harvesting
Authors: Sinha Swati Vishal , Patil B.M. 
Published: September 2023

Minimizing pesticide use, preserving water, as well as enhancing soil health are just a few of the sustainable farming techniques that must be carefully considered while growing grapes of a high calibre. These practices can help preserve the environment and ensure the longevity of the vineyard. However, it is difficult for the farmers to find the suitability of the soil and its environment to cultivate grapes with high quality. Thus this research aims to evaluate the fitness of the soil for the fitness of growing quality grapes with the aid of machine learning algorithm. The research was done on Nasik region which is called as the “Grape Capital of India” situated in Maharashtra. Total of 154 villages were considered for the examination and soil specimens were collected and sent to the government testing lab in Maharashtra. The soil characteristics by considering both micro and macro nutrients, and the water characteristics were obtained from the lab. Also the climatic features, quality of the petiole and fruit characteristics were included for creating the dataset. These data was given to six different machine learning algorithm to classify the soil by defining whether the soil is fit for grapes or not. Moreover, this research proposed to analyze the correlation between the nutrients by which the relationship and dependency between the different nutrients and features were considered for defining the grapes quality. Also both the micro and macro nutrients were given equal importance in defining the soil quality suitable for obtaining high quality grapes. Based on the results obtained, Pimpalas Ramche contains more nutrients for the grape to grow more successfully based on samples gathered from different vine yards and the decision tree classifier scores better than any other classifiers among the machine learning algorithms employed in terms of accuracy.
Risk Mitigation and Monitoring Challenges in Software Organizations: A Morphological Analysis
Authors: Hasib Nida , Rizvi Syed Wajahat Abbas , Katiyar Vinodani 
Published: September 2023

Context: The present body of research on risk mitigation focuses mostly on sparse and dispersed research using various approaches during project development.
Objectives: This paper's goals are to conceptualize and construct a morphological analysis (MA) framework, examine the literature that has already been published on these risks and how to mitigate them in software development, and identify research gaps that suggest areas for further study.
Methods: Based on a survey of 102 works, we provide an MA framework. The MA framework, which is composed of five dimensions and 17 variants, identifies 33 distinct research gaps as the maximum number of potential areas for future study. The implications for future research are covered in the paper's conclusion. Risk reduction during the project development process is extremely beneficial for project managers at an organization in delivering high-quality goods on time and within budget.
Results: Based on a review of the literature, it was discovered that the development of software projects used a very low percentage of official risk mitigation and elimination methods. Research also revealed the significance of people-related risks in the risk mitigation process for the benefit of the organization. Some key research demonstrated experimentation, case studies, and empirical approaches for evaluation. Future work may include examining project managers' perceptions and knowledge of risk management. Managers of software development teams require greater risk-mitigation strategies.
Conclusion: Scholars and researchers need to put more effort into developing quantitative and intelligent risk models. It is crucial to identify major risks so that they won't compromise the project's future success. In our future work, we can employ more models that are helpful for combining quantitative and intelligent risk models using various AI strategies to reduce hazards.
An Adaptive Technique for Crime Rate Prediction using Machine Learning Algorithms
Authors: Rao V. Chandra Shekhar , Spandhana Kallepelly , Srinivas C. , Sujatha M. , Vani Bojja , Venkatramulu S. 
Published: September 2023

Any country must give the investigation and preventive of crime top priority. There are a rising amount of cases that are still pending due to the rapid increase in criminal cases in India and elsewhere. It is proving difficult to classify and address the rising number of criminal cases. Understanding a place's trends in criminal activity is essential to preventing it from occurring. Crime-solving organisations will be more effective if they have a clear awareness of the patterns of criminal behavior that are present in a particular area. Women's safety and protection are of highest importance despite the serious and persistent problem of crime against them. This study offers predictions about the kinds of crimes that might occur in a particular location using ensemble methods. This facilitates the categorization of criminal proceedings and subsequent action in a timely manner. We are applying machine learning methods like KNN, Linear regression, SVM, Lasso, Decision tree and Random forest in order to assess the highest accuracy.
Feature Classification and Extreme Learning Machine Based Detection of Phishing Websites
Authors: Bhagat Pallavi M. , Waghmare Surendra , Waje Manisha , Patil Rupali , Joshi Kavita , Bakuli Meeta 
Published: September 2023

Phishing is a cyber-attack that uses a phishing website impersonating a real website to deceive internet users into disclosing sensitive information. Attackers using stolen credentials not only utilize them for the targeted website, but they may also be used to access other famous genuine websites. This paper proposes a novel approach for detecting phishing websites using a feature classification technique and an Extreme Learning Machine (ELM) algorithm. The proposed system extracts various features from the website URL and content, including text-based, image-based, and behavior-based features. These features are then classified using a feature selection technique, which selects the most relevant features to improve the detection accuracy. The selected features are then fed into the ELM algorithm, which is a powerful machine learning method for classifying and predicting data. The ELM algorithm It trains upon a huge set of data legitimate & phishing websites, and final outcome model is applied to classify unknown websites as either legitimate or phishing. The proposed approach is evaluated on several benchmark datasets and compared with other state-of-the-art phishing detection methods.
The experimental results demonstrate that the proposed approach achieves high detection accuracy and outperforms other methods in terms of precision, recall, and F1-score. The proposed approach can be used as an effective tool for detecting and preventing phishing attacks, which are a major threat to the security of online users.
Violation of Traffic Rules and Detection of Sign Boards
Authors: Venkatramulu S. , Yugasri Bairy , Sadala Triveni Mohan , Revathi Garidepalli , Rao V. Chandra Shekhar 
Published: September 2023

Today's society has seen a sharp rise in the number of accidents caused by drivers failing to pay attention to traffic signals and regulations. Road accidents are increasing daily as the number of automobiles rises. By using synthesis data for training, which are produced from photos of road traffic signs, we are able to overcome the challenges of traffic sign identification and decrease violations of traffic laws by identifying triple-riding, no-helmet, and accidents, which vary for different nations and locations. This technique is used to create a database of synthetic images that may be used in conjunction with a convolution neural network (CNN) to identify traffic signs, triple riding, no helmet use, and accidents in a variety of view lighting situations. As a result, there will be fewer accidents, and the vehicle operator will be able to concentrate more on continuing to drive but instead of checking each individual road sign. Also, simplifies the process to recognize triple driving, accidents, but also incidents when a helmet was not used.
Hybrid Cryptography and Steganography-Based Security System for IoT Networks
Authors: Suguna T. , Padma C. , Rani M. Janaki , Priya G.Padma 
Published: September 2023

Despite the fact that many IoT devices are publicly accessible to everyone on the network, understanding the security risks and threats posed by cyber attacks is critical; as a result, it should be safeguarded. Plain text is constructed into encrypted text, before being delivered by using cryptography, and is then reconstructed back to plain text after receiving a response from the recipient. The steganography technique can be used to hide sensitive information incorporated in a text, audio, or video file. One approach is to hide data in bits that correspond to successive rows of pixels with the same color in an image file. As a consequence, the image file retains the original's appearance while also containing "noise" patterns made out of common, unencrypted data. To do this, the encrypted data is subtly applied to the redundant data. In this work, it is suggested that IoT network data be encrypted using cryptography, and that an encrypted message be concealed inside an image file using steganography. Additionally, it is suggested to enhance the number of bits that may be stored within a single picture pixel. The payload that may be sent through an image is significantly increased by incorporating Convolutional Neural Networks into the classic steganography technique. In this work, we propose, design, and train Convolutional Neural Networks (CNN) to enhance the amount of data that can be securely encrypted and decrypted to show the original message.
Implementation of Decoy Deception based Detection System for Ransomware Attack
Authors: Salunke Mangesh D. , Rathod Subhash G. , Jadhav Hemantkumar B. , Yashwante Meghna , Rewaskar Vaibhav D. , Deshmukh Pranjali V. 
Published: September 2023

Ransomware poses a dangerous threat to cybersecurity. Data as well as rights owned by the user are adversely impacted. The situation has become considerably more critical as a result of the emergence of new ransomware varieties and Ransomware-as-a-Service. In this paper, we presented a novel deception-based and behaviour-based method for real-time ransomware detection. In order to avoid any loss before ransomware is discovered, we build pretend files and directories for nefarious behaviours. We conducted a pilot study using Locky, and the results demonstrate the effectiveness of our strategy with little system resource usage and geographical cost.
Design and Demonstration of Arduino based Low Cost Pulse Oximeter
Authors: Vananth P. N. Shakthi , Rajalakshmi S. , Sathya P. 
Published: September 2023

One of the main reasons for the pushback of the pandemic in India was the awareness that developed in people during the crisis. The adapting mindset of the Indian populace helped turn the tides. The Pulse Oximeter proved to be an important tool that helped in identifying the affected, to get timely healthcare. A very simple information was shared among people that the BPM (heart rate in beats per minute) should be around 60 - 100 (resting) for an average adult and the SpO2, i.e., oxygen saturation must be above 95% for all age groups. Just this was able to counter an entire wave of the Pandemic with the help of early detections, timely isolations, immediate health care and much more. However, access to this was not available to everyone due to the high prices of these devices. This project aims to build a simple monitor module with an Arduino Nano board which can determine the Blood Oxygen level and Heart rate of a person by using the MAX30102 sensor.
Crude Oil Cost Forecasting using Variants of Recurrent Neural Network
Authors: Zaware Sarika , Kanawade Bhavana , Pimpalkar Shilpa , Chetan Aher , Phapale Anuja , Zaware Sumedha 
Published: September 2023

Crude oil cost plays very important role in the country’s economic growth. It is having close impact on economical stability of nation. Because of these reasons it is very important to have accurate oil forecasting system. Due to impact of different factors oil cost data is highly nonlinear and in fluctuated manner. Performing prediction on those data using data driven approaches is very complex task which require lots of preprocessing of data. Working on such a non-stationary data is very difficult. This research proposes recurrent neural network (RNN) based approaches such as simple RNN, deep RNN and RNN with LSTM. To compare performance of RNN variants this research has also implemented Naive forecast and Sequential ANN methods. Performance of all these models are evaluated based on root mean square error(RMSE), mean absolute error(MAE) and mean absolute percentage error(MAPE). The experimental result shows that RNN with LSTM is more accurate compare to all other models. Accuracy of LSTM is more than 96% for the dataset of U.S. Energy Information administration from March 1983 to June 2022. On the basis of experimental result, we come to the conclusion that RNN with LSTM is best suitable for time series data which is highly nonlinear.
EHFT: An Ensembled Hyperopt Fine-Tuned Neural Network for Disease Detection in Tomato Plants
Authors: Padmanabhuni Sri Silpa , Gera Pradeepini 
Published: September 2023

The identification of unhealthy plants in the crops at the early stage of cultivation helps for good farming. Unhealthy parts can be recognized using shape, color and texture, which are processed using feature extraction techniques. The feature extraction system stores the images in the matrix pixel format, which requires 3 channels for processing the images. Traditional neural networks utilize backpropagation techniques to adjust the random weights, which requires many resources while extracting a more significant number of features from a huge amount of data. These mechanisms also require more trainable parameters during the transformation of data from one layer to another. The proposed model implements the pre-trained model "RESNET152" (Residual Network), which is efficient for feature extraction and designs the last layer of the network as a "Tuned X-Gradient Boosting" ensemble algorithm for performing the binary classification of tomato leaves. RESNET can reduce computational resources because it implements residual blocks which fasten the learning rate by skipping a few connections in the network. The fine-tuned ensemble model helps the model identify the best parameters quickly. The learnable parameters are the essential elements of any ML model because they can easily identify the patterns associated with the different features. In the proposed model for feature extraction, pattern matching is the crucial step. Therefore, it is very necessary to tune the XGBOOST algorithm. Compared to the traditional approaches, the proposed model enhanced the accuracy performance in training and testing with 98.58% and 95.56%, correspondingly
Adaptive Optimized Discriminative Learning based Image Deblurring using Deep CNN
Authors: Sree P. Hema , Nagajyothi D. , Revathi K. , Rao B. Janardhana 
Published: September 2023

Image degradation plays a major problem in many image processing applications. Due to blurring, the quality of an image is degraded and there will be a reduction in bandwidth. Blur in an image is due to variations in atmospheric turbulence, focal length, camera settings, etc. Various types of blurs include Gaussian blur, Motion blur, Out-of-focus blur. The effect of noise along with blur further corrupts the captured image. Many techniques have evolved to deblur the degraded image. The leading approach to solve various degraded images are either based on discriminative learning models or on optimization models. Each method has its own advantages and disadvantages. Learning by discriminative methods is faster but restricted to a specific task whereas optimization models handle flexibly but consume more time. Integrating optimization models suitably by learning with discriminative manner results in effective image restoration. In this paper, a set of effective and fast Convolutional Neural Networks (CNNs) are employed to deblur the Gaussian, motion and out-of-focus blurred images that integrate with optimization models to further avoid noise effects. The proposed methods work more efficiently for applications with low-level vision.