Computer Science & Information Technology Engineering

Brain Cancer Detection Using Advanced Deep Learning Algorithm

The disease needs to be treated cautiously because of the complexity of brain cancer. Since each patient’s brain tumor is unique in terms of dimension, shape, and localization, it is challenging to identify them in medical imaging. Magnetic Resonance Imaging (MRI) process is used to diagnose the brain tumor anatomy results in inaccurate detection of the tumor and it is very time consuming. Therefore, many classification techniques from Machine Learning (ML) and Deep Learning (DL) are utilized to rapidly recognize the tumor. This paper proposes segmenting and detecting the brain cancer using Deep Convolutional Neural Network (DCNN). An image is first pre processed using a median filter to minimize noise in the image.  Global thresholding is a simple image segmentation technique that uses a single threshold value to separate an image into distinct regions. After that, the feature extraction method of Histogram of Oriented Gradients (HOG) is employed to detect brain tumors with higher accuracy and lower computational complexity than other methods. This performance measure is applicable to quantifying the performance of the segmented tumor region. Additionally, DCNN classification algorithm has achieved high performance and accuracy. For the purpose of identifying the performance of the model, this proposed project produced a confusion matrix. With a classification accuracy of 91.23% and a precision of 90.39%, the experiment’s results demonstrated that the method is more successful than the current methods.

Bridge Crack Detection Using Multi-SVM Classifier

The Automatic crack detection system is a significant technology to detect crack in concrete images. Identification of the crack Intensity and severity is the main objective of the detection system. Detection of images containing unwanted noises, blurriness and interference is considerably difficult hence image pre processing is done using the adaptive filter by which the disturbances is removed. Images with non uniform light, poor contrast or sudden brightness changes is rectified in the segmentation process using the fuzzy c algorithm that uses clustering technique. Techniques like Gray scale processing do not provide accurate detection therefore Gray level co event matrices (GLCM) technique which extracts high level features needed in order to perform classification of images and Multi support vector machine (SVM) is used because they can handle multiple continuous and categorical variables. The proposed technique provides better outcomes for crack detection and treatment. The PYTHON software is utilized.

Enhanced Prediction of Diabetic Retinopathy Types Using Deep Convolutional Neural Networks

Diabetic retinopathy is an eye condition which leads to the loss of eye sight in the person who already has diabetics. There are several stages in detecting the diabetic retinopathy which includes manual medical methods or detected by automatic methods using image processing. In these cases, neural networks are used in detecting the defects caused in the retina of the diabetics. These neural network process as same as human brain, in this methodology Artificial Intelligence (AI) is implemented. In this methodology, the image is pre processed to improve the image clarity by Gaussian filtering method. The pre processed image undergoes segmentation using the Fuzzy C means (FCM) clustering method. By involving these methods, the complexity in detecting the diabetic retinopathy is reduced. The Deep Convolutional Neural Network (DCNN) method is implemented in which data is compared to find the defect. The pooling layer samples the image to speed up computation, and the fully connected layer provides the final prediction. In the Convolutional Neural Network, the data to the compared is already processed and stored. This paper provides excellent specificity and sensitivity for classifying images as having or without diabetic retinopathy. This paper is implemented using Python Google Colab software.

Reflection FSS-Based Millimetre Slot Antenna with Gain Enhancement for 5G Networks

In this paper a millimeter wave (mm wave) with rectangular shaped slot antenna is implemented at the operating frequency of 28 GHz to improve the efficiency of 5G networks using micro strip technique for feeding. The main objective of utilizing slot is to enhance the antenna performance in terms of gain, directivity, bandwidth, efficiency, radiated power and accepted power. Rectangular slot antenna of required frequency bandwidth is utilized in the proposed model. Additionally to enhance antenna gain Frequency Selective Surface (FSS) is deployed which is designed using the similar fundamental mode of antenna and located at particular distance to attain desired gain improvement with appropriate dimension of 25 x 25 x 5 𝑚𝑚3. By incorporating FSS into mm wave rectangular antenna ensures 6.7 dBi of gain improvement and S11 ≤ −10 dB from 26 GHz to 29.8 GHz of bandwidth enhancement with improved radiation efficiency which are obtained from tested and experimental data. These results attained using proposed antenna are subsequently better when compared to existing antenna system. Thus, the proposed antenna with high gain efficiency makes it more suitable for 5G wireless communication applications.

Mm-Wave Wideband MIMO Antenna for High Gain in 5G Generation

This paper analyses the designing of millimeter (MM) wave wideband with multiple input multiple output (MIMO) antenna to obtain high gain in 5G generation. The challenges of attaining better performance under varying frequency bands in MIMO antenna models are discussed and isolation methods developed for MIMO design are examined in this paper. The proposed design contains two circular patch array elements for two antennas each with a circle shaped slot to boost bandwidth. The patch antenna’s bandwidth is determined by using circle’s radius. A part of the ground plane is integrated into the antenna to achieve an operating frequency range from 24-26 GHz, at 28 GHz. To reduce mutual coupling between elements decoupling surface is used between antennas. To improve gain and to optimize bandwidth, ground plane utilizes Defected Ground Structure (DGS), partially grounded technique is used. The techniques used is compared based on S parameters and peak gain. MM wave wideband MIMO meets the desired band requirements with high gain in 5G generation.

Cloud Service Authentication Based On Advanced Encryption Standard (AES) For Ensure Privacy

Considering the possible consequences of illegal access, cloud service authentication privacy must be guaranteed. Negative actors might get access to private information without the right authentication procedures, which could result in monetary losses, harm to one’s reputation, and legal repercussions. This paper proposed using the advanced encryption standard (AES) algorithm for cloud service authentication to guarantee privacy and alleviate these problems. Providing a safe and effective authentication process that ensures user data privacy is the main goal of this framework. To encrypt user credentials and other authentication related data before sending it to the cloud service provider, the proposed framework makes use of the AES algorithm. With the use of an encryption key, the data is rendered unintelligible even in the event that it is intercepted. In order to guard against man in the middle attacks and eavesdropping, the authentication system also includes secure key exchange protocols and secure communication routes. Organizations may feel secure and in control of their data stored in the cloud by implementing this cloud service authentication architecture. It provides a strong barrier against breaches of privacy, illegal access, and data breaches. A workable and efficient way to guarantee privacy and safeguard sensitive data is to employ the AES algorithm for cloud service authentication. Through the use of this framework, businesses may take use of cloud computing’s advantages while retaining control over their data and lowering the danger of illegal access.

Implementation of A Deep Learning Algorithm for Fraud Detection Based on Credit Transactions

Online transactions using credit cards are being more reliable and efficient method followed by most of the users. In the same way, the fake transaction and scam occurring during the credit card transactions are increasing day by day. This scam not only steal user’s money but also misuse their personal details. Manual detection of these scam may be a tedious process, so this detection must be a deep learning method for the fastest and effective method of detection. This paper proposes a methodology of deep learning algorithm involving Multi Layer Perceptron (MLP) to prevent the users from fraud transaction. The data which is trained form a neural network and the test data is evaluated to define the classification, whether the data is fraud or verified data. This work involves data processing, data cleaning and data visualization of the data transaction. At the end of the process, the detected results undergo validation and model evaluation. Data visualization uses libraries from Matlab to learn the data, which is used in the data classification.

Enhancement of VANETS Security against Gray Hole Attack with ANFIS-GWO and LBK

Vehicular Ad Hoc Networks (VANETs) have emerged as a critical component of intelligent transportation systems, facilitating real time communication among vehicles and infrastructure. However, the open and dynamic nature of VANETs makes them vulnerable to various security threats, among which gray hole attacks pose significant challenges. In gray hole attacks, malicious nodes manipulate or selectively drop data packets, disrupting communication and compromising network integrity. In response to these challenges, this paper proposes an innovative approach that integrates the Adaptive Neuro Fuzzy Inference System (ANFIS) with Grey Wolf Optimization (GWO). The main objective of this research is to develop a robust ANFIS GWO framework capable of identifying and mitigating gray hole attacks in real time VANET scenarios. Through extensive simulations and experiments, the performance of proposed technique in terms of average energy consumed, throughput, Packet Delivery Ratio (PDR) and packet drop is examined. The results demonstrate that the ANFIS GWO approach not only improves the resilience of VANETs against gray hole attacks but also guarantees reliable and secure communication among vehicles and infrastructure.

MobileNet and Streamlit Applications for Classification and Prediction of MRI-Based Types of Brain Tumor

The rapid proliferation of abnormal cells within the cerebrum and its surrounding areas leads to the formation of tumor cells with intricate and challenging patterns. Consequently, relying solely on magnetic resonance imaging (MRI) for brain tumor detection presents complexities due to the need for precise identification amidst normal anatomical structures and potentially anomalous tissues. Recognizing the urgency of early and precise diagnosis in combating brain cancers, this paper proposes a methodology for classifying and predicting MRI based brain tumor types using MobileNet through a Streamlit application. Initially, the input image dataset undergoes preprocessing to enhance image quality, facilitated by a Gaussian filter. Subsequently, the FCM segmentation technique is employed to accurately delineate tumors’ size, location, and morphology. Finally, feature extraction and classification are performed utilizing the MobileNet framework, a deep learning method known for its accuracy and efficiency, particularly in medical image segmentation. Performance evaluation metrics such as precision, recall, and accuracy are computed, culminating in the calculation of the F score to assess the model’s effectiveness. This work leverages the Streamlit environment, a Python platform for developing web based interactive applications, to facilitate seamless user interaction. Ultimately, the predicted output images depicting various brain tumor types glioma, meningioma, pituitary, and absence of tumor are displayed within the Streamlit software interface. By offering a swift and precise means of disease classification, this approach aids healthcare professionals in promptly identifying tumor afflicted patients while streamlining computational overhead. The proposed classifier shows the highest specificity of 94.5 % and sensitivity of 97.6%.

Applications for Predicting Airfare Prices based on Machine Learning Techniques and the Flask Web App

The flight fare prediction model is meant to receive user inputs from the Flask web application, including departure and arrival cities, trip dates, and airline preferences. The flight price predictor estimates flight fares by comparing the previous prices of flights for the dates and destinations. This paper proposes an applications for predicting airfare prices based on machine learning techniques and the flask web app. This paper uses Machine Learning (ML) techniques, which provide a collection of data accurately and efficiently. Additionally, it is also used to forecast future requirements. The input dataset is initiated by a preprocessing technique, which helps clean the datasets, and the data exploration tries to explore the datasets. ML classification is the next step, which is conducted using the Gradient Boosting Algorithm (GBA). This technique is used in regression and classification tasks. Hyper-parameter tuning in gradient boosting is essential for the overall performance of the machine learning model. The last step in the Flask application is to connect to the database and display the flight price data to users. This paper provides excellent specificity and sensitivity for classifying flight prices. Further provides users with a better understanding of when to purchase tickets and how long they profitable. Overall, the proposed work is developed using the Python Jupyter software. The Specificity Comparison of GBA is 93.5% and Sensitivity Comparison of GBA is 92.5% respectively.