In the modern environment, it is quite difficult to detect Heart Disease (HD) through early stage symptoms. Heart disease is the cause of mortality if the diagnosis is delayed including death occurs to prevent these issues. This paper proposes an innovative approach for the detection of heart disease from HD dataset, incorporating advanced techniques. Initially, data cleaning and data normalization is applied to the HD to eliminate or to find the duplicate values.  Next, the images are segmented using K means clustering, allowing for the separation of relevant regions associated with HD. Features are then optimized from the segmented HD using the Dung Beetle Optimization (DBO) algorithm, capturing optimized values for further analysis. Finally, a Bidirectional Long Short Term Memory (BiLSTM) framework is proposed as a hybrid model to enhance the classification of HD images, leveraging both forward and backward temporal information to overcome challenges in disease analysis. The assessment of proposed work using python software reveals that the proposed framework with BiLSTM classifier ranks with improved accuracy of 91.56% when compared to the other techniques.

Tsunami detection in the coastal area is a major problem and a big impact on the environment, therefore early detection and training for tsunami need to be carried out to reduce the impact of casualties and losses incurred. Machine learning techniques, with their ability to identify patterns and anomalies, offer an approach for tsunami detection. This paper introduces a novel tsunami detection model combining the Chicken Swarm Optimization (CSO) Algorithm and Artificial Neural Network (ANN) classifier. Using the data pre processing technique such as data cleaning and data visualization the input dataset is processed and featured using speedup data transformation the raw data is converted in to certain data using feature engineering. This hybrid approach effectively analyses the detection of tsunami by leveraging the strengths of both methods. The model is trained and evaluated using the earthquake dataset, achieving a commendable accuracy of 92%. The software used here is python. The proposed model demonstrates superior results compared to existing techniques, offering an efficient solution for environment.

Credit card fraud has played a major issue for both cardholders and the issuing authorities as a notable financial challenge. To address this issue here involves modified Convolutional Neural Network (CNN) to identify fraudulent activities. The proposed methodology involves data pre processing, data visualization, validation and evaluation. Here the data is preprocessed by normalization to improve the quality of data performance and training stability of deep neural network. One hot encoding is used to convert categorical variables into binary format and is highly essential for data processing which in turn improve the prediction and classification accuracy of the method. Furthermore by using CNN with a modified Quantum Vortex Search Algorithm (MQVSA) is developed to increase the accuracy and effectiveness of the credit card fraud detection. The MQVSA improves CNN capacity of classification and to identify complex pattern indicative of fraud and model parameters. Thus the proposed method used in this work is performed in the python software and thus the classifier achieve the remarkable precision of 98% and the sensitivity value of 97% and effectively predicting the credit card fraud by evaluation and validation.

Sophisticated security measures are required to safeguard vital infrastructure and sensitive data due to the ever changing nature of cyber threats. Integrating deep learning techniques into cybersecurity is the subject of this study, which aims to improve threat detection, response, and prevention using novel methodologies. We compare the performance of different deep learning architectures, such as RNNs and CNNs, in detecting patterns that can indicate cyberattacks, using tools like malware detectors and intrusion detection systems (IDS). By utilizing massive datasets and real time analytics, these models outperform conventional methods in terms of speed and accuracy. Moreover, we offer real world examples of deep learning in action, including automated threat intelligence analysis and anomaly detection in network data. Strong training processes and model optimization are necessary, as the results show that cybersecurity frameworks must constantly learn and adapt. Also covered are issues with data privacy and the need for computational resources, two of the many obstacles to using deep learning solutions in cybersecurity. By expanding our knowledge of how deep learning can strengthen cybersecurity measures, this research ultimately leads to more resilient systems that can withstand emerging cyber threats.

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.

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.

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.

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.

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.

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.