A full-duplex 40/40Gbps orthogonal frequency division multiplexing (OFDM) basics free space optics (FSO)-fiber system is presented. The performance is measured under different turbulences effects with fiber impairment...
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Various kinds of Information retrieval or processing task can be difficult basis on how the information is viewed or represented. Representation learning is a technique that allows a system to discover the representat...
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Down syndrome(DS) is a common chromosomal abnormality that affects about 1 in 800 live births. It is a genetic condition that occurs due to the presence of an extra copy of chromosome 21. Early detection of Down syndr...
Down syndrome(DS) is a common chromosomal abnormality that affects about 1 in 800 live births. It is a genetic condition that occurs due to the presence of an extra copy of chromosome 21. Early detection of Down syndrome during pregnancy is crucial for appropriate prenatal care and planning. Ultrasound imaging is a widely used method for detecting fetal abnormalities, including Down syndrome. In recent years, deep Convolutional Neural Networks (CNNs) have shown great promise in analyzing medical images for disease diagnosis. In this study, we propose a deep CNN-based approach for Down syndrome prediction from ultrasound images. Our approach consists of a pre-processing step for image normalization and a CNN-based model for feature extraction and classification. We trained and evaluated our model on a publicly available dataset of ultrasound images with both normal and Down syndrome cases. Our experimental results show that our proposed CNN-based approach achieved an accuracy of 98.7% for Down syndrome prediction, outperforming traditional machine learning algorithms and other deep learning models. Our proposed approach has the potential to assist clinicians in the early detection of Down syndrome and improving prenatal care for affected individuals.
IoT devices have become an increasingly accessible target for evasive attacks, such as botnets, due to insecure network services, deprecated software components, unencrypted data communication, and other vulnerabiliti...
IoT devices have become an increasingly accessible target for evasive attacks, such as botnets, due to insecure network services, deprecated software components, unencrypted data communication, and other vulnerabilities. To address these security concerns, our work makes several significant contributions toward curating datasets and designing and developing a robust and effective Host-Based Intrusion Detection algorithm (HIDS) for IoT devices. The proposed algorithm leverages memory-based fingerprints to train a convolutional neural network (CNN) model. Our approach is based on the premise that despite the heterogeneity of IoT devices, the functionality of each IoT device is often unique and remains relatively constant throughout its lifespan. Thus, to develop an effective IDS algorithm based on anomaly detection, we encode the dynamic IoT device memory into sound wave signals, extract discriminable features such as MFCCs and Chroma features, and pass these deterministic fingerprints as feature vectors to a device-specific CNN model. When trained using three different testbed datasets, our model achieved 100% accuracy for known and anomaly memory instances. Additionally, the evaluation of our features and feature engineering process showed that our model, which is trained with the features from memory-encoded signals, is more reliable and robust than those algorithms that leverage raw memory bytes as feature vectors.
Current interactions of network traffic through cloud data centers have become an important process of network services. Precise and real-time detection and prediction of network traffic can assist system operators in...
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This contribution describes new useful geometric transformations using the tensor product. The geometric transformations are used widely in many applications, especially in CAD/CAM systems, systems for Civil Engineeri...
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This paper tackles a joint optimization problem in uplink sparse code multiple access (SCMA) networks to enhance network fairness, i.e., Jain's fairness index (JFI). We propose a novel game-theoretic approach, the...
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data security is a major issue for computer users these days. So, we conceal the textual data using steganography, the art and science of concealing information. In this case, we conceal both the message’s content an...
data security is a major issue for computer users these days. So, we conceal the textual data using steganography, the art and science of concealing information. In this case, we conceal both the message’s content and the fact that it is being sent. In order to make it challenging to Figure out that a hidden message is present in a specific film, this technique involves concealing secret data within a conventional, non-secret file. At its destination, the secret data is subsequently removed. Several uses of steganographic technology have distinct requirements. Here, we suggest a method for concealing data, including reverse data hiding for data extraction. The textual data will be secured in different frames of the video and audio using the Least Significant Bit (LSB) and related algorithms. We can conclude that the original data will never change the characteristics of video or audio even after applying steganography techniques.
An earnings announcement report (EAR) contains the latest information about a company's financial situation and operating performance. Short-term stock price reacts strongly to such information. In this paper, to ...
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In this work, using first principles within the framework of Density-functional theory, we have explored the structural, electronic, and optical properties of two-dimensional edge passivated pristine GaN nanostructure...
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