Mobile ad-hoc networks (manets) are everywhere. They are the basis for many current technologies (including vanets, iot, etc.), and used in multiple domains (including military, disaster zones, etc.). For them to func...
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In healthcare domain, the demand for efficient diagnosis and hospital management systems has increased unprecedentedly. This research study proposes a novel Symptom Checker integrated with a Multidisease Hospital Mana...
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ISBN:
(数字)9798350375190
ISBN:
(纸本)9798350375206
In healthcare domain, the demand for efficient diagnosis and hospital management systems has increased unprecedentedly. This research study proposes a novel Symptom Checker integrated with a Multidisease Hospital Management System, leveraging state-of-the-art Machine Learning (ML) algorithms such as Random Forest Classifier and XGB Regression. Unlike existing systems, this advanced framework outperforms conventional methods by offering increased accuracy and agility in diagnosing a multitude of medical conditions. The proposed system operates as a comprehensive tool, integrating patient symptoms with ML algorithms to accurately identify the diseases. Through the analysis of symptoms given as input by patients, the Random Forest Classifier preforms well in classifying a broad spectrum of diseases, demonstrating high precision in analysing complex symptom patterns. Furthermore, the XGB Regression model enhances the system’s predictive capabilities, enabling the estimation of disease progression and facilitating proactive treatment strategies. Moreover, the proposed approach extends beyond symptom analysis. It encompasses a robust hospital management component that streamlines administrative tasks, optimizes resource allocation, and enhances patient care.
Mobile ad hoc networks (manets) are self-creating, self-configuring, self-healing, decentralized adaptive networks. The Optimized Link State Routing protocol (olsr) is one of four base routing protocols for use in ad ...
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In the era of ubiquitous data collection and analysis, preserving privacy while utilizing data, such as inner product evaluations, poses a significant challenge. One such method is inner product functional encryption ...
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ISBN:
(数字)9798331506940
ISBN:
(纸本)9798331506957
In the era of ubiquitous data collection and analysis, preserving privacy while utilizing data, such as inner product evaluations, poses a significant challenge. One such method is inner product functional encryption (IPFE), which enables the computation of inner products on encrypted vectors without exposing the vectors themselves. However, the computational intensity of IPFE decryption poses challenges, particularly for resource-limited client devices and high-dimensional vectors. To facilitate partial decryption, prior IPFE schemes require the client to derive a partial decryption key from an assigned secret key, incurring computation costs linear to the vector dimension. In this paper, we provide a framework for the challenges of IPFE, enabling efficient decryption outsourcing, maintaining data privacy, and facilitating key generation outsourcing in IPFE schemes. In the proposed framework, outsourced decryption using a partial decryption key generates a masked plaintext that only the client can unmask. Furthermore, the process of deriving the partial decryption key can be fully outsourced, thereby ensuring that the client’s computational burden remains constant.
Attention deficit hyperactivity disorder (ADHD) is one of the major psychiatric and neurodevelopment disorders that affects 11% of children worldwide. Moreover, the prevalence of ADHD has rapidly increased over time w...
Attention deficit hyperactivity disorder (ADHD) is one of the major psychiatric and neurodevelopment disorders that affects 11% of children worldwide. Moreover, the prevalence of ADHD has rapidly increased over time worldwide. According to DSM-V, three types of symptoms such as inattention, hyperactivity, and combined type (inattention with hyperactivity). So, it is necessary to use a simple, non-invasive, and automatic detection system for identifying children with ADHD at an early stage. The objective of this study was to propose a machine learning (ML)based ADHD-combine type (ADHD-CT) detection system from electroencephalogram (EEG) signals. EEG signals were recorded from nineteen ADHD-CT children and fourteen healthy children. We extracted five entropy-based features such as approximate-based entropy, Shanon-based entropy, permutation-based entropy, sample-based entropy, and singular value decomposition (SVD)-based entropy from each signal. The subset of the most informative and discriminative features was selected for ADHDCT using sequential forward floating selection (SFFS). Following that, support vector machine (SVM) was implemented with leave-one-out cross-validation for the identification of ADHD-CT children and assessed its performances based on classification accuracy. Our results illustrated that SVM with polynomial kernel provided 96.87% classification accuracy to discriminate children as ADHD-CT and healthy children. Our findings showed that our proposed system can be used to detect children with ADHD-CT.
Social media is one of the most predominantly used online platforms by individuals across the world. However, very few of these social media users are educated about the adverse effects of obliviously using social med...
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Steganography is the art and science of writing secret messages so that neither the sender nor the intended recipient knows there is a hidden message. Data hiding is the art of hiding data for various reasons, such as...
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Steganography is the art and science of writing secret messages so that neither the sender nor the intended recipient knows there is a hidden message. Data hiding is the art of hiding data for various reasons, such as keeping private data, secure, confidential data, etc. With increasing data exchange over a computer network, information security has become a significant issue. There are many methods used for data hiding, and steganography is a well-known technique. Steganography is the art of invisible contact and science. Steganography is the process through which the presence of a message can be kept secret. The objective of this paper is to hide data using the LSB (Least Significant Bit) technique into images that can be detected only by the specified user. We have developed a user-friendly GUI such that it can be used with the utmost ease. This paper is motivated to hide the message stated by the user in the dialog box given within the picture. The secret text is converted to the ciphertext to make it more stable. The sender selects the cover image, and it is used to generate the secured Stegno image, which is identical to the cover image. With the support of a private or public communication network, on the other hand, the stegno image can be saved and sent to the designated user, i.e., the recipient downloads the stegno image and can retrieve the secret text concealed in the stegno image using that same application. As for the watermarking, we have visible and invisible we have used the same LSB technique. In visible watermarking, text or image is embedded in the cover image, which can be noticed easily. As for invisible watermarking, some specific text is inserted into an image, and while retrieving it, it generates a QR code, which can be scanned to get the watermarked text. We used the three different types of cover images i.e. Gray, and RGB also estimated the performance metrics. SNR, MSE, and PSNR the three performance metrics are used, and fou
This paper presents a comprehensive framework for assessing the efficacy of Distributed Ledger technology (DLT) in IoT retail applications. The framework integrates five key algorithms: Data Validation Algorithm (DVA)...
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YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time...
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Despite the significance of music genre classification in audio identification, it remains under-explored within AI research. This tool is crucial for personalized music recommendations and similar music detection. We...
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