In cloud forensics, scientific concepts, methods, and approaches are used to find, collect, retain, evaluate, and report digital data in order to reorder events. For everyone involved, including cloud service provider...
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ISBN:
(纸本)9781665476560
In cloud forensics, scientific concepts, methods, and approaches are used to find, collect, retain, evaluate, and report digital data in order to reorder events. For everyone involved, including cloud service providers (CSPs), cloud consumers, researchers, and forensic practitioners, cloud forensics is getting increasingly complex. If a criminal want to conceal their dirty laundry, they can use cloud storage to conceal sexual photographs of children, documents related to terrorist groups, etc. To investigate crimes involving the cloud, forensic investigations must be undertaken in the cloud. While attempting to collect evidence, forensic investigators face significant obstacles posed by the cloud's dynamic and dispersed nature. In this paper, Cloud-based forensics professionals are only able to conduct investigations if they have access to the proper tools and techniques for retrieving relevant digital evidence from a device is discussed. It further presents the challenges associated with cloud based forensics and applications to move forward the research in current area.
Medical professionals use Anomaly Detection(AD) to identify patients with potential health problems in the heart rate data (HRD), an essential metric related to cardiovascular health. In this research, the most effect...
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Supervised discretisation is widely considered as far more advantageous form of transformation than unsupervised processing of attributes. When evaluating candidate cut-points for splitting the ranges of values of dis...
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A chronic brain ailment known as Parkinson’s disease (PD) affects millions of people around the globe. The breakdown or death of brain cells responsible for producing dopamine, a neurotransmitter that regulates movem...
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ISBN:
(数字)9798350388916
ISBN:
(纸本)9798350388923
A chronic brain ailment known as Parkinson’s disease (PD) affects millions of people around the globe. The breakdown or death of brain cells responsible for producing dopamine, a neurotransmitter that regulates movement, causes this condition. Issues with mobility, equilibrium, and posture are brought on by PD, which develops as a result of this. To lessen the disease’s course and boost patients’ quality of life, early diagnosis is essential. A cosine annealing scheduler and deep transfer learning are presented in this study as a method for handwriting-based prediction. It uses handwriting samples from people with and without Parkinson’s disease, found in the NIATS collection. With the use of spiral sketching and CNN, this research hopes to provide a PD detection method. The main concept is to determine whether a person is healthy or has Parkinson’s disease based on their spiral drawings. Spiral drawings made by individuals in good health resemble conventional spiral forms. People with Parkinson’s disease have sluggish movement and poor hand-brain coordination, therefore their spiral drawings seem deformed because they stray greatly from the correct curve. This research presents the results of using a Convolutional Neural Network to diagnose Parkinson’s disease; the classification accuracy achieved was 83.6%.
The categorization of text is an essential and pivotal step in the process of natural language understanding and processing. There are a lot of different approaches to solving the problem of classifying text, but very...
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ISBN:
(数字)9798350388916
ISBN:
(纸本)9798350388923
The categorization of text is an essential and pivotal step in the process of natural language understanding and processing. There are a lot of different approaches to solving the problem of classifying text, but very few of them leverage the semantic integration of several views to boost the classification performance. A dual-DL attention network model, referred to as DDL, is proposed in the study. In order to address the knowledge gap, this paradigm employs semantic complementarity. For more precise extraction of textual logical semantics, DDLfirst employs transductive learning and graph structure. The next step is to use logical semantics in the attention fusion layer (Channel) to train other semantics jointly, which will gradually fix the predictions produced using unlabeled test data. Important for upcoming text mining jobs, experiments show that DDL can achieve better classification across a variety of text classification datasets.
The aim of this research is to do a CFD investigation on the K-type propeller for enumerating the various performance factors. This paper focuses on the design of rotor blades with the 3D pro-E software and analyzes a...
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In the recent years, the categorization of text documents into predefined classifications has perceived a growing interest due to the growing of documents in digital form and needs to organize them. Text categorizatio...
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In the recent years, the categorization of text documents into predefined classifications has perceived a growing interest due to the growing of documents in digital form and needs to organize them. Text categorization is one of the extensively used for natural language processing (NLP) applications have achieved using machine learning algorithms. Text classification is a challenging researcher to find the best suitable structure and technique. Classification process done using manual and automatic classification. This research paper covers the preprocessing, feature extraction, different algorithms and techniques for text classification and finally evaluates the performance metrics for assessment.
By recording a wide range of spectral bands across the electromagnetic spectrum, hyperspectral imagery (HSI) delivers extensive information. Due to the complicated interaction between spectral and spatial properties, ...
By recording a wide range of spectral bands across the electromagnetic spectrum, hyperspectral imagery (HSI) delivers extensive information. Due to the complicated interaction between spectral and spatial properties, extracting meaningful geographical information from HSI is difficult. This article uses Spectral-Spatial Networks to detect geographical objects in hyperspectral data. The proposed deep learning system blends spectral and spatial information to improve object discrimination in complicated scenarios. SSN uses convolutional neural networks (CNNs) and spectral attention mechanisms to adaptively weigh spectral bands and capture spatial correlations. A large dataset of annotated hyperspectral photography with ground truth object identification trains the SSN. The collection includes different lighting, topography, and item scales. The object detection task is fine-tuned after pre-training on a large HSI dataset. Experimental results on benchmark hyperspectral datasets show that the SSN outperforms state-of-the-art approaches. Precision, recall, and F1-score for numerous object classes reveal that the suggested technique can distinguish objects in complicated and congested situations. The suggested SSN framework is also resilient to illumination, climatic conditions, and sensor properties, making it suitable for real-world applications. The SSN architecture's scalability is further examined, showing its suitability for heterogeneous HSI datasets with different spectral resolutions.
The death rate from skin cancer has radically grown due to a lack of understanding about the indications and prevention measures. It is one of the most lethal types of cancers, and the mortality rate due to skin cance...
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ISBN:
(数字)9798331518394
ISBN:
(纸本)9798331518400
The death rate from skin cancer has radically grown due to a lack of understanding about the indications and prevention measures. It is one of the most lethal types of cancers, and the mortality rate due to skin cancer has risen sharply. Cancer must be diagnosed as early as possible to halt its growth and prevent metastasis. Even though there are numerous varieties of skin cancer, melanoma, basal cell carcinoma, and squamous cell carcinoma are considered the three most dangerous types of skin cancer. This research, using the CNN image-processing technology and machine learning, tries to detect and classify into the many kinds of skin cancer. Artificial intelligence (AI) based algorithms for automated skin cancer classification have consistently outperformed human professionals in several clinical studies, laying the groundwork for a future in which AI will be widely used.
This research introduces an innovative approach to water conservation and personalized consumption management by combining Internet of Things (IoT) sensors with Deep Reinforcement Learning (DRL) methods. The suggested...
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ISBN:
(数字)9798350379297
ISBN:
(纸本)9798350379303
This research introduces an innovative approach to water conservation and personalized consumption management by combining Internet of Things (IoT) sensors with Deep Reinforcement Learning (DRL) methods. The suggested approach aims to give people more control over their water use in response to water shortages and the need for sustainable resource management. It installs IoT sensors on taps to track water use in real time. A DRL framework is fed data that these sensors have collected. The DRL algorithm develops individual consumption profiles for each user by factoring in past water use, environmental conditions, and personal tastes. Using this unique profile, water efficiency may be improved instantly. Users may easily enter their water conservation choices, budgets, and the system's user interface. Adjusting the water's flow rate by the user profiles learned by the DRL actively regulates water use while keeping people happy. The results of the experiments reveal that our method effectively reduces both water use and waste. Integrating IoT sensors with DRL methods provides a flexible and scalable answer for smart water management that economically benefits the environment and consumers.
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