Context-aware scheduling in Fog computing: A survey, taxonomy, challenges and future directions

Skin cancer (melanoma and non-melanoma) is one of the most common cancer types and leads to hundreds of thousands of yearly deaths worldwide. It manifests itself through abnormal growth of skin cells. Early diagnosis drastically increases the chances of recovery. Moreover, it may render surgical, radiographic, or chemical therapies unnecessary or lessen their overall usage. Thus, healthcare costs can be reduced. The process of diagnosing skin cancer starts with dermoscopy, which inspects the general shape, size, and color characteristics of skin lesions, and suspected lesions undergo further sampling and lab tests for confirmation. Image-based diagnosis has undergone great advances recently due to the rise of deep learning artificial intelligence. The work in this paper examines the applicability of raw deep transfer learning in classifying images of skin lesions into seven possible categories. Using the HAM1000 dataset of dermoscopy images, a system that accepts these images as input without explicit feature extraction or preprocessing was developed using 13 deep transfer learning models. Extensive evaluation revealed the advantages and shortcomings of such a method. Although some cancer types were correctly classified with high accuracy, the imbalance of the dataset, the small number of images in some categories, and the large number of classes reduced the best overall accuracy to 82.9%.

https://www.mdpi.com/1424-8220/22/13/4963/pdf?version=1656924746

On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning

Sensor-cloud originates from extensive recent applications of wireless sensor networks and cloud computing. To draw a roadmap of the current research activities of the sensor-cloud community, we first investigate the state-of-the-art sensor-cloud literature reviews published in the recent five years, and discover that these surveys have primarily studied the sensor-cloud in specific aspects, namely, security-enabled solutions, efficient management mechanisms, and architectural challenges. Whilst the existing surveys have reviewed the sensor-cloud from various perspectives, they are inadequate for the three key issues that require urgent attention in the sensor-cloud: reliability, energy, and heterogeneity. To fill this gap, we perform a thorough survey by examining the origins of the sensor-cloud and providing an in-depth and comprehensive discussion of these three key challenges. We summarize initial designs of the new edge-based schemes to address these challenges and identify several open issues and promising future research directions.

https://dl.acm.org/doi/pdf/10.1145/3582270

Edge Computing and Sensor-Cloud: Overview, Solutions, and Directions

Corn is a mass-produced agricultural product that plays a major role in the food chain and many agricultural products in addition to biofuels. Furthermore, households in poor countries may depend on small-scale corn cultivation for their basic needs. However, corn crops are vulnerable to diseases, which greatly affects farming yields. Moreover, extreme weather conditions and unseasonable temperatures can accelerate the spread of diseases. The pervasiveness and ubiquity of technology have allowed for the deployment of technological innovations in many areas. Particularly, applications powered by artificial intelligence algorithms have established themselves in many disciplines relating to image, signal, and sound recognition. In this work, we target the application of deep transfer learning in the classification of three corn diseases (i.e., Cercospora leaf spot, common rust, and northern leaf blight) in addition to the healthy plants. Using corn leaf image as input and convolutional neural networks models, no preprocessing or explicit feature extraction was required. Transfer learning using well-established and well-designed deep learning models was performed and extensively evaluated using multiple scenarios for splitting the data. In addition, the experiments were repeated 10 times to account for variability in picking random choices. The four classes were discerned with a mean accuracy of 98.6%. This and the other performance metrics exhibit values that make it feasible to build and deploy applications that can aid farmers and plant pathologists to promptly and accurately perform disease identification and apply the correct remedies.

/pdf/classification-of-corn-diseases-from-leaf-images-using-deep-2cbhjs18.pdf

Classification of Corn Diseases from Leaf Images Using Deep Transfer Learning

The oil palm industry is vital for the Malaysian economy. However, it is threatened by the Ganoderma boninense fungus, which causes basal stem rot (BSR) disease. Foliar symptoms of the disease include the appearance of several unopened spears, flat crowns, and small crown size. The effect of this disease depends on the severity of the infection. Currently, the disease can be detected manually by analyzing the oil palm tree’s physical structure. Terrestrial laser scanning (TLS) is an active ranging method that uses laser light, which can directly represent the tree’s external structure. This study aimed to classify the healthiness levels of the BSR disease using a machine learning (ML) approach. A total of 80 oil palm trees with four different healthiness levels were pre-determined by the experts during data collection with 40 each for training and testing. The four healthiness levels are T0 (healthy), T1 (mildly infected), T2 (moderately infected), and T3 (severely infected), with 10 trees in each level. A terrestrial scanner was mounted at a height of 1 m, and each oil palm was scanned at four positions at a distance of 1.5 m around the tree. Five tree features were extracted from the TLS data: C200 (crown slice at 200 cm from the top), C850 (crown slice at 850 cm from the top), crown area (number of pixels inside the crown), frond angle, and frond number. C200 and C850 were obtained using the crown stratification method, while the other three features were obtained from the top-down image. The obtained features were then analyzed by principal component analysis (PCA) to reduce the dimensionality of the dataset and increase its interpretability while at the same time minimizing information loss. The results showed that the kernel naive Bayes (KNB) model developed using the input parameters of the principal components (PCs) 1 and 2 had the best performance among 90 other models with a multiple level accuracy of 85% and a Kappa coefficient of 0.80. Furthermore, the combination of the two highest PC variance with the most weighted to frond number, frond angle, crown area, and C200 significantly contributed to the classification success. The model also could classify healthy and mildly infected trees with 100% accuracy. Therefore, it can be concluded that the ML approach using TLS data can be used to predict early BSR infection with high accuracy.

Classification of Basal Stem Rot Disease in Oil Palm Plantations Using Terrestrial Laser Scanning Data and Machine Learning

Sensor Cloud has emerged as a rising technology in removing the barriers of Wireless Sensor Network. It increases the lifetime of the sensor network by storing all the sensed information into cloud server instead of saving in node’s memory. Yet sensor cloud has some important issues like latency, information classification and accuracy which still need an attention to improve overall performance of the sensor cloud system. Fog computing is a problem solver for sensor cloud in removing latency and carrying out computational tasks in faster way. Fog computing is acting as a middleware between physical network and cloud, located at the edge of the network for fast computation and quick response. The proposed work utilizes the functionalities of sensor cloud and fog computing to classify and save the information in better way along with minimizing the latency issue. The Random forest classifier along with Genetic Algorithm is utilized for classifying the information and saving only required amount of information into cloud server with priority. Agent paradigm is included to save the energy of the physical sensor nodes and also to perform quick analyzing and classification of information at the fog server. The result shows that proposed work is working far better than conventional methods in terms of classification accuracy, latency, packet delivery ratio, energy consumption and network lifetime.

Fog computing based information classification in sensor cloud-agent approach

. The capability of deep learning on chest radiographs is diagnosed in this dissertation, and an image classifier based on the COVID-Net has been provided to categorize chest X-Ray pictures. Information augmentation was recommended to extend COVID-19 reports 17 times in the case of a little amount of COVID-19 metadata. The system aims to improve transfer learning and model integration by categorizing chest X-ray pictures into three categories: normal, COVID-19, and infectious pneumonia. Choose the models ResNet-101 and ResNet-152 with best outcomes for fusion based on accuracy and loss value, and apathetically enhance its weight percentage during the training procedure. The system can obtain 97 percent accuracy in chest X-Ray pictures post training. In the diagnosing of lung nodules, this technique offers a higher accuracy than radiologist. It can assist radiologists enhance their efficiency levels and diagnostic performance as an additional diagnostic technique.

Detection of Covid-19 from Chest X-Ray Images

In recent years, sensor cloud showing its impact in the networking field and also it is being used for most of the applications. The major concern for sensor cloud is its lifetime because yet nodes are operated with limited energy, bandwidth and exhaust very soon affecting the whole network. To make reliable communication and optimizing the whole system for improving network lifetime, load balancing concept is included in sensor cloud. The fuzzy optimizer is responsible for finding the deserving cluster head and precise next hop node for information transmission. Fuzzy method is conducted at the sensor cloud server and the information is shared to sink node for physical network setup. Based on the importance of the information coming from physical network, it can be saved into priority or non priority servers using information classification technique leading to load balancing at server also. Agents are triggered into the network for information collection along with truthful information delivery ratio in a minimum time. The proposed method is compared with existing popular methods to check the load balancing capacity, and it is found that the proposed work is far better than existing methods with respect to response time, delay, energy consumption, minimum packets transmission and network lifetime.

Fuzzy based Load Balancing in Sensor Cloud: Multi-Agent Approach

Mobile Ad-Hoc Networks (MANET’s) are the one of the popular and ongoing research where nodes exchange their information without forming predefined network (infrastructure less based network), so maintaining data secrecy and data confidentiality is an important and challenging task. In order to secure data, one has to deploy security technique either at the transmitter side or at the medium side (Channel) so that attacker or third party should not access the information. In this paper we are surveying some of the efficient security techniques available in MANETs considering both possible ways i.e. security either can be employed at the transmitter side or between links. Survey says that SMT technique holds good in all aspects where SPREAD and SDMP provides security but increases system overhead.

/pdf/efficient-security-approaches-in-mobile-ad-hoc-networks-a-2j8i4dhu18.pdf

Efficient security approaches in mobile ad-hoc networks: a survey

Fog computing is a recent trend in networking where it is acting as a middleware between networking end devices / sensors and cloud computing. The fog computing is a solution to Iatency problem in the networks,which are connected to cloud computing. The fog computing computes, networks, compresses and provides the security to the information so that only essential information is stored in the cloud server and it can beeasilyaccessible to users. In this paper we are proposing a new concept inside the fogging. Instead of considering the huge amount of sensory data into fog for computation, only the required amount of information is called for computation and further process by the inclusion of context aware information classifier. The proposal further enhances the features of fog computing along with further minimizing the Iatency issue by considering K-NN & ID3 classifiers and making effective connectivity between sensors and clouds server.

Context Aware Information Classification in Fog Computing

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Prashant Sangulagi

Author Tools

Chat about Author