Simple Object Access Protocol と,その応用としてのソフトウェアの組み合わせについて (渡邉昭夫教授退任記念号)

Sensors are distributed across the globe leading to an avalanche of data about our environment. It is possible today to utilize networks of sensors to detect and identify a multitude of observations, from simple phenomena to complex events and situations. The lack of integration and communication between these networks, however, often isolates important data streams and intensifies the existing problem of too much data and not enough knowledge. With a view to addressing this problem, the semantic sensor Web (SSW) proposes that sensor data be annotated with semantic metadata that will both increase interoperability and provide contextual information essential for situational knowledge.

Semantic Sensor Web

During data transmission, a decentralised Mobile Ad Hoc Network (MANET) might result in high Energy Consumption (EC) and a short Network Lifetime (NLife). To address these difficulties, an on-demand Power and Load-Aware multipath node-disjoint source routing is presented based on the Enhanced Opportunistic Routing (PLAEOR) protocol. This unique protocol aims at using power, load, and latency to manage routing costs depending on control packet flooding from the destination node. However, the exchange of control packets from the target to all nodes may impact network efficiency. As a result, the PLAEOR is designed with a Multichannel Cooperative Neighbor Discovery (MCND) protocol to locate the nearby cooperative nodes for each node in the routing path during control packet transmission. Furthermore, when the packet rate of CBR is 20 packets/sec, the simulated results show that the PLAEOR-MCND achieves 120 sec of NLife and 20 J of EC than the state-of-the-art protocols.

Delay-Driven Opportunistic Routing with Multichannel Cooperative Neighbor Discovery for Industry 4.0 Wireless Networks Based on Power and Load Awareness

Lung cancer is the cellular fission of abnormal cells inside the lungs that leads to 72% of total deaths worldwide. Lung cancer are also recognized to be one of the leading causes of mortality, with a chance of survival of only 19%. Tumors can be diagnosed using a variety of procedures, including X-rays, CT scans, biopsies, and PET-CT scans. From the above techniques, Computer Tomography (CT) scan technique is considered to be one of the most powerful tools for an early diagnosis of lung cancers. Recently, machine and deep learning algorithms have picked up peak energy, and this aids in building a strong diagnosis and prediction system using CT scan images. But achieving the best performances in diagnosis still remains on the darker side of the research. To solve this problem, this paper proposes novel saliency-based capsule networks for better segmentation and employs the optimized pre-trained transfer learning for the better prediction of lung cancers from the input CT images. The integration of capsule-based saliency segmentation leads to the reduction and eventually reduces the risk of computational complexity and overfitting problem. Additionally, hyperparameters of pretrained networks are tuned by the whale optimization algorithm to improve the prediction accuracy by sacrificing the complexity. The extensive experimentation carried out using the LUNA-16 and LIDC Lung Image datasets and various performance metrics such as accuracy, precision, recall, specificity, and F1-score are evaluated and analyzed. Experimental results demonstrate that the proposed framework has achieved the peak performance of 98.5% accuracy, 99.0% precision, 98.8% recall, and 99.1% F1-score and outperformed the DenseNet, AlexNet, Resnets-50, Resnets-100, VGG-16, and Inception models.

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Early Prediction of Lung Cancers Using Deep Saliency Capsule and Pre-Trained Deep Learning Frameworks

A deep learning based convolutional neural network model with VGG16 feature extractor for the detection of Alzheimer Disease using MRI scans

Elderly people are the assets of the country and the government can ensure their peaceful and healthier life. Life expectancy of individuals has expanded with technological advancements and survey tells that the elderly population will become double in the year 2030. The noninfectious cognitive dysfunction is the most important risk factor among elderly people due to a decline in their physiological function. Alzheimer, Vascular Dementia, and Dementia are the key reasons for cognitive inabilities. These diseases require manual assistance, which is difficult to provide in this fast-growing world. Prevention and early detection are the wise solution for the above diseases. Diabetes and hypertension are considered as main risk factors allied with Alzheimer's disease. Our proposed work applies a two-stage classification technique to improve prediction accuracy. In the first stage, we train a Support vector machine and a Random Forest algorithm to analyze the influence of diabetes and high blood pressure on cognitive decline. In the second stage, the cognitive function of the person with the possibility of Dementia is assessed using the neuropsychological test called Cognitive Ability Test (CAT). Multinomial Logistic Regression algorithm is applied to CAT results to predict the possibility of cognitive decline in their postlife. We classified the risk factor using the operational definitions: “No Alzheimer’s,” “Uncertain Alzheimer’s,” and “Definite Alzheimer’s”. SVM of stage 1 classifier predicts with an accuracy of 0.86 and Random Forest with an accuracy of 0.71. Multinomial Logistic algorithm of stage 2 classifier accuracy is 0.89. The proposed work enables early prediction of a person at risk of Alzheimer's Disease using clinical data.

Early Detection of Cognitive Decline Using Machine Learning Algorithm and Cognitive Ability Test

Agriculture census information is a leading source of a country's development. Such information is used by many who provide services to farmers and rural communities. The human interaction system provides a move from entity and object centric processing to relationship and event centric processing. The computer is interacted efficiently with the human for giving the solution to their problems. The integrated system gives the ability to extract, represent and reason about a variety of relationship as well as providing integral support. The proposed system is called farmer helping system which integrates relevant web services like soil information, plant disease information, and plant information and also contains pesticides and fungicides information. This farmer helping system gives the appropriate solution for farmers. The farmer helping system analyses the message from the user, contacts appropriate resources, and return actionable information, while requiring minimal involvement or technology consciousness from the user. The semantically annotated data is used for integration, search, analysis, discovery, question answering and situational awareness for making the user system efficient. This system can help for agricultural development planning and formulate of agricultural policies.

Design of ontology based ubiquitous web for agriculture — A farmer helping system

The human interaction system provides a move from entity and object centric processing to relationship and event centric processing. The computer is interacted efficiently with the human for giving the solution to their problems. To provide the service based on the human requirements by integrating relevant web services. The integrated system gives the ability to extract, represent and reason about a variety of relationship as well as providing integral support. The proposed system is called farmer helping system which integrates relevant web services like soil information, plant information and weather information and pesticides and fungicides information. This farmer helping system gives the appropriate solution for the farmers. To provide the meaningful representation ontology is used. The integration of the services to increase the interoperability in the heterogeneous environment. The farmer helping system analyses the message from the user, contacts appropriate resources, and return actionable information, while requiring minimal involvement or technology consciousness from the user. The semantically annotated data is then used for integration, search, analysis, discovery, question answering and situational awareness for making the user system efficient.

Integration of semantics, sensors and services on the ubiquitous web

Weather prediction has been a delicate task in the meteorological department since times due to varied reason similar as the drastically unpredicted behaviour of climate. These predictions are important because they impact diurnal life and also affect the frugality of a state or indeed a nation. Our model uses machine literacy algorithm to predict the weather. Our model is intended to provide accurate weather prediction. Long-term climatic change on Earth and the goods it’ll have on current and unborn generations’ lives are both unknown.

Weather Prediction using linear regression model

The crime rate is frequently increasing day by day in our society, also crime goes unreported, because people don’t have enough substantiation to help police with the disquisition. Also police didn't know the correct statistical report of once times of crime. Crime analysis and forestallment is a technical way of finding and analysing similar pattern occurred in crime. By adding the arrival of motorized systems, available data judgement can help the judicialsystem tofasten the process of recent an emergent crimes. With the conception of data mining and machine learning, the proposed system can prioritize preliminarily unknown, known from an unshaped data. By using the crime analysis technic, the levels of the crime are reduced and thus leads the Country’s reputation and growth. We can also reduce the cost of investment in the crime investigation. Our Moto of this paper is to mitigate the crime rate using Machine Learning algorithms.

R. Kaladevi

Papers

Early Detection of Cognitive Decline Using Machine Learning Algorithm and Cognitive Ability Test

Design of ontology based ubiquitous web for agriculture — A farmer helping system

Integration of semantics, sensors and services on the ubiquitous web

Weather Prediction using linear regression model

Crime Analysis Using Linear Regression