Image Mining for Flower Classification by Genetic Association Rule Mining Using GLCM features

Image mining is concerned with knowledge discovery in image databases. It is the extension of data mining algorithms to image processing domain. Image mining plays a vital role in extracting useful information from images. In computer aided plant identification and classification system the image mining will take a crucial role for the flower classification. The content image based on the low-level features such as color and textures are used to flower image classification. A flower image is segmented using a histogram threshold based method. The data set has different flower species with similar appearance (small inter class variations) across different classes and varying appearance (large intra class variations) within a class. Also the images of flowers are of different pose with cluttered background under varying lighting conditions and climatic conditions. The flower images were collected from World Wide Web in addition to the photographs taken up in a natural scene. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM). This paper introduces multi dimensional genetic association rule mining for classification of flowers effectively. The image Data mining approach has four major steps: Preprocessing, Feature Extraction, Preparation of Transactional database and multi dimensional genetic association rule mining and classification. The purpose of our experiments is to explore the feasibility of data mining approach. Results will show that there is promise in image mining based on multi dimensional genetic association rule mining. It is well known that data mining techniques are more suitable to larger databases than the one used for these preliminary tests. Computer-aided method using association rule could assist people and improve the accuracy of flower identification. In particular, a Computer aided method based on association rules becomes more accurate with a larger dataset .Experimental results show that this new method can quickly and effectively mine potential association rules

Identification of novel components of Trypanosoma brucei editosomes

The editosome is a multiprotein complex that catalyzes the insertion and deletion of uridylates that occurs during RNA editing in trypanosomatids. We report the identification of nine novel editosome proteins in Trypanosoma brucei. They were identified by mass spectrometric analysis of functional editosomes that were purified by serial ion exchange/gel permeation chromatography, immunoaffinity chromatography specific to the TbMP63 editosome protein, or tandem affinity purification based on a tagged RNA editing ligase. The newly identified proteins have ribonuclease and/or RNA binding motifs suggesting nuclease function for at least some of these. Five of the proteins are interrelated, as are two others, and one is related to four previously identified editosome proteins. The implications of these findings are discussed

Deep Meta Q-Learning based Multi-Task Offloading in Edge-Cloud Systems

Resource-Constrained Edge Devices Can Not Efficiently Handle the Explosive Growth of Mobile Data and the Increasing Computational Demand of Modern-Day User Applications. Task Offloading Allows the Migration of Complex Tasks from User Devices to the Remote Edge-Cloud Servers Thereby Reducing their Computational Burden and Energy Consumption While Also Improving the Efficiency of Task Processing. However, Obtaining the Optimal Offloading Strategy in a Multi-Task Offloading Decision-Making Process is an NP-Hard Problem. Existing Deep Learning Techniques with Slow Learning Rates and Weak Adaptability Are Not Suitable for Dynamic Multi-User Scenarios. in This Article, We Propose a Novel Deep Meta-Reinforcement Learning-Based Approach to the Multi-Task Offloading Problem using a Combination of First-Order Meta-Learning and Deep Q-Learning Methods. We Establish the Meta-Generalization Bounds for the Proposed Algorithm and Demonstrate that It Can Reduce the Time and Energy Consumption of IoT Applications by Up to 15%. through Rigorous Simulations, We Show that Our Method Achieves Near-Optimal Offloading Solutions While Also Being Able to Adapt to Dynamic Edge-Cloud Environments

Tail-to-tail carbon-carbon bond coupling of acetylides on chalcogen-bridged Fe/W mixed-metal clusters

On thermolysis of a toluene solution containing [Fe3(CO)9(μ3-E)2] (E = S 1a, Se 16 or Te 1c) and [W(η5-C5Me5)2(CO)3(C≡CPh)]2 the new clusters [W2Fe3(η5-C5Me5)2(CO)6(μ3-E)2{μ4-CC(Ph)C(Ph)C}] (E = S 3, Se 4 or Te 5) were isolated. Compounds 3-5 were characterised by IR and 1H, 13C, 77Se and 125Te NMR spectroscopy. The crystal structure of 3 was elucidated by X-ray diffraction methods. It shows a novel tail-to-tail coupling of substituted acetylides on a sulfur-bridged mixed-metal Fe-W cluster

Low Cost, Efficient Output- Only Infrastructure Damage Detection with Wireless Sensor Networks

Sensor network comprises of sensors and actuators with universally useful processing components to agreeably screen physical or ecological conditions, for example, temperature, pressure, and so on. Wireless Sensor Networks are particularly portrayed by properties like the constrained power they can reap or store, dynamic network topology, expansive size of the arrangement. Sensor networks have an enormous application in fields which incorporates territory observing, object tracking, fire detection, landslide recognition and activity observing. Given the network topology, directing conventions in sensor networks can be named at based steering, various levelled based directing and area-based directing. Low Energy Adaptive Clustering Hierarchy (LEACH) is a vitality productive various levelled based steering convention. Our prime spotlight was on the examination of LEACH given specific parameters like network lifetime, soundness period, and so forth and furthermore the impact of particular sending assault and level of heterogeneity on LEACH convention

Hybrid Image Mining Methods to Classify the Abnormality in Complete Field Image Mammograms Based on Normal Regions

Breast Cancer now becomes a common disease among woman in developing as well as developed countries. Many noninvasive methodologies have been used to detect breast cancer. Computer Aided diagnosis through, Mammography is a widely used as a screening tool and is the gold standard for the early detection of breast cancer. The classification of breast masses into the benign and malignant categories is an important problem in the area of computer-aided diagnosis of breast cancer. We present a new method for complete total image of mammogram analysis. A mammogram is analyzed region by region and is classified as normal or abnormal. We present a hybrid technique for extracting features that can be used to distinguish normal and abnormal regions of a mammogram. We describe our classifier technique that uses a unique re-classification method to boost the classification performance. Our proposed hybrid technique comprises decision tree followed by association rule miner shows most proficient and promising performance with high classification rate compared to many other classifiers. We have tested this technique on a set of ground-truth complete total image of mammograms and the result was quite effective

Stabilization of Black Cotton Soil using Lime, Coir Fiber & Rice Husk

Because of their low bearing capacity, the expansive black cotton soils' high swelling and shrinking characteristics have posed numerous challenges to construction projects. When subjected to varying levels of moisture, black cotton soil expands and contracts rapidly. As a result, stabilising the soil is necessary to address these issues. Rice Husk Ash (RHA), Cori Fiber, and Lime are being tested in this study to see if they can act as a stabilising material in the expansive black cotton soil. The impact of RHA, CF, and LIME on the expansive soil's index and engineering properties was studied in the lab. Coir fibre concentration is 1.5 percent, lime is 5 percent by weight of dry soil, and RHA is mixed in at a ratio of 20 percent. The virgin soil sample is first tested for specific gravity and grain size distribution. With and without these admixtures soil's index properties like its plastic limit, liquid limit and shrinkage limit and its strength properties like its California Bearing Ratio, Unconfined Compressive Strength tests are discovered. According to the test results, a combination of 5 percent lime and 1.5 percent coir fibre yielded the strongest soil and best index properties