INTERNET OF THINGS BASED SMART AGRICULTURE SYSTEM USING PREDICTIVE ANALYTICS

Due to the use of internet of things (IoT) devices, communication between different things is effective. The application of IoT in agriculture industryplays a key role to make functionalities easy. Using the concept of IoT and wireless sensor network (WSN), smart farming system has been developedin many areas of the world. Precision farming is one of the branches comes forward in this aspect. Many researchers have developed monitoring andautomation system for different functionalities of farming. Using WSN, data acquisition and transmission between IoT devices deployed in farms will be easy. In proposed technique, Kalman filter (KF) is used with prediction analysis to acquire quality data without any noise and to transmit this data for cluster-based WSNs. Due to the use of this approach, the quality of data used for analysis is improved as well as data transfer overhead is minimized in WSN application. Decision tree is used for decision making using prediction analytics for crop yield prediction, crop classification, soil classification, weather prediction, and crop disease prediction. IoT components, such as and cube (IOT Gateway) and Mobius (IOT Service platform), are integrated in proposed system to provide smart solution for crop growth monitoring to users.Â

An architecture for a loosely-coupled parallel processor

technical reportAn architecture for a large (e. g. 1000 processor) parallel computer is presented. The processors are loosely-coupled, in the sense that communication among them is fully asynchronous, and each processor is generally not unduly delayed by any immediate need for specific data values. The network supporting this communication is tree shaped, with the individual processors connected at leaf nodes. The machine executes a graphical version of applicative Lisp. The program execution model is demand-driven, with a special deferred interpretation for dotted pair evaluation, termed "lenient cons". Opportunities for concurrency arise in the parallel evaluation of arguments to strict operators, i. e. those known to require evaluation of their full set of arguments. Such opportunities are exploited by exporting function application tasks to neighboring processor nodes in the tree, subject to a hierarchical notion of load balancing. Locality of task allocation and communication is a key objective of the machine. An integrated design toward that end is presented, combining language issues, firm semantic foundations, and anticipated hardware technologies

Automatic Classification of Medicinal Plants Using State-Of-The-Art Pre-Trained Neural Networks

Now a days every mankind is suffering due to infections. Ayurveda, the science of life helped to take preventive measures which boost our immunity.  It is plant-based science. Many medicinal plants found useful in daily life of common people for boosting immunity. Identifying the plant species having medicinal plant is challenging, it requires botanical expert. In the process of manual identification, botanical experts use various plant features as the identification keys, which are examined adaptively and progressively to identify plant species. The shortage of experts and trained taxonomist created global taxonomic impediment problem which is one of the major challenges.  Various researchers have worked in the field of automatic classification of plants since the last decade. The leaf is considered as primary input as it is available throughout the whole year. The research paper mainly focuses on the study of transfer learning approach for medicinal plant classification, which reuse already developed model at the starting point for model on a second task. Transfer learning approach is a black box approach used for image classification and many more applications by extracting features from an image. Some of the transfer learning models are MobileNet-V1, VGG-19, ResNet-50, VGG-16. Here it uses Mendeley dataset of Indian medicinal plant species which is freely available. Output layer classifies the species of leaves. The result provides evaluation and variations of above listed features extracted models. MobileNetV1 achieves maximum accuracy of 98%

Accuracy Optimization of Centrality Score Based Community Detection

Various concepts can be represented as a graph or the network. The network representation helps to characterize the varied relations between a set of objects by taking each object as a vertex and the interaction between them as an edge. Different systems can be modelled and analyzed in terms of graph theory. Community structure is a property that seems to be common to many networks. The division of the some objects into groups within which the connections or relations are dense, and the connections with other objects are sparser. Various research and data points proves that many real world networks has these communities or groups or the modules that are sub graphs with more edges connecting the vertices of the same group and comparatively fewer links joining the outside vertices. The groups or the communities exhibit the topological relations between the elements of the underlying system and the functional entities. The proposed approach is to exploit the global as well as local information about the network topologies. The authors propose a hybrid strategy to use the edge centrality property of the edges to find out the communities and use local moving heuristic to increase the modularity index of those communities. Such communities can be relevantly efficient and accurate to some applications. DOI: 10.17762/ijritcc2321-8169.15073

Metal oxides and its blended derivative’s coating for anti-corrosion application

9-17Thin film deposition by using different nanomaterials has been an efficient and reliable way for enhancing the anti-corrosive property of the materials as well as strength improvement such as hardness, conductivity and wear resistance. Various materials have been taken as substrates like mild steel, magnesium, Aluminum, Copper, Tin, Carbon steel with thin film coatings of Zn–TiO2, Ce/Co, Zn-HA/TiO2, CrN/TiN, Ni- Co have been sampled. These specimens have been studied for numerous properties like surface roughness, wear resistance, adhesion strength, microhardness, hydrophobicity etc. It has been found that the components like muffler, differential, engine chassis, exhaust system, gears do undergo corrosion due to several factors like climate change, oxidation, moisture content etc. The aim of the review has been to highlight the advances in the coatings providing anticorrosive properties to various metallic substrates used specially for mechanical and automobile industries

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

A Loosely-coupled Applicative Multi-Processing System

This paper describes a proposed machine, AMPS (Applicative Multi-Processing System). It features a loosely-coupled architecture, incorporating a large number (say 1000) of processors functioning independently to a large extent, but effectively interacting when necessary. Furthermore, the programs supported are not tied to the structure of the machine, thereby facilitating expandability. Such expandability is further enhanced by the particular physical organization to be described. The architecture of AMPS attempts to bring costs of communication among processing units to a manageable level by taking advantage of locality of reference