Review of the State of the Art of Transfer Learning for Plant Leaf Diseases Detection

Plant leaf diseases can have a significantly negative influence on the quantity and quality of agricultural cultivation, as well as the safety of food production. Plant leaf diseases could potentially entirely prevent the harvest of grains in some situations. Therefore, it is extremely important from a pragmatic standpoint to look for quick, automatic, cheap, and accurate ways to detect plant leaf diseases. One of the well-known plant leaf disease detection approaches is deep learning. Deep learning has several drawbacks as a result of the huge amount of data required to train the network. When a dataset has inadequate photographs, performance falls. An approach called "Transfer Learning" is an extensively used method for addressing the shortcomings of a small dataset, the length of the training process, and improving the performance of the model. In this study, we investigated transfer learning for deep CNNs to improve the learning capability to recognize leaf disease. This survey focuses on categorizing and analyzing the recent developments in transfer learning for Deep CNN situations to enhance learning performance by reducing the need for extensive training data collecting

Data-Driven Decision Making: Real-world Effectiveness in Industry 5.0 – An Experimental Approach

This empirical study on Industry 5.0 offers verifiable proof of the transformational potential of data-driven decision making. The validation of data-driven choices as a key component of Industry 5.0's performance is shown by a noteworthy 46.15% increase in decision outcomes. The fact that choice criteria are in line with pertinent data sources emphasizes how important data is in forming well-informed decision-making processes. Moreover, the methodical execution and oversight of choices showcase the pragmatic significance of data-driven methodologies. This empirical evidence positions data-driven decision making as a cornerstone for improving operational efficiency, customer happiness, and market share, solidifying its essential role as the industrial environment changes. These results herald in an age when data's revolutionary potential drives industrial progress by providing a compass for companies trying to navigate the complexity of Industry 5.0

Data-Driven Decision Making: Real-world Effectiveness in Industry 5.0 – An Experimental Approach

This empirical study on Industry 5.0 offers verifiable proof of the transformational potential of data-driven decision making. The validation of data-driven choices as a key component of Industry 5.0's performance is shown by a noteworthy 46.15% increase in decision outcomes. The fact that choice criteria are in line with pertinent data sources emphasizes how important data is in forming well-informed decision-making processes. Moreover, the methodical execution and oversight of choices showcase the pragmatic significance of data-driven methodologies. This empirical evidence positions data-driven decision making as a cornerstone for improving operational efficiency, customer happiness, and market share, solidifying its essential role as the industrial environment changes. These results herald in an age when data's revolutionary potential drives industrial progress by providing a compass for companies trying to navigate the complexity of Industry 5.0

A Survey (NLP) Natural Language Processing and Transactions on (NNL) Neural Networks and learning Systems

Natural Language Processing NLP, or Natural Language Processing, is an area of artificial intelligence (AI) that concentrates on the interaction between computers and human language. Its goal is to develop algorithms and models that enable computers to understand, interpret, and generate human language in a meaningful manner. NLP aims to bridge the gap between human language and computer language, enabling computers to process and comprehend natural language data effectively. This field encompasses a range of tasks, including speech recognition, language translation, sentiment analysis, text summarization, question answering, and many others. These tasks rely on machine learning models that are trained using large amounts of annotated data. media monitoring, information retrieval, customer support chatbots, and many other areas where understanding human language is crucial. As research and development in NLP progress, we can anticipate the emergence of more advanced and sophisticated models that will further enhance the capabilities of language processing and understanding by computers. This advancement holds exciting possibilities for human-computer interaction. NLP research has seamless communication and empowering us to extract valuable knowledge from textual data on a large scale. technique employed in the process of multi-criteria decision-making and prioritizing alternatives. Its purpose is to provide a structured method for assessing and ranking different options in the presence of multiple criteria that may conflict with one another. MOORA is widely utilized across various domains, including operations research, engineering, and management. Alternative: Programming language, Linguistic theory, Hardware platform, Knowledge Representatio

Fabrication of copper sulfide nanoparticles from b

A nanomaterial has played a major role in protecting the environment-related issues. The prime reason for that nanomaterials synthetics approach is greener pathway, without using any hazardous chemicals and solvents. A huge number of plant-mediated metal sulfide nanoparticle (especially, Copper sulfide) synthesis has been reported and is still successfully continuing, because of its cost effective manner, eco-friendly nature, simple approach, reaction was carried out room temperatur. The current reports to synthesis of Copper sulfide nanostuctured materials through the green patch way, using Boswellia Ovalifoliolata leaves extract. From the UV-Visible spectroscopy noticed nanoparticles absorbance value is around 325 nm. As identified by FT-IR spectroscopy, a variety sources of phytochemicals in the extract which are responsible for the reduction of metal ions and stabilizing of the nanoparticles. X-ray diffraction studies revealed that nanomaterials were crystalline in nature, average crystalline size around 11 nm. SEM revealed that nanoparticles are spherical in nature and average size is 38.43 nm. The current report emphasizes that the materials are an excellent catalyst activity for the reduction of environmental pollutant azo dyes, antibacterial and antioxidant activity. The current article highlights the reduction of the azo dyes, antibacterial and antioxidant activity so the nanomaterials are apromising for the reduction of polluntat dyes

Digital Control of DSTATCOM using TI-C2000 Processor and MATLAB/ Simulink for Sustainable Power System Network

This paper describes the detailed digital control of a Distribution Static Synchronous shunt Compensator (DSTATCOM) using a MATLAB embedded coder for compensation to be in command of Voltage Source Converter (VSC) input voltage, reactive power compensation and the power factor improvement of a secondary radial distribution system with a voltage rating of 11kV/415V and with a capacity of 750 kW. The model has been simulated and tested in MATLAB/Simulink using the Simscape tool block set version of MATLAB R2019a. The same is achieved using a 32-bit DSP TMS320F2812 controller programmed with CCS V 6.1 software and tested with MATLAB embedded coder generation using Texas Instrument C2000 processors. Using this embedded code is developed and dumped into the CCS V 6.1. Real and Reactive power control (PQ) and Back Propagation Control Algorithm (BPCA) are the control methods. BPCA controller is based on the elementary extracted biased value of Reactive Power (Q) and Active Power (P) components of load currents. These current values are necessary for the reference source's current estimation. The reference source currents are estimated from the available source currents to generate firing pulses for the PQ and BPCA controllers. The real and reactive power controller is based on the transformation of synchronously rotating load currents in alpha and beta frames. The BPCA-based DSTATCOM is simulated for linear and non-linear load circumstances and that helps in restoring the power system networks sustainability

EoG based Biopotential Instrumentation Amplifier

To examine the biomedical signals from the human body is of a challenging task. Signal acquisition of the biosignal needs proper design methodology with the estimation of consequences further. In this paper we discuss about the signal acquisition of EoG using the surface electrodes and to amplify the weak biopotential signal an amplifier is proposed with the additive noise reduction filter. The biopotential instrumentation amplifier is designed using standard operational amplifier that posses the high voltage swing. However the major artifacts present in the EoG signal acquisition is because of power line interference can be eliminated by notch filter