Abstract:The diagnosis of any disease is like the light at the end of the tunnel. In the case of the COVID-19 pandemic, the importance of earlier diagnosis and detecting the disease is beyond measure. The initial focus must be on the data by which we need to efficiently train a model. This data will help Machine Learning (ML). or Deep Learning (DL) algorithms to diagnose COVID-19 cases. Due to the disadvantages of RT-PCR, researchers adopted an alternative method which is the use of Artificial Intelligence on chest CT or X-Ray images to diagnose COVID-19. Fundamentally, a chest CT image is an image taken using the computed tomography (CT) scan procedure where X-Ray images are captured from different angles and compiled to form a single image. Although a CT scan consumes less time to demonstrate, it is fairly expensive. As a result, many researchers adopted X-Ray images instead of CT images to develop a COVID-19 detection model. A chest X-Ray is a procedure of using X-Rays to generate images of the chest. Also, it is relatively economical and convenient to maintain.

Keywords:Detecting, Machine Learning, Deep Learning, X-Ray, Computed Tomography(CT)

Abstract:Whenever an evolving technology approaches a dead end, a new technological revolution is needed. “The present“ VLSI technology is based on the technology of CMOS. Due to the new challenges in the existing technology, the advanced technology based on quantum-dot cellular automata (QCA) has been introduced.QCA is an interesting area in nano-computing technology, providing an alternative approach to resolve the physical limitations faced by CMOS systems during further down scaling of their significant sizes. At nanometer scale, QCA offers powerful features like higher packaging density, minimized area, much lesser power consumption and better operating speed. Current logic gates really aren’t power saving or energy efficient because they are not inherently reversible in nature and thus results in the dissipation of energy. Therefore, a serious effort is required to provide an effective model for the design of circuits that do not dissipate energy and hence preserve information. Power-efficient circuits can be constructed with more precision which ultimately increase the lifetime and speed of the circuit using this technique. The successful design of the Feynman gate-based reversible Binary to Gray and Gray to Binary code converter using QCA is presented in this paper. The proposed design proves to be efficient in terms of cell size, cell count, overall area, latency and complexity. The outcome shows that the configuration of the design is territory proficient and has a lower clock delay. Besides, the circuit setup is extremely clear and did not use any flipped, translated QCA cells, and offers single-layer access to their information sources and outcomes. This encoder circuit using reversible logic gates can be further explored for the designing of other low power loss devices”.”In addition to this for the first time energy dissipation analysis for different scenarios is also done on all the designs using QCA Pro-tool and it is observed that the proposed designs dissipate minimum energy thereby making them suitable for Ultra-low power designs. All the proposed reversible code converter prototypes have been simulated and the QCA Designer tool has checked their credibility successfully”.

Keywords:Meta- Binary Code, Energy estimation, Feynman Gate ,Gray Code, , Latency, QCA Designer, QCA Pro tool, Ultra Low Power.

Abstract:Learning as a process is very important aspect of growth in nature. Meta-learning in machine learning refers to learning algorithms that learn from other learning algorithms [7]. Meta-learning benefits all the machine learning systems from their repetitive application. But meta-learning differs a lot from the base-learning in the scope of the level of adaptation. It has its own benefits and challenges with gradual growth in its process towards evolution over the year. optimization-based formulation of meta-learning that learns to design an optimization algorithm automatically, which we call Learning to Optimize [8]. Understanding how it actually affects various sectors and making things simpler or difficult is a complex analysis of meta-learning in detail. This paper best bit is the challenges lying in front of us to reach such goal and benefits to use for research. Meta-learning is one of the most vibrant regions of research in the profound learning space. A few ways of thinking inside the Artificial Intelligence(AI) people group buy in to the postulation that meta-learning is one of the venturing stones towards opening Artificial General Intelligence(AGI) [14].

Keywords:Meta-learning, Adaptive learning, learning to learn

Abstract:Agriculture is a main part of India's economy and at present it is among the top two rank makers in the world. This field provides roughly around 52 % of the total number of occupations in India and contributes around 18.1 percent to the GDP (Gross Domestic Product).For a country like India to gain profit it is necessary that rural and mechanical advancement ought to takeplace head to head.In India, the greater part of land used for rural section which produces semi-completed item. Around 65% of individuals rely upon agribusiness as a principle occupation.Since antiquated occasions cultivating is done physically which causes lesser efficiency and additional time requirement.The opportunity has arrived to modernize our cultivating cycle so profitability can be expanded. The fundamental target of the task is to build up the under grounded plants harvester thinking about the necessities of Indian ranchers. Among the field activities conserned with cultivation, collecting is the most difficult and exorbitant undertaking. Existing ground collectors are too gigantic to possibly be helpful for limited scope ranchers and in situation like multicroping. At first overview of common place ranch field has been done trailed by writing study, static examination, manufacture testing and plan adjustments. The existing harvester accessible currently is financially heavy which just only plucks the evacuated plants not the under established plants.

Keywords:Automated ground harvester , Agriculture Robot , Robotic Arm , Rover , Processor , Controller , Arduino, Irrigation , Tradational cultivating rehearses , Motor, Motor Driver, Pump, Camera

Abstract:Multisite deals with many websites within the domain name. The multisite features are provided in Wordpress open source software. Many softwares are used to clone WordPress sites and most of the softwares are price. Some of the softwares are not supported to different domain name. The languages with many sites are not properly with their changed domain name. From this all phenomenon, the data management and loading sections for cloning method are explored.

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