Disease, Detection and Description of Plant Using CNN
Authors: Revathy B, Ritika Sudipa Lenka
Abstract
At the point when plants and harvests are experiencing vermin it influences the rural creation of the country. Normally, ranchers or specialists notice the plants with eye for location and ID of infection. Yet, this strategy is regularly time handling, costly and mistaken. Programmed location utilizing picture handling methods give quick and exact outcomes. This paper cares with a substitution way to deal with the advancement of infection acknowledgment model, upheld leaf picture arrangement, by the usage of profound convolutional networks. Propels in PC vision present an opportunity to grow and improve the act of exact plant security and broaden the market of PC vision applications inside the field of accuracy farming. This paper (project) likewise points the amateurish nursery workers and specialists who track down trouble to seek after their advantage effectively. This procedure of plant sickness location will be of incredible assistance to those individuals and they find out about the infection caused , its depiction, counteraction and therapy of it. A totally exceptional approach to preparing and subsequently the technique involved work with a quick and direct framework execution by and by. All fundamental advances needed for carrying out this sickness acknowledgment model are completely depicted all through the paper, beginning from social affair pictures to make an information base, surveyed by horticultural specialists, a profound learning system to play out the profound CNN preparing. This technique paper might be another methodology in distinguishing plant infections utilizing the profound convolutional neural organization prepared and finetuned to suit precisely to the information base of a plant's leaves that was accumulated freely for assorted plant illnesses. The development and curiosity of the created model abide its effortlessness; solid leaves and foundation pictures are in accordance with different classes, empowering the model to recognize sick leaves and sound ones or from the climate by utilizing CNN. Plants are the wellspring of food on the planet. Contaminations and sicknesses in plants are thusly a major danger, while the first normal finding is essentially performed by inspecting the plant body for the presence of visual manifestations [1]. As an option in contrast to the customarily tedious cycle, different exploration works intend to track down plausible methodologies towards securing plants. Lately, development in innovation has caused a few options in contrast to customary laborious techniques [2]. Profound learning methods are extremely effective in picture grouping issues.
Chloroquine and Hydroxychloroquine: A Major Breakthrough for Covid-19
Authors: Kritika Verma, Inder Kumar
Abstract
Coronavirus pandemic or COVID-19 is a global public health emergency at this period. Presently, no pharmacological treatment is known to treat this condition. Hydroxychloroquine (HCQ), a derivative of chloroquine (CQ), was first synthesized in 1946 by adding a hydroxyl group to CQ, which is much less toxic than CQ in animal studies. Other than being an anti-malarial drug, it was revealed to have various pharmacological effects and one of those is its anti-viral property. CQ, as well as HCQ, has been used in SARS (Severe Acute Respiratory Syndrome) coronavirus infection due to its antiviral properties. Even though various scientists have considered HCQ as a better therapeutic approach than CQ for the treatment of coronavirus infection, there are various adverse drug reactions associated with HCQ treatment in COVID-19 patients. In this paper, we review the anti-viral mechanism, various adverse drug reactions, and side effects of HCQ for COVID-19 treatment.
Blockchain Based Data Sharing
Authors: Mr. Vikas Narkhede, Dr. Satpalsing Rajput
Abstract
In traditional cloud storage systems, all data is stored in the cloud server, which could lead to major issues including key misuse and privacy data leaking. However, because the traditional cloud storage system depends on centralized storage, a single point of failure could cause the system to fail. With the advancement of blockchain technology, decentralized storage has become more well-known. Decentralized storage can overcome the problem of a single point of failure in typical cloud storage systems while also providing a variety of benefits over centralized storage, including low cost and fast throughput. In this work, focus on data storage and sharing in decentralized storage systems and propose a framework that includes IPFS, an encryption technique, and blockchain technologies. The data holder can distribute secret keys to data users and encrypt shared data location by Blockchain network. In this research contribute to data integrity verification in the blockchain network.
Developing Students’ Writing Skills in English - A Process Approach
Authors: Mr. G. Lakshmikanthan
Abstract
Writing is an extremely complex cognitive activity in which the writer is required to demonstrate control of variables simultaneously. Strong writing skills may enhance students' chances for success. Writing is an essential factor of language. Good writing skills are needed for all the students in order to accomplish their educational and employable requirements. Process Approach stresses writing activities which move learners from the generation of ideas and the collection of data through to the publication of a finished text. Process approach is learnercentered in which learners’ needs, expectations; goals, learning styles, skills and knowledge are taken into consideration.