Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology http://hebgydxxb.periodicales.com/index.php/JHIT en-US Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology 0367-6234 CLOUD DATA: ENCRYPTION AND AUDITING BY A THIRD PARTY http://hebgydxxb.periodicales.com/index.php/JHIT/article/view/1395 <p>In cryptography, secrecy, honesty, and auditability have long been stressed, and providing security is the foundation of all cutting-edge cryptographic research. There may be certain challenges in the emerging era of distributed computing when clients store information on integrated cloud servers. This research focuses specifically on the reliability of data stored on cloud servers. One of the services provided by cloud computing is distributed storage, which enables remote management of information and client access across an enterprise (regularly the Internet). The client is concerned about the security of information stored in the cloud since it could be stolen or altered by an outside attacker. Considering this, a different concept known as information examining is put out, which uses a component known as a Third-Party Auditor (TPA) to verify the accuracy of information. The goal of this work is to develop an evaluation strategy that is safe, effective to use, and has capabilities like protection saving, public inspecting, maintaining information uprightness alongside classification. Information owner, TPA, and cloud server are the three components that make up this system. The owner of the information undertakes several activities such as dividing the record into blocks, encrypting them, creating a hash as a reward for each, connecting it, and leaving a mark on it. The TPA performs the essential function of information trustworthiness verification. It carries out tasks including generating a hash as a reward for encoded blocks obtained from a cloud server, connecting them, and creating a signature on it. To save the data-scrambled squares, only the cloud server is used. Utilizing a TPA that is authorized to periodically verify the accuracy of information rather than a customer, the information uprightness is confirmed. Block level verification is developed further using Markel Hash Tree. Bilinear total mark is used to manage reviewing assignments continuously. The evaluation of a multi-client TPA framework is discussed.</p> Deepali Ujalambkar*, Geetanjali Sharma, Punam Sawale, Sairabanu Pansare & Aman Kamble Copyright (c) 2022 2022-11-15 2022-11-15 54 11 1 10 SEISMIC EVALUATION OF SINGLE-STORY REGULAR AND IRREGULAR BRICKS HOUSES IN IRAQ http://hebgydxxb.periodicales.com/index.php/JHIT/article/view/1396 <p>he seismic performance of the unreinforced brick houses building in Iraq was assessed in the present work considering of three types of the typologies. These houses are a kind of different types of masonry construction for houses in Iraqi cities, where bricks are used with cement mortar in the construction of these houses. The main aim of work herein is the study of the seismic sensitivity of three types of single-story houses in two perpendicular directions (x- and z- directions), by using the nonlinear dynamic analysis (Time-History analysis) in ABAQUS software. The houses were analyzed in two cases (regular and irregular in plan) while the height was constant for all models (3m). From the results, it can be concluded that the dimensions of these houses buildings, for all case studies, are the strong influence on the values of the ultimate top displacement and the ultimate base shear. Where the values are greater when placing the seismic wave in the largest direction and vice versa. The general trend of changing in these values occurs when changing the direction of the seismic wave.</p> Zainab Faisal Mutasher Falih*, Ammar Jasim Dakhil & Samoel Mahdi Saleh Copyright (c) 2022 2022-11-15 2022-11-15 54 11 11 29 IMAGE SEGMENTATION AND DEEP LEARNING FOR PLANT DISEASE DETECTION http://hebgydxxb.periodicales.com/index.php/JHIT/article/view/1399 <p>We reviewed numerous research and survey paper to check the techniques applied by other but they do not mention using advanced image segmentation methods. So, to test various methods for image processing and image segmentation we tried various methods like HSV segmentation, color correction, segmentation using flood fill algorithm, etc. At the end, by extracting the leaf portion and using various image processing techniques, we can increase the accuracy for detecting the plant disease. We trained a deep learning CNN based image segmentation model on more than 30k leaf images of more than 5 plants. After training and segmenting the dataset with the trained model, we used various image processing techniques to check for the increase in the accuracy. Also, to get the best result with least computational load, we experimented with various parameters while training the model. By using these mentioned methods, we are able to achieve 0.9795% validation accuracy. After training the model to classify the disease and plant, we converted it into tflite so that it can be directly loaded on the smartphone.</p> Sejal Thakkar*, Dr.Chirag Patel, Ved Suthar, Mitansh Gor, Raj Madhu, Prahar Shah, Yashas H Majmudar, Dharmik Patel & Jainam Patel Copyright (c) 2022 2022-11-22 2022-11-22 54 11 30 39 COTTON LEAF DISEASES DETECTION AND PREDICTION USING RESNET ALGORITHM http://hebgydxxb.periodicales.com/index.php/JHIT/article/view/1408 <p>In India, most of the revenue is generated from the agriculture sector. In agriculture, most of the farmers produce different farms like rice, wheat…etc. The most important farm is cotton, It is also called “White Gold”.The production of cotton is a very sensitive farm. If production is good it gives more profit. If production is less shows big losses and its leads to farmers' suicide also. In this regard, the main objective of this paper is to detect the diseases in the leaf of the cotton le leaf. To predict, the disease was priorly given to alert farmers and it leads an increase in the production of cotton. In computer science and engineering, there are so many cutting-edge technologies are used to provide solutions in any field. Now Deep Learning is one of the cutting-edge technology used to predict situations in the agriculture field is the important thing. In the cotton crop growth, the main thing is healthiness. if any insects and weather change diseases happened. By using RESNET algorithms to classify the diseases and predict the labels of the disease and find out the accuracy and find out the best suitable algorithm.</p> S. Anup Kant*, Movva Pavani & Kamarajugadda Kishore Kumar Copyright (c) 2022 2022-11-27 2022-11-27 54 11 49 57