Journal of Science & Technology (JST)
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A LOAD BALANCING ALGORITHM FOR THE DATA CENTRES TO OPTIMIZE CLOUD COMPUTING
Get PDFDespite the many past research conducted in the Cloud Computing field, some challenges still exist related to workload balancing in cloud-based applications and specifically in the infrastructure as service (IaaS) cloud model. Efficient allocation of tasks is a crucial process in cloud computing due to the restricted number of resources/virtual machines. IaaS is one of the models of this technology that handles the back end where servers, data centers, and virtual machines are managed. Cloud Service Providers should ensure high service delivery performance in such models, avoiding situations such as hosts being overloaded or under loaded as this will result in higher execution time or machine failure, etc. Task Scheduling highly contributes to load balancing, and scheduling tasks much adheres to the requirements of the Service Level Agreement (SLA), a document offered by cloud developers to users. Important SLA parameters such as Deadline are addressed in the LB algorithm. The proposed algorithm is aimed to optimize resources and improve Load Balancing in view of the Quality of Service (QoS) task parameters, the priority of VMs, and resource allocation. The proposed LB algorithm addresses the stated issues and the current research gap based on the literature’s findings. Results showed that the proposed LB algorithm results in an average of 78% resource utilization compared to the existing Dynamic LBA algorithm. It also achieves good performance in terms of less Execution time and Make span
DYE DEGRADATION USING COMBINATION OF ZnO NANOPARTICLES AND LEMON PEEL WASTE
Get PDFA variety of synthetic dyestuffs released by the textile industry pose a threat to environmental safety. Azo dyes account for the majority of all dyestuffs, produced because they are extensively used in the textile, paper, food, leather, cosmetics, and pharmaceutical industries. Industrial revolution as marked a strong impact of the economy and financial up gradation. which includes advantages and disadvantages. Major impact include environmental pollution. It creates major impact on environmental because of the release of unwanted products in air and inside the water bodies. The uses of die as increased in varies industries like food, leather, textile, paper, cosmetics, pharmaceuticals etc.The problem as emerged because of disposing of dye in open environment which leads major issues inhuman health ,aquatic life, animals life.In this role, nano particles is used for eliminating the dye from industrial water the nanoparticle ZnO. The lemon peel was soaked in the water for overnight and the water was collected as a sample.There are two types of dye used in this paper work they are blue RR and red RR dye. The absorption of dye is determined in a particular interval of time measured using calorimeter .FTIR(Fourier transform infrared spectroscopy) was done to identify the rate of absorption ,emission and photoconductivity of dye sample.The calorimeter shows the reading of differentt values (0.74,0.66,0.84,0.70,etc.,) based on dye and extract and compared
Comparative Study of the Impact of Ranitidine and Sitagliptin on Acetic Acid- Induced Gastric Ulcer Healing in Rats
Get PDFCyclooxygenase-2 (COX-2) and Inducible Nitric Oxide Synthase (iNOS) are two of the many promoting factorsthat control the complicated process of gastric ulcer healing. In most cases, delayed stomach ulcer healing islinked to diabetes mellitus. In order to compare the effects of ranitidine and sitagliptin (dipeptidyl peptidase-4inhibitor) on the healing of stomach ulcers, the current study was created. Forty male albino rats, split into fourequal groups, participated in the current study: Groups 1 and 4 are the normal control group, the gastric ulcermodel group, the sitagliptin-treated group, and the ranitidine-treated group, respectively. The stomach of the ratswas taken for histological analysis and immunohistochemical evaluation of COX-2 and iNOS ten days after ulcerinduction, and the rats were then killed. This study found that the sitagliptin-treated group had much worse gastriculcer healing than the ranitidine-treated group. This was demonstrated by the stomach's histological investigation,which showed a significantly bigger ulcerated region and poor ulcer base maturation. In comparison to the ulcermodel group and the ranitidine-treated group, the sitagliptin-treated group exhibited a substantial decrease inmean vascular density (MVD), COX-2, and iNOS expression. A strong positive association between iNOS andCOX-2 was discovered, suggesting that they work in concert. COX-2 and iNOS were found to have a substantialpositive connection with MVD, indicating that they had a proangiogenic effect. Given these findings, it is unclearif sitagliptin is recommended for diabetic patients who already have a stomach ulcer. Future investigationsinvolving humans are required to validate our initial experimental findings
ASSESSMENT OF GROUND WATER QUALITY AROUND SOLID WASTE DUMPING YARD, AMRAVATI, VIDARBHA REGION, INDIA
Get PDFWater is one of the abundantly available substance in nature. It is essential constituent of all animal and vegetable matter and forms about 75% of the matter of Earth's Crust. The present research work is based on assessment of ground water quality around solid waste dumping yard in Amravati, Vidarbha region of India. In this work ground water quality assessment was done with the help of physico-chemical parameters like pH, temperature, turbidity, total dissolved solids, conductivity, total alkalinity, total hardness, chlorides, sulphate, total phosphate, dissolved oxygen, nitrate and fluoride. The all parameters are analysed with the help of NEERI standards water and waste water manual methods and obtained results ware compared with BIS and WHO standards. The results shows that the quality of underground water around solids waste dumping yards villages are not so good and not for suitable to directly drinking purpose. It is caused due to water percolated through solid waste dumping area and it contaminated the ground water. The treatment of ground water is essential in the study area
Lung Cancer Detection Using Image Processing Technique
Get PDFCancer is a quite common and dangerous disease. The various methods of cancer exist in the worldwide. Lung cancer is the most typical variety of cancer. The beginning of treatment is started by diagnosing CT scan. The risk of death can be minimized by detecting the cancer very early. The cancer is diagnosed by computed tomography machine to process further. In this paper, the lung nodules are differentiated using the input CT images. The lung cancer nodules are classified using support vector machine classifier and the proposed method convolutional neural network classifier. Training and predictions using those classifiers are done. The Nodules which are grown in the lung cancer are tested as normal and tumor image. The testing of the CT images are done using SVM and CNN classifier. Deep learning is always given prominent place for the classification process in present years. Especially this type of learning is used i
Text Classification for Newsgroup using Deep Learning
Get PDFWith the developments of internet technologies, dealing with a mass of law cases urgently and assigning classification cases automatically are the most basic and critical steps. Convolutional Neural Networks (CNNs), has been shown to be effective for text classification. To better apply CNNs into law text classification, this paper presents a new semi-supervised Convolutional Neural Networks (SSC) framework. Our method combines unlabeled data with a small labelled training set to train better models, and then integrates into a supervised CNN. More specifically, for effective use of word order for text categorization, we use the feature of not low-dimensional word vectors but high-dimensional text data, that is, a small text region is learned based on sequences of one-hot vectors. To better improve the prediction accuracy of the scheme, we seek effective use of unlabeled data for text categorization for integration into a supervised CNN. We compare the proposed scheme to state-of-the-art methods by the real datasets. The results demonstrate that the semi-supervised learning model can get best text classification accuracy
SIGN LANGUAGE RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS
Get PDFSign Language Recognition (SLR) targets on interpreting the sign language into text or speech, so as to facilitate the communication between deaf-mute people and ordinary people. This task has broad social impact, but is still very challenging due to the complexity and large variations in hand actions. Existing methods for SLR use hand-crafted features to describe sign language motion and build classification models based on those features. However, it is difficult to design reliable features to adapt to the large variations of hand gestures. To approach this problem, we propose a novel convolutional neural network (CNN) which extracts discriminative spatial-temporal features from raw video stream automatically without any prior knowledge, avoiding designing features. To boost the performance, multi-channels of video streams, including color information, depth clue, and body joint positions, are used as input to the CNN in order to integrate color, depth and trajectory information. We validate the proposed model on a real dataset collected with Microsoft Kinect and demonstrate its effectiveness over the traditional approaches based on hand-crafted feature
An Efficient Application to Provide Security to The Banks Using Face Recognition Using Open CV
Get PDFA facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. Proposed paper uses face recognition technique for verification in ATM system. For face recognition, there are two types of comparisons. The first is verification, this is where the system compares the given individual with who that individual says they are and gives a yes or no decision. The next one is identification this is where the system compares the given individual to all the other individuals in the database and gives a ranked list of matches. Face recognition technology analyzes the unique shape, pattern and positioning of the facial features. Face recognition is very complex technology and is largely software based using Convolutional Neural network (CNN). Face Recognition is a computer application. It is capable to detect, identify or verify, track human faces from the input captured using a digital camera. This technology facilitates the machine to identify and recognize each and every user uniquely through the face as a key. This completely eliminates the chances of fraud due to theft and duplicity of the ATM cards. The captured face of the user must be matched with the registered face to have the access of the account. On the basis of iris uniqueness and other prerequisites the images of user are differentiated. The main aim or outcome of this project is to provide security to ATM transactions
A CONTEMPORARY TECHNIQUE FOR LUNG DISEASE PREDICTION USING MACHINE LEARNING
Get PDFLung cancer is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of lung cancer is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (X-ray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Nodule detections were performed through faster R-CNN on efficiently-learned features from CMixNet and U-Net like encoder– decoder architecture. Classification of the nodules was performed through a gradient boosting machine (GBM) on the learned features from the designed 3D CMixNet structure. To reduce false positives and misdiagnosis results due to different types of errors, the final decision was performed in connection with physiological symptoms and clinical biomarkers. With the advent of the internet of things (IoT) and electro-medical technology, wireless body area networks (WBANs) provide continuous monitoring of patients, which helps in diagnosis of chronic diseases—especially metastatic cancers. The deep learning model for nodules’ detection and classification, combined with clinical factors, helps in the reduction of misdiagnosis and false positive (FP) results in early-stage lung cancer diagnosis. The proposed system was evaluated on LIDC-IDRI datasets in the form of sensitivity (94%) and specificity (91%), and better results were obatined compared to the existing method
OPEN PORT SCANNER TO IDENTIFY OPEN PORTS USING PYTHON
Get PDFAn Open Port Scanner is a vital tool for identifying open network ports on a target system, which is crucial for network security assessment and troubleshooting. This abstract introduces a Python-based Open Port Scanner designed to help network administrators and security professionals quickly and efficiently identify open ports on a target host. The tool leverages Python's socket library to establish connections with various network services on the target system, and it can be customized to scan specific port ranges or even conduct comprehensive scans on multiple hosts. The Open Port Scanner operates by systematically probing a range of TCP or UDP ports on a target host to determine their open or closed status. It provides real-time feedback on the ports' accessibility, making it a valuable asset for identifying potential vulnerabilities and unauthorized services. The tool also includes features for customizing scan parameters, such as setting timeout values and specifying multiple target hosts. The results of the scan are presented in a user-friendly format, enabling users to easily identify open ports and assess the network's security posture. This Python-based Open Port Scanner serves as a powerful and flexible solution for network administrators and security experts seeking to secure their systems and networks by proactively identifying and addressing potential vulnerabilities. Its opensource nature allows for further customization and integration into existing network security workflows, making it a valuable addition to the toolkit of those responsible for maintaining the integrity of network infrastructure