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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Sridhar, J. Kumar, B. Pavan Soumya, G. Saravanan, A.V. Surendra, A. Shruthi, P. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh Chittoor517583 India

ISBN: (纸本)9798350376685

Gastric cancer (GC) is a common and deadly tumor with poor prognosis. Early detection uses endoscopy, and further treatment requires pathological confirmation and CT scanning. AI systems can help due to a lack of pathologists globally. Most GCs have genetic instability, an early gastric cancer sign. A new classification system based on histology, genotype, and molecular phenotype aids early diagnosis, prevention, and treatment. The project uses deep learning, especially Gastronet, to assist in GC diagnosis, offering high accuracy without extra tests. © 2024 IEEE.

关键词： Endoscopy

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Hot Deformation Characteristics and Tool Wear Morphology of GH4169 Super Alloy: A Frugal Usage of Cryogenic Cooling During Turning Process

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Journal of The Institution of Engineers (India): Series D 2025年 1-13页

作者： Vinod, B. Sudhakiran, P. Reddy, S. Sunil Kumar Pasupulati, Baburao Department of Mechanical Engineering Siddharth Institute of Engineering & Technology Puttur India Department of Electronics and Communication Engineering New Horizon College of Engineering Bangalore India Department of Electrical and Electronics Engineering Veltech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi India

Demand for nickel-based superalloys has increased significantly in the automotive industry because of their great potential to reduce the weight of components and improve efficiency. The present study aims to improve the tribological performance and tool lifespan of the GH4169 alloy through machining, focusing specifically on the potential benefits of using cryogenic cooling. It has better wear and mechanical properties to make this alloy more competitive and viable. Hence, the experiments are conducted under liquid nitrogen (LN2), carbon dioxide (CO2), and minimum quantity lubrication (MQL) conditions. The critical tool wear factors have been investigated. The test results proved that cryogenic cooling with the inclusion of MQL helps improve tool life with better tribological characteristics. Furthermore, a hot deformation test was also performed to identify the material’s behavior. The results indicate that flow curves led to smooth regions controlled at low-temperature and high-strain rates. The formation of DRX structures in the processing map is crucial to improving the mechanical properties of these cast components. The Brinell hardness of nickel-based alloys improved after using cooling conditions. Applying the minimum quantity of lubrication cooling conditions exhibits a high hardness of 498 BHN, a 41% increase compared to other conditions. © The Institution of Engineers (India) 2025.

关键词： Strain rate

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Data Protection Strategies: Employing IBBE and Attribute-Based CPRE for Secure Data Distribution

Data Protection Strategies: Employing IBBE and Attribute-Bas...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Murali, Erasappa Jerinjose, J. Sharanya, L. Lakshmi Lokeswari, K. Roshini, K. Kalpana, D. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh Chittoor India

ISBN: (纸本)9798350378092

Worldwide, people and businesses are starting to adopt cloud computing more and more. Although there are still many issues that need to be overcome, cryptography can assist users in public clouds in protecting their data. We present a practical and safe identity-based data group sharing and distribution system for public clouds that enables data owners to simultaneously broadcast encrypted data to multiple recipients. Security issues with public cloud infrastructure are the driving force behind data confidentiality regulations. We employ conditional proxy re-encryption that is both ascribe-based and timed-release, which gives the cloud server a reencryption key. These ways, only disseminators whose characteristics comply with the encrypted data's access policy are able to distribute it to other groups beyond the release period. Through this, we are able to disseminate data groups in a secure and adaptable manner. The distribution of cipher texts can be controlled by data owners with fine-grained and timed-release access control by re-encryption conditions that are connected to characteristics and release time. © 2024 IEEE.

关键词： Ciphertext

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Detecting Plant Disease and Suggesting Sustainable Solutions by using Deep Learning Technique 5

Detecting Plant Disease and Suggesting Sustainable Solutions...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Kumar, M.P. Rani, Tms Mekala Teja, C. Priya, A. Vishnu Vivek, K. Nithya, Mudduluru Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andra Pradesh Chittoor517583 India

ISBN: (纸本)9798350376685

Crop loss due to pests and diseases poses a significant challenge in global agriculture, resulting in substantial economic ramifications, with an annual loss of crops valued at Rs 50,000 crore. This project addresses the pressing issues surrounding disease identification and management in agriculture. To revolutionize disease detection, we are pioneering an innovative approach leveraging Convolution Neural Networks (CNNs). By harnessing the power of AI platforms in collaboration with government resources, our aim is to deliver effective and efficient solutions to combat plant diseases. Our overarching goal is to simplify the process of disease identification and treatment through the utilization of advanced technology. Our project harnesses various convolution neural networks, including VGG-16, ResNet, DeepNet, and YOLO V3. We intend to train our model using these convolutional networks and select the one that exhibits the highest accuracy for our specific project requirements. Beyond merely identifying the type of disease, our project also provides tailored remedies for treating these specific diseases. We leverage Chat GPT technology and collaborate with government platforms to achieve this objective. Additionally, we translate these remedies into local languages to ensure accessibility for those in need. Moreover, we employ text-to-speech conversion, enabling the vocalization of remedies. This empowers farmers to receive guidance audibly and take prompt action to mitigate crop diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Epilepsy Seizure Determination Combining Brain and Heart Signal Analysis and Training 4

Epilepsy Seizure Determination Combining Brain and Heart Sig...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Babu, N. Sarvesan, B. Vani, G. Reddy, R. Uday Kumar Reddy, T. Venu Reddy, J. Sunayana Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh Chittoor517583 India

ISBN: (纸本)9798350349900

Epilepsy is a serious ongoing neurological problem, can be identified by investigating the mind cues delivered by cerebrum neurons. Neurons are associated with one another in a mind¬¬-boggling manner to speak with human organs and produce signals. The checking of these mind cues is ordinarily done utilizing Electroencephalogram (EEG) [5] and Electrocorticography (ECOG) media. These signs are intricate, loud, non-straight, non-fixed and produce a high volume of information. Worldwide, epilepsy influences roughly 50 million individuals, with 100 million being impacted no less than once in the course of their life. By and large, it represents 1% of the world's weight of sicknesses, and the predominance rate is accounted for at 0.5-1%. The principal side effect of epilepsy is to encounter more than one seizure by a patient. It causes an unexpected breakdown or uncommon movement in the mind that driving forces a compulsory change in a patient's way of behaving, sensation, and loss of fleeting cognizance. Consequently, the recognition of seizures and revelation of the cerebrum related information is a difficult undertaking. In this manner, the proposed framework gives a viable answer for anticipate the location of epilepsy capture of joining cerebrum heart signal in a more productive manner. In order to determine whether seizures are present at an early stage in this project, we will use AI calculations as gathering models, such as Irregular Backwoods, SVM, and Strategic Relapse. The datasets will be extracted from the Physio Net dataset and processed using AI calculations, culminating in the creation of a model document. When information is provided for the possibility of seizures, it can accurately determine whether epilepsy seizures are present and can lead to the development of effective therapy. Thus, our endeavor contributes to a feasible examination of the seizure assurance of combining the cerebrum [6] heart with motioning, more precisely than the existing models.

关键词： Electroencephalography

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Forecasting Network Intrusions To Prevent Secrecy Leakage Using Machine Learning 5

Forecasting Network Intrusions To Prevent Secrecy Leakage Us...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Kumar, R.G. Elankavi, R. Suchitra, Y. Sofiya, S. Thakur, Himanshu Kumar Shalini, K. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andra Pradesh Chittoor517583 India

ISBN: (纸本)9798350376685

An internet site intrusion detection gadget (IDS) facilitates finding assaults over the business enterprises and the criminals can be arrested. Earlier, diverse Machine Learning (ML) Methods were implemented for IDS to try to improve the detection outcomes for improved accuracy of attackers. They advocated this article which was an approach to develop an effective IDS using most important aspect analysis (PCA) and random wooded area category set of rules where PCA can assist in organizing the facts set by decreasing the dimensionality of the statistics and making it random a wooded area. The results received indicate the scope of the technique it plays and greater efficiently in terms of accuracy than different such methods like SVM, Naive Base and Decision Tree. The results received via the proposed technique are Execution time values (min) are three.24 mins, to be actual (%) is 96 and mistakes fee (%) is 0.21%. © 2024 IEEE.

关键词： Network intrusion

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Strengthening Cloud Security For Unauthorized Access Prevention

Strengthening Cloud Security For Unauthorized Access Prevent...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Balaji, V. Kumar, B. Pavan Lokamanya, G. Rammohan, B. Rakesh, S. Nethaji, M. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh Chittoor India Andhra Pradesh Chittoor India

ISBN: (纸本)9798350378092

Due to the increased frequency of recent attacks, there are serious cyber security issues as a result of smart device usage. Cloud computing has brought about major changes in the commercial world. Centralization, however, increases the danger of important data breaches and makes it impossible to deploy dispersed services like security systems due to the massive volume of data that is transmitted between businesses and cloud service providers. Insider attacks are a serious risk to the business since they can cause more damage and have greater access. Finding different strange events that could point to anomalies and security issues with privilege escalation is the aim of this technique. Through the use of ensemble learning to combine multiple models, the strategy can improve machine learning outcomes and prediction performance. The proposed standard uses a modified dataset taken from the CERT dataset to assess the Support Vector Machine (SVM) machine learning technique for insider attack classification. Utilizing patterns of behavior and activity, such as resource usage, file access times, and login times, the system builds a profile of typical behavior and looks for deviations. This strategy can assist companies in identifying insider threats early on and avert serious harm to their priceless resources and data. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Crypto Transaction Security: A Machine Learning Approach 10

Enhancing Crypto Transaction Security: A Machine Learning Ap...

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Mekala Rani, T.M.S. Suresh, A. Bhargavi, S. Vardhan Reddy, M. Harsha Sai Nikhil, K. Priya, G. Chandu Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh India Andhra Pradesh India

ISBN: (纸本)9798350353778

The project aims to enhance the security of cryptocurrency transactions through the implementation of advanced machine learning methodologies that are RNN(recurrent neural networks),LSTM,VGG16. We aim to create a strong and flexible system that can recognize and stop fraudulent activity in real-time by utilizing machine learning techniques. By utilizing dataset containing the features related to average time between sent and recived transactions, amount involved. This project focuses on creating a proactive defense mechanism that can analyze transaction patterns, detect anomalies, and promptly alert users or intervene to mitigate potential threats. By combining cutting-edge machine learning techniques with the ever-evolving landscape of cryptocurrency security, this project aims to safeguard digital assets and promote trust in the rapidly growing crypto ecosystem. © 2024 IEEE.

关键词： Adversarial machine learning

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An Analyzation Of Data Privacy In Sharing And Storing Data In Cloud Environment 10

An Analyzation Of Data Privacy In Sharing And Storing Data I...

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Subhasreevignani, R.S. Babu, N. Raji, D. Sowmya, K.N. Afreen Zaiba, S. Reddy Abhishek, G. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh India Andhra Pradesh India

ISBN: (纸本)9798350353778

The Wide spread use of cloud storage services and their demonstrated power provide the foundational underpinning for the cloud computing industry rapid development. Nevertheless, a lot of security incidents still occur at the cloud storage layer due to deliberate attacks and management negligence, resulting in significant leaks of critical data. To address the issue, we inculate AES and Cryptographic Pseudo Random Number Generator Algorithms. The proposed standard illustrates the process of dividing the data into segments which is encrypted with aes individually to ensure the strong security for data. By distributing the scattered data segments among several cloud sites, security is improved and the possibility of a single point of failure is decreased. Securing access control mechanisms makes it easy for authorized individuals to access and decode data. © 2024 IEEE.

关键词： Cloud security

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Artificial Intelligence-Based Reference Evapotranspiration Modelling with Minimum Climatic Parameters 26th

Artificial Intelligence-Based Reference Evapotranspiration M...

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Proceedings of the26th International Conference on Hydraulics, Water Resources and Coastal engineering , HYDRO 2021

作者： Chandrasekhar Reddy, K. Civil Engineering Siddharth Institute of Engineering & Technology Andhra Pradesh Puttur517583 India

ISBN: (纸本)9789819919000

Artificial neural networks (ANN), an artificial intelligence-based technology, have yielded numerous favourable water resource and hydrology simulation results. The investigation goal is to find the efficient ANN architecture for estimating reference evapotranspiration with the fewest climate variables. The climatic parameters, namely wind speed (W), relative humidity (RH), air temperature (T), and sunshine hours (S) were used to estimate monthly reference evapotranspiration (ET0). Partial correlation and multiple linear correlations between the climatic parameters and FAO-56 Penman–Monteith reference evapotranspiration (PMET0) were carried out in order to determine the most influential factor by eliminating one factor at a time. The influence of parameters T, S, W, and RH was observed from highest to lowest, respectively. Therefore, the best ANN models to estimate artificial neural network reference evapotranspiration (ANNET0) were developed using climatic parameters as inputs and eliminating one lowest influencing parameter each time. Training of model was done with a portion of data, and the remaining was used for testing the model. Performance indices have been used to assess the ability of the model by correlating the PMET0 and ANNET0. The viability of the generated models was verified by using the numerical indicators (i.e. efficiency coefficient, coefficient of determination, and root mean square error). ANN (1-5-1), ANN (2-5-1), ANN (3-4-1), ANN (4-3-1) with (T), (T, S), (T, S, W), and (T, S, W, RH) as inputs, respectively, were shown to have 89.58%, 94.36%, 95.20%, and 99.44% during the testing period, respectively. Thus, in the area of study and other locations with similar climatic conditions, these ANN models can assess monthly ET0 with adequate accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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