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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Maheswari, K. Elankavi, R. Lahari, Koneti Navitha, Jootoori Manoj, Peta Maheshwar Reddy, Bala Obanna Gari Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350361537

Elastic, on-demand, and completely managed computer system resources and services are some of the highly alluring aspects of cloud computing. Several challenges include not being able to access data entering or leaving cloud services, data theft and misuse, lack of control over access to sensitive data, and advanced threats including distributed denial of service attacks (DDoS) and virtual machine escape. DDoS assaults are among of the most popular. This title demonstrates an innovative architecture that combines the strengths of neural networks and fuzzy neural networks with a well-posed stacking sparse AutoEncoder (AE) for feature learning. The result is an incredibly powerful hybrid that can distinguish between DDoS attack and regular network traffic. This AEF proposal's enhancements result in a smaller network that prevents overfitting by employing a fuzzy expert approach, reduced reconstruction error, and prevented exploding and vanishing gradients. Using performance criteria like detection precision, accuracy, recall, and F1-Score, a comparison between the suggested method and ten cutting-edge techniques will be carried out. To validate the results, experiments will be run on regular NSL-KDD datasets. © 2024 IEEE.

关键词： Denial-of-service attack

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Adaptive Malware Detection In Windows Portable Executables (Wpes) 4

Adaptive Malware Detection In Windows Portable Executables (...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Sathish, A. Babu, N. Bhavani, M. Divya, K. Jhoshna, B. Hemavathi, T. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andra Pradesh India

ISBN: (纸本)9798350354218

It's crucial to comprehend the characteristics of malware variants that enable them to elude malware detection methods as they are created with the intention of avoiding detection by means of a variety of evasion strategies. It is done by a framework designed to generate undetectable malware samples on Windows executable files by incorporating various techniques such as adding benign sections and obfuscating code. Creating undetectable malware variants using a framework can successfully evaded numerous anti-malware programs, showing significantly higher evasiveness than both the original malware and a previously suggested solution. By examining various perturbations and their efficacy, our results contribute to the improvement of malware detection algorithms by providing a better knowledge of the categories to which more evasive malware variants may belong and how they might be created. To create anti-malware programmed that are capable of successfully identifying malware varieties that have not been observed before, experimental findings and observations would be helpful. © 2024 IEEE.

关键词： Cyber attacks

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Small Object Detection & Classification In Cardiac Images For the Improvised Early Diagnosis 4

Small Object Detection & Classification In Cardiac Images Fo...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Geethavani, B. Elankavi, R. Latha, M. Swarna Yugandhar, J. Varma, S. Vinay Pavithra, K. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andra Pradesh India

ISBN: (纸本)9798350354218

Early diagnosis of cardiac abnormalities plays a crucial role in preventing severe cardiovascular diseases. This paper presents a novel approach for detecting and classifying small objects, such as anomalies, in cardiac images to facilitate early diagnosis and intervention. The proposed methodology integrates various image processing and machine learning techniques, including input image preprocessing, edge detection, boundary extraction, KAZE feature extraction, region mapping, morphological analysis, ensemble learning, and convolutional neural network (CNN) classification, followed by decision-making mechanisms. Initially, the input cardiac images undergo preprocessing to enhance quality and reduce noise, followed by edge detection to identify potential regions of interest. Subsequently, boundary extraction techniques are applied to delineate object boundaries for more accurate analysis. KAZE feature extraction is then employed to capture discriminative features from the identified regions. Next, a region mapping approach is utilized to segment and classify small objects within the cardiac images. Morphological analysis is applied to refine the detected regions and improve classification accuracy. An ensemble learning method is then employed to integrate diverse classifiers for enhanced performance. Furthermore, a CNN classifier is trained on the extracted features to classify the detected objects into relevant categories, facilitating automated diagnosis. Finally, a decision-making mechanism is employed to interpret the classification results and provide actionable insights for healthcare professionals. The proposed approach offers a robust solution for early diagnosis of cardiac abnormalities by effectively detecting and classifying small objects in cardiac images. Experimental results demonstrate the efficacy of the proposed methodology in improving diagnostic accuracy and efficiency, thereby contributing to enhanced patient care and prognosis in cardiovascular

关键词： Convolutional neural networks

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A Hybrid IoT and Machine Learning Approach for Crop Recommendation Using a Voting Ensemble Model 2

A Hybrid IoT and Machine Learning Approach for Crop Recommen...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Suresh, A. Geetha Vani, B. Lavanya, M. Kumar, R.G. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering A.P India

ISBN: (纸本)9798350317558

Agriculture is a key component of a nation's economy. In the traditional method, farmers rely on experience to determine the ideal climate and environment for a given crop. technology is advancing, and while the system depends on it to some extent, improvements are still required. This study presents an Internet of Things (IoT) based crop recommendation system that uses Machine learning (ML) to use the value that IoT devices collect from the soil. Initially, we utilized baseline ML classifier models like Support vector machine (SVM), K-Nearest Neighbor(KNN), Random forest (RF), and Random forest (NB).Then we employed an ensemble model to detect the suitable crops. We employed several IoT sensors in our research. The DHT11 sensor collects real-time environmental data, such as temperature and humidity. During crop recording, vital soil parameters like potassium and phosphorus - which are essential for crop growth - are measured using an NPK sensor, a pH sensor, and a rain sensor. These sensors can be seamlessly integrated by the IoT device, offering an effective way to collect crucial agricultural data. The Voting Base Ensemble performs exceptionally well in terms of precision, recall, and F1 score measures. The voting Base Ensemble algorithm improves metrics such as F-score, recall, and precision by up to 98%. A thorough evaluation of soil conditions is made possible by the integration of various sensors, and the application of an ensemble model enables accurate crop recommendations based on environmental factors. © 2024 IEEE.

关键词： Agriculture Crop Recommendation Ensemble IoT Machine Learning

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Synthesis and characterization of PET impregnated agro-medical waste: Novel approach for plastic substitute using hybrid composites 2

Synthesis and characterization of PET impregnated agro-medic...

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2nd International Conference on Advances in Mechanical engineering and Material Science, ICAMEMS 2023

作者： Vinod, B. Suresh, S. Reddy, S. Sunil Kumar Sai Sujith, K. Department of Mechanical Engineering Siddharth Institute of Engineering &Technology AP Puttur51758 India

Composites are environmentally friendly materials made from natural fibers that have lightweight properties and improve the performance of synthetic parts. Medical waste has been identified as one of the biggest challenges recently in India due to COVID-19. More than 70% of medical waste is considered non-hazardous waste, obtained from crushing used surgical face mask (SFM) and surgical gloves (SG) as reinforcement materials chosen in this study. The research aim is to increase the strength and minimize the cost of production of polyethylene terephthalate (PET) matrix by employing hybrid waste fibers as particle reinforcement particles. Five fiber composites of 3% to 12% weight fractions of hybrid fiber (Kevlar Fiber/Glass fiber) and a constant 10% medical waste were fabricated using an injection moulding process to regulate the polymer structure and determine its thermal conductivity. Adding fibers improved mechanical properties such as tensile, flexural, and impact strength. TGA and thermal conductivity were analyzed. Tribological characteristics like a specific wear rate and coefficient of friction were conducted. Morphology is used to identify material behaviour by applying different weight fractions. Adding 9% (KF/GF) and 10% (SFM/SG) particles improves the wear and mechanical properties of the hybrid composite by 52%, respectively. Adding medical and fiber waste particles will improve the interfacial bonding between the matrix material and fillers. It is widely used as a leaf spring in ballistic and automobile applications suspension. Copyright © 2024 Elsevier Ltd.

关键词： Thermal conductivity

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A Progressive Approach of an Augmented Steganography for Network Peers

A Progressive Approach of an Augmented Steganography for Net...

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

作者： Kumar, B. Pavan Monika, N. Sravani, T.C. Sasi Preetham, C. Varuntej Reddy, V. Kumar Gupta, Manish Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh Chittoor India

ISBN: (纸本)9798350378092

In order to interface and exchange data with various devices and frameworks via the internet, real objects or things implanted with sensors, programming, and other advancements are referred to as the Internet of Things (IoT). While there are IoT devices [13] that are publicly accessible to everyone in the company, it is still important to understand the risks associated with digital attacks and security threats. Before a message is sent, it is entirely converted from plain to encoded in cryptography. The reverse also applies to plaining messages received following exchanges of letters. One method of hiding sensitive data is to embed it within a text, image, video, or sound file via a technique called steganography. Information can be hidden using bits that address comparable variety pixels that are repeatedly rehashed in an image record. A picture record that seems identical to the original image but contains 'commotion' samples of typical, decoded information will be produced by subtly applying the encoded information to this repeating information. The goal of this project is to jumble up the information on IoT networks by using an image information concealing technique to hide the message inside a picture record. We plan to integrate the use of convolutional neural networks with standard image information hiding techniques in order to significantly increase the amount of data that can be transmitted through an image. We will investigate various convolutional bounds in order to achieve the highest payload. The information will be encrypted and unscrambled using the recently developed profound learning computation. As a result, the convolutional organizations involved in this project will be set up to increase the payload of the data that needs to be encrypted and safely decoded in order to view the initial message. © 2024 IEEE.

关键词： Convolution

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Enhancing Cyber Supply Chain Security through Predictive Analytics 4

Enhancing Cyber Supply Chain Security through Predictive Ana...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Sathish, A. Kumar, R.G. Lakshmi, P. Venkata Sathvika, P. Sirisha, R. Sreedhar, B. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andra Pradesh Chittoor India

ISBN: (纸本)9798350354218

In the domain of cyber security, supporting the flexibility of supply chains against potential dangers is vital. This review dives into the domain of prescient examination, utilizing progressed AI methods including CatBoost, XGBoost, and Stacking Classifier. Utilizing a complete dataset including a plenty of elements like IsBeta, RtpStateBitfield, and AV-ProductStatesIdentifier, among others, the examination expects to foster strong models fit for estimating digital dangers. By examining markers like HasDetections and Wdft-IsGamer, the models endeavor to distinguish designs demonstrative of potential security breaks. Through fastidious examination of qualities like Firewall status and Census-OSBuildNumber, the review tries to give significant experiences to upgrading digital store network security. By amalgamating different classifiers, this exploration tries to offer a diverse way to deal with danger location, consequently strengthening the digital foundation against arising gambles and guaranteeing the honesty of inventory network organizations. © 2024 IEEE.

关键词： Predictive analytics

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Effect of graphene oxide and nano silica on mechanical and durability properties of cement mortar

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Materials Today: Proceedings 2022年 60卷 1042-1050页

作者： Vasudevareddy, Pothala Chandrasekhar Reddy, K. Department of Civil Engineering Siddharth Institute of Engineering & Technology Puttur517583 India

Nowadays, nano-technology has already influenced many fields, including civil engineering also. Cementitious composites contain various nanomaterials that have considered as novel materials with improved microstructure, mechanical and durability properties. Recently graphene oxide and nano silica have appeared as the most promising nano-materials for civil engineering applications. However, graphene oxide and nano silica addition in the cement mortar improves the compressive strength, split tensile strength and flexural strength. Ultra-high resolution field emission scanning electron microscopy (FE-SEM) and X-ray diffraction (XRD) analysis are used to see the fracture surface of samples containing 0.07% GO and 4.5% of NS to the weight of cement indicated that GO and NS flakes were well dispersed in the cement mortar matrix and no aggregates were observed. The compressive, bending, and split tensile strength of cement mortar, which contains 0.07% GO and 4.5% of NS by weight of cement after 28 days of curing, is raised by 22.25%, 38.77%, and 10.25%, respectively, as compared to regular cement mortar. These durability studies, such as acid resistance and salt resistance, the weight loss was found in acid, and salt immersion addition of GO&NS shows good resistance in the cement matrix. In addition, SEM analysis has shown the growth of calcium silicate hydrates (C-S-H Gel) in GO and NS cement mortar has compared with normal cement mortar. © 2022

关键词： Compressive strength

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Enhancing Public Safety through Real-time Weapon Detection: A Deep Learning Approach 3

Enhancing Public Safety through Real-time Weapon Detection: ...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Dass, J. Maria Arockia Suresh, A. Swathi, K. Shalini, Paidimuddala Sinha, N A Yaswanth Kotholla Uday Kiran, U. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh Chittoor India

ISBN: (纸本)9798350354171

Date In response to the imperative need for mitigating criminal activities and ensuring public safety, this research proposes a novel approach leveraging deep learning techniques for real-time weapon detection. In contemporary society, criminal acts pose significant threats to both individuals and the broader community, necessitating proactive measures to counter such offenses swiftly. By harnessing advanced technologies, particularly deep learning algorithms like YOLOv7-tiny, this study intend to create a robust system capable of identifying weapons in surveillance footage captured by CCTV cameras. The project begins with the meticulous collection of datasets from reputable research sources, ensuring diverse and comprehensive samples for training the deep learning model. Subsequently, these datasets undergo rigorous pre-processing steps to optimize them for training, including resizing images to the required format. To facilitate accurate annotation and labelling of the dataset, advanced tools like Robo flow software are employed, streamlining the process and enhancing efficiency. Upon completion of the pre-processing phase, the YOLOv7-tiny algorithm is deployed for training the model on the annotated datasets. Through iterative training and validation processes, the model is fine-tuned to achieve optimal performance in weapon detection tasks. Once trained, the model is capable of swiftly and accurately identifying weapons in real-time surveillance footage. This project presents an effective solution for real-time weapon detection in public spaces, leveraging deep learning algorithms and advanced software tools. © 2024 IEEE.

关键词： Image annotation

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InterOperative Biopsy Site Relocalization in Endoscope for Gastrointestinal tract using Deep Learning 5

InterOperative Biopsy Site Relocalization in Endoscope for G...

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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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