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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Haswanth, Gedela Mouli, Chava Chandra Swathi, Kailasam Manoj, Gottam Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350361155

When potholes are present, there is a tremendous threat to the safety of road equipment and driving. This study proposes a unique approach that employs Convolutional Neural Networks (CNNs) to automate the process of recognising potholes in road visualisations. Convolutional neural network (CNN) model development and training, input preparation, and model correctness verification are the phases in the process. The findings reveal that potholes can be appropriately found using the recommended technology, which makes it practical to implement safer and better road repair procedures. © 2024 IEEE.

关键词： Convolutional neural networks

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Advancements in Disease Diagnosis and Prediction Leveraging Insights Extraction and Computational Learning Techniques 3

Advancements in Disease Diagnosis and Prediction Leveraging ...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Konda, Niveditha Vidya, Koprada Pydi Durga Lakshmi Sri Chavalam, Varshith Vadde, Karthikeya Rao, K B Venkata Brahma Koneru Lakshmaiah Education Foundation Department Of Computer Science and Engineering Andhra Pradesh Vijayawada India

ISBN: (纸本)9798350372120

Technology has revolutionized the health domain largely. Accurate and on-time analysis of any health-related problem It is crucial for the prevention and treatment of the ailment. The traditional way of diagnosis may in the event of a severe illness, it may not prove to be satisfactory. This project aims to design a diagnostic framework for different diseases based on their symptoms. This system utilizes data mining methodologies including classification techniquesin order to achieve such a mode and also ML algorithms for prediction of any diseases can help in a more accurate diagnosis than the conventional method. We have introduced a system for disease prediction that employs several machine learning algorithms. The dataset contains information on symptoms, age, blood group, and gender of individuals, which is used for processing by the diagnosis system. The output of the system is the disease. Here, the SympoDx algorithm, which incorporates Random Forest, Decision Tree, KNN, and Support Vector Classifier, is employed for classification tasks and computes the probabilities of diseases. Following the results, patients can contact the doctor accordingly for further treatment. © 2024 IEEE.

关键词： Forecasting

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Mucormycosis Predictive Analysis Using Machine Learning Techniques

Mucormycosis Predictive Analysis Using Machine Learning Tech...

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International Conference on Computing and Machine Learning, CML 2024

作者： Durga Venkata Sri, Sola Mohana Nikitha Kumari, Chakka Ranga Keerthana, Mannava Srija, Gangarapu Akhter Farooqui, Nafees Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeshwaram India

ISBN: (纸本)9789819775484

The emergence of mucormycosis, commonly known as black fungus, as a post-COVID-19 complication has led to a surge in morbidity and mortality rates across the globe. In this research paper, we present a comprehensive analysis utilizing machine learning techniques, specifically Support Vector Machine (SVM) and Logistic Regression (LR), to discern predictive patterns and risk factors associated with the incidence and progression of mucormycosis. The study incorporates a dataset comprising clinical records, demographic information, comorbidities, medication history, and laboratory findings of patients diagnosed with mucormycosis post-COVID-19 infection. Feature engineering and selection methods are employed to extract relevant variables, while SVM and LR models are trained and validated using a cross-validation technique to predict the likelihood and severity of mucormycosis infection. Our findings reveal significant predictive capabilities of SVM and LR models in identifying high-risk individuals predisposed to mucormycosis, showcasing notable accuracies and area under the curve (AUC) scores. Furthermore, through the interpretation of feature importance, this study unveils crucial risk factors contributing to the susceptibility and severity of mucormycosis, providing valuable insights for healthcare professionals in early identification and proactive management strategies. This research contributes to the ongoing efforts in understanding and combating the rise of mucormycosis cases post-COVID-19, emphasizing the potential of machine learning models in facilitating timely interventions and improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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CNN Models Transfer Learning Based Ship Classification for Intelligent Ship Routing

CNN Models Transfer Learning Based Ship Classification for I...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Tiwari, Brij Kishor Munagala, Rohini Sai Mannarugudi, Sai Dheeraj Vignan's Foundation for Science Technology and Research Department of Advanced Computer Science and Engineering Guntur522213 India

ISBN: (纸本)9798350376876

Collision free autonomous navigation is challenging area of interest for the shipping industry, and an accurately classifying the ships according to their size and type classes is a crucial undertaking in marine operations. Accurate prediction and identification will improve the maritime security and navigation safety. However, automatic ship classification is a difficult subject because of the wide variety of ship designs and the complexity of visual data. We developed a deep learning model using transfer learning from pre-trained models in order to classify five different kinds of ships. We performed the experiment using different pre-trained convolution neural network model of the deep learning. In the transfer learning we considered the training of the dataset for 100 epochs. In this study, we investigated four deep learning model ResNet 50, VGG 16, VGG 19, and Inception V3. We carried out the identical procedure for training of each model by removing the top layer of pre-trained model and adding three dense layers for the top layers. The neuron counts are 1024, 512, 256 respectively followed by dropout layer of 0.5. Results of our model shows the maximum obtained accuracies are 86.66 % and from these we can observed that the developed model can be used for ship identification and classification and to take the decisions for collision free and smooth navigation. © 2024 IEEE.

关键词： Transfer learning

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Predictive Modeling for Cardiovascular Health: Unveiling Insights, Enhancing Interpretability, and Charting Future Trajectories 8

Predictive Modeling for Cardiovascular Health: Unveiling Ins...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Lasya, Katakam Naga Venkata Anjani Siva Sri Meghana, Peram Pranathi, Kota Iswarya, Gontla Venkata Velmurugan, A.K. Kumar, A.Dinesh Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350386578

Heart disease is still a major global health concern, which makes the creation of reliable and treatment necessary. This research study analyzes a large dataset that includes cardiovascular risk variables by using cutting-edge technologies by considering clinical measurements, lifestyle factors, and demographic data. The study uses well-known Python tools for data preprocessing, exploratory data analysis (EDA), and predictive modeling, including NumPy, pandas, and scikit-learn. Statistical and visual techniques are used in exploratory data analysis to find patterns and relationships present in the dataset. The relationships between gender, forms of chest discomfort, and the existence is investigated by using chi-squared testing and visualization approaches. The significance of several characteristics are also analyzed in this study. To determine possible risk factors, the relationship between categorical variables is measured using the Cramer's V statistic. Correlation matrices are also utilized to investigate the connections between numerical variables. An extensive analysis of feature importance is also included in the conclusion, highlighting the factors that have a major impact on the prognosis. Bar charts are used as visualization tools to reveal the significance of different parameters in the prediction models. © 2024 IEEE.

关键词： Electronic health record

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ENERGY EFFICIENT SECURITY PROTOCOL FOR HEALTHCARE INTERNET OF THINGS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第12期83卷 65-78页

作者： Babu, Ravi Mahesh Malluvalasa, S.N. Lakshmi Devi, M. Kalpana Karthik, Sagirala Satya Rakesh, M. Prasad, Satamraju Krishna 522508 India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Green Fields Andhra Pradesh Vaddeswaram522302 India Department of Electronics and Communication Engineering Department of Computer Science and Engineering Newton’s Institute of Engineering Guntur Andhra Pradesh Macherla India

The Energy Efficient Security for Internet of Things (EESP-IoT) protocol aims to achieve a harmonious equilibrium between energy preservation, security, and network optimization within the ever-changing domain of the internet of things (IoT). EESP-IoT coordinates the actions of IoT devices where each device independently optimizes its energy usage throughout the network to ensure stability and equity. Adaptive techniques such as power optimization, alternating between sleep and wake modes, and energy harvesting reduce energy inefficiency, while assuring security by using lightweight specifically designed cryptographic techniques. By including energy and security considerations, the protocol achieves both efficiency and resilience against a range of security threats. EESP-IoT exhibited exceptional energy conservation skills, while maintaining comparable levels of throughput and latency. The protocol underwent testing in a simulated environment to assess its ability to handle a large number of devices and its effectiveness in various network circumstances. The findings confirmed that EESP-IoT is capable of maintaining a high degree of network efficiency and strong security procedures, while considerably prolonging the lifespan of IoT devices. © 2024 by Begell House, Inc.

关键词： Energy utilization

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Procedural Content Generation for Racing Games

Procedural Content Generation for Racing Games

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Ram, M. Raghavendra Jadhav, Amey Reddy, V. Uma Maheshwar Tripathy, Asesh Kumar Kancharla, K. Yogiswar Puppala, Ramya Koneru Lakshmaiah Education Foundation Department of Computer science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798331540685

Procedural Content Generation (PCG) is a critical element of contemporary game design, facilitating the creation of dynamic and diverse game worlds. This paper explores the application of PCG in racing games, focusing on algorithms, techniques, and design principles that contribute to the creation of immersive and challenging racing experiences. It also discusses challenges and ethical considerations associated with PCG in racing games and explores future research directions in this field. © 2024 IEEE.

关键词： Game design

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Securing the Cloud through the Implementation of Encryption Algorithms- A Comprehensive Study 3

Securing the Cloud through the Implementation of Encryption ...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Manikanta, Maturi Lohith Naga Venkata Sai Poojith, Chandu Sai Prakash, Modem Bhanu Sathwik, Vagu Bharadwaj Mothukuri, Radha Bulla, Suneetha Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350375190

Cloud computing has become an integral part of modern technology, offering scalable resources and services. However, the security of data and services in the cloud is a paramount concern. To address this, research explores a comprehensive study of various security algorithms employed in cloud computing environments. This research study reviews the mathematical foundations, working principles, and practical applications of these algorithms, focusing on their strengths and weaknesses. This research aims to provide valuable insights into the landscape of cloud security, assisting cloud service providers and users in making informed decisions to protect their data and operations in the cloud. The main goal of this research is to assess the effectiveness of different data encryption techniques in relation to cloud security. This study evaluates the effectiveness of four popular encryption techniques to see which one works best in cloud contexts and for possible industry uses. Those four techniques are AES, DES, SHA-256, ElGamal Numerous advantages come with data encryption, including improved management control, economic effectiveness, and data and communication security for distant workers. By advancing the knowledge of cloud data security, this study helps businesses to make wise decisions about data protection. © 2024 IEEE.

关键词： Cloud security

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Encryption in the Cloud: Analysing Deep into the Layers of Security 4

Encryption in the Cloud: Analysing Deep into the Layers of S...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Kumar, K. Sathish Reddy, Palle Jaswanth Gnanateja, P. Babu, Chellu Saidulu Ande, Pavan Kumar Sai, N. Raghavendra Koneru Lakshmaiah Education Foundation Department of Computer science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350385793

This study focuses on the intricate domain of cloud security, highlighting encryption's crucial role in safeguarding data integrity. Beginning with a comprehensive introduction to cloud encryption, this study explores the fundamental concepts and contemporary encryption methodologies. Examining the shared responsibility model, complexities surrounding key management, data residency challenges, and the emerging threat of insider breaches, it tackles cloud security challenges. Analysing deep into the layers of cloud encryption, this study explores the strategies used for securing the data in transit, at rest, and within applications. Empirical case studies illustrate the successful implementations and security breaches, offering valuable insights. Additionally, it addresses compliance, outlines key management practices, discusses about cloud encryption deployment, and provides guidance on integrating it with existing security protocols. Concluding with an analysis on the potential future directions, this study serves as a comprehensive reference for security professionals, IT experts, and decision-makers navigating the intricate landscape of cloud security, for enhancing the security of cloud-based environments. © 2024 IEEE.

关键词： Cloud security

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Cyber Deceptions: Unveiling the Threat of Deepfakes in Digital Realms 6

Cyber Deceptions: Unveiling the Threat of Deepfakes in Digit...

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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Saha, Mainak Pagadala, Pavan Kumar Basu, Miriyala Trinath Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad500075 India

ISBN: (纸本)9798331505790

In today's digitally interconnected world, the widespread use of deepfake technology poses a significant threat to cybersecurity and digital trust. The development of deepfake has advanced recently, making them easier and more realistic to create. This study explains the deterioration of trust, propagation of misinformation, and potential for malicious exploitation across various domains, including politics, business, and personal relationships, by conducting a thorough analysis of deepfake creation techniques and their impact on digital communication. Additionally, it also examines existing countermeasures and detection techniques for mitigating the risks posed by deepfakes, emphasizing the importance of interdisciplinary collaborations and proactive measures to ensure digital authenticity and trust in the face of evolving cyber frauds. © 2024 IEEE.

关键词： Cyber attacks

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