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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Rajesh, S.M. Prabha, R. Gitam Deemed to Be University Department of Computer Science Engineering Bengaluru India Dr. Ambedkar Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665474511

With the origin of the Internet of Things (IoT),massive real-time applications are evolving, but in this cyber world, securing the data of those devices or users is to be focussed more on. Intelligent services are the base components of IoT applications, such as intelligent systems, smart homes, smart cities, smart cars, Etc. An unfold challenge is securing the large amount of data generated and transmitted over the network in the IoT ecosystem by resisting cyber-attacks and adopting the efficient cryptographic method for a resource-constrained environment. This paper focuses on the exhaustive work carried out on user/device identity and a lightweight cryptographic viable solution for the IoT ecosystem through ECC (Elliptic-curve cryptography). The study assures the secure transmission of data and authentication of device/user using various security strategies of ECC for real-time IoT applications. A systematic survey of the previous works is conducted to determine the critical aspects of designing ECC-based authenticated and secured transmission solutions for the IoT ecosystem. The outcome of this study is essential components of ECC, requirements, and solution architecture of ECC for various security aspects in the IoT ecosystem to ensure user/device privacy and secured data transmission and its challenges are addressed. © 2023 IEEE.

关键词： Authentication

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Exploratory Data Analysis using Autoviz for Machine Learning Classification Problem

Exploratory Data Analysis using Autoviz for Machine Learning...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Guru Prasad, M.S. Praveen Gujjar, J. Kotiyal, Arnav Prasanna Kumar, H.R. Devadas, Raghavendra M Jahan, Afsar Department of Computer Science and Engg. Dehradun India Faculty of Management Studies Bengaluru India Visvesvaraya Technological University PESITM Dept. of Information Science & Engg. Shivamogga India Gitam School of Technology Department of Computer Science and Engineering Gitam University Bengaluru India Department of Computer Application Dehradun India

ISBN: (纸本)9798350376470

As the field of machine learning continues to advance, the importance of effective exploratory data analysis (EDA) cannot be overstated, especially in the context of classification problems. This research paper introduces an innovative and automated approach to exploratory data analysis using Autoviz, a powerful Python library designed to streamline the visualization process for machine learning datasets. The study aims to demonstrate the efficacy of Autoviz in meaningful insights, facilitating feature understanding, and supporting data preprocessing for classification tasks. Furthermore, the paper discusses how Autoviz assists in feature selection by highlighting the relevance and importance of each feature about the target variable. This feature-centric analysis contributes to optimizing machine learning models by guiding the selection of relevant input features, thereby improving model accuracy and interpretability. Autoviz is a Python library designed to automate the exploratory data analysis (EDA) process for machine learning tasks. It is specifically tailored to generate a variety of visualizations that aid in understanding the structure and characteristics of a dataset. The results indicate that Autoviz significantly reduces the time and effort required for exploratory data analysis while maintaining the quality and depth of insights obtained. The differences between Autoviz and Pycaret is discussed in this paper. It has been observed that AutoViz automates the process of exploring and analyzing data, while PyCaret is designed for the comparison, construction, and tuning of machine learning models. © 2024 IEEE.

关键词： Adversarial machine learning

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A Novel Drone-Station Matching model in Smart Cities based on Strict Preferences

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无人系统（英文） 2023年第3期11卷 261-271页

作者： Debolina Nath Anjan Bandyopadhyay Ankit Rana Tarek Gaber Aboul Ella Hassanien Department of Computer Science and Engineering Amity University Kolkata KolkataWest BengalIndia Department of Computer Science and Engineering Gitam University BangaloreKarnatakaIndia School of Science Engineeringand Environment University of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailiaEgypt Scientific Research Group in Egypt(SRGE) Egypt Faculty of Computer and Artificial Intelligence Cairo UniversityCairoEgypt Scientific Research Group in Egypt(SRGE) Egypt

There has been a considerable increase in the use of drones,or unmanned aerial vehicles(UAVs),in recent times,for a wide variety of purposes such as security,surveillance,delivery,search and rescue operations,penetration of inaccessible or unsafe areas,*** increasing number of drones working in an area poses a challenge to finding a suitable charging or resting station for each drone after completing its task or when it goes low on its *** classical methodology followed by drones is to return to their pre-assigned charging station every time it requires a *** approach is found to be inefficient as it leads to an unnecessary waste of time as well as power,which could be easily saved if the drone is allotted a nearby charging station that is ***,we propose a drone-allocation model based on a preference matching algorithm where the drones will be allotted the nearest available station to land if the station is *** problem is modeled as three entities:Drones,system controllers and charging *** matching algorithm was then used to design a Drone-Station Matching *** simulation results of our proposed model showed that there would be considerably less power consumption and more time saving over the conventional *** would save its travel time and power and ensure more efficient use of the drone.

关键词： Unmanned Aerial Vehicle(UAV) stable matching resource allocation smart city matching algorithm

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Credit Card Fraud Detection Using Machine Learning 2

Credit Card Fraud Detection Using Machine Learning

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Peneti, Subhashini Rama Krishna, S. Kiran, Ajmeera Tripathy, Hrudaya Kumar Mlr Institute of Technology Department of Computer Science & Information Technology Hyderabad India Gitam University Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Kalinga Institute of Industrial Technology Deemed to Be University India

ISBN: (纸本)9798350370188

India's banking sector has grown rapidly in modern times. With increased bank functioning, frauds skyrocket. Debit cards, Internet Banking, and ATM cards are among the financial services offered today. These services attracted many new customers worldwide. We present a Machine Learning technique to detect credit card fraud. The global use of credit cards is expanding significantly. Thus, the bank server can track these transactions using Machine Learning. These algorithms detect or anticipate transaction fraud. Each transaction has a few functions in the dataset. We should classify and identify fraudulent transactions from those transactions. The work suggests using Machine Learning calculation for this. © 2024 IEEE.

关键词： Crime

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Detection and Identification of Lung Cancer Using an Improvised CNN Model: A Novel Approach to Assist Doctors in Diagnosing Lung Cancer

Detection and Identification of Lung Cancer Using an Improvi...

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International Conference on Business Intelligence and Information Technology, BIIT 2022

作者： Mohammed, Sabah Govathoti, Sudeepthi Satyanarayana, K.V. Joshua, Eali Stephen Neal Department of Computer Science Lakehead University 955 Oliver Road Thunder BayONP7B 5E1 Canada Department of CSE GITAM School of Technology GITAM Deemed-to-be-University Telangana Hyderabad India Department of Computer Science and Engineering Raghu Engineering College Andhra Pradesh Visakhapatnam India Department of Computer Science Engineering GITAM School of Technology GITAM Deemed-to-be-University Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9789819934157

Conditions that affect the lungs and make breathing difficult are respiratory ailments. Lung cancer is a disease that affects people all over the globe. The greater the number of individuals aware of them, the better. The likelihood of surviving lung cancer jumps from 14 to 49% if the disease is detected early. Even though CT scans are far more accurate than X-rays, a comprehensive diagnosis requires several scans. The development and testing of a deep neural network for identifying lung cancer in CT images are carried out here. The CNN, DenseNet software was used to determine if the lung image was normal or malignant. This was accomplished via the use of adaptive boosting. There are 201 photographs of lungs in the research collection. Only 15% of the pictures are chosen for testing and classification, while the rest are discarded. According to the testing results, the proposed approach is 95.85% accurate. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computerized tomography

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Machine Learning Mastery in Cardiovascular Risk Assessment 1

Machine Learning Mastery in Cardiovascular Risk Assessment

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Gandhimathi, Suma Kamalesh Rajesh, S.M. Ghamya, K. Lohitha, A. Lakshmi Vinitha, A. Reddy, C. Surya Prakash VIT-AP University School of Computer Science and Engineering Amaravathi India GITAM University-Bengaluru Campus GITAM School of Technology Department of CSE Bengaluru India Tirupati India Sree Vidyanikethan Engineering College Department of CSE Tirupati India

ISBN: (纸本)9798350356816

Coronary illness stays a basic general well-being concern, requiring progressed prescient instruments for opportune determination and intercession. It is the maincause of death universally, liable for almost 18million passings every year. Early recognition and intercession are significant for lessening CVD mortality. AI has arisen as an integral asset for anticipating coronary illness risk, offering expected benefits for customized medication and further developed medical service results. Data preprocessing, feature selection, model training, and evaluation are all covered in this review of ML-based heart disease prediction systems. We talk about the logistic regression, decision trees, random forests, support vector machines (SVMs), naive Bayes, k-means, and artificial neural networks (ANNs) ML algorithms used to predict heart disease. We additionally feature the significance of interpretability and logic in ML models for clinical navigation. At last, we talk about future directions in ML-based coronary illness expectation, accentuating the capability of deep learning and hybrid models for upgrading prescient precision and clinical importance. © 2024 IEEE.

关键词： Risk assessment

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Lightweight Privacy Preserving Framework at Edge Layer in IoT 1

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10th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2022

作者： Agrawal, Kavita Chittineni, Suresh Reddy, P. V. G. D. Prasad Subhadra, K. Diaz, Elizabeth D. Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering GITAM Deemed to be University Vishakhapatnam India Department of Computer Science and Systems Engineering Andhra University Vishakhapatnam India Department of Computer Science and Engineering University of Texas Arlington United States

ISBN: (数字)9789811975240

ISBN: (纸本)9789811975233

The Internet of Things is helping us to make our lives comfortable. IoT devices used here are resource-constrained in terms of memory and CPU as well as they lack basic security design. When everyone is using IoT, there are high chances of their personal data getting exposed to the outside world. For real-time application, data need to be processed at the edge layer directly instead of doing the entire processing and analysis at cloud. Privacy preserving at the edge layer in the Internet of Things is a challenging task. We proposed a lightweight privacy preserving framework that uses the concept of blockchain, zero knowledge proof algorithm, principal component analysis, and digital signatures. Here for the mining process in blockchain, Trust concept is being used instead of Proof of Work (POW) and Proof of Stake (POS) as it consumes fewer resources and our edge layer is resource constrained. This could be a feasible solution and can be deployed at the edge layer in the Internet of Things for preserving privacy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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MOPSO-driven optimization for sustainable retrofitting: balancing time, cost, and environmental impacts

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Asian Journal of Civil engineering 2025年第5期26卷 2165-2178页

作者： Singh, Rekha Sangeeta, Viswanadham Manjunath, T.C. Singh, Prachi Bishoyi, Sasanka Sekhar Pati, Aditya Kumar Department of Civil Engineering Raj Kumar Goel Institute of Technology Ghaziabad India Computer Science and Engineering GITAM Deemed to Be University Visakhapatnam India Department of Computer Science & Engineering Rajarajeswari College of Engineering Bengaluru India Department of Civil Engineering Madhav Institute of Technology and Science Gwalior India Department of Mathematics NIST University Berhampur India Department of Mathematics Centurion University of Technology and Management Gajapati R. Sitapur India

Retrofitting projects play a critical role in enhancing the sustainability of existing structures, yet balancing time, cost, and environmental impact remains a significant challenge for decision-makers. This study introduces a Multi-Objective Particle Swarm Optimization (MOPSO) approach to achieve optimal trade-offs among these competing objectives. By leveraging MOPSO’s capability to explore Pareto-efficient solutions, the research provides a robust framework for sustainable decision-making in retrofitting projects. The model evaluates project scenarios based on key metrics such as time efficiency, cost minimization, and reduced carbon emissions. Through the application of MOPSO, the study generates a range of viable solutions, enabling project managers to make informed decisions tailored to specific sustainability goals. A case study is conducted to validate the model’s effectiveness, comparing its performance with conventional optimization techniques. The results demonstrate that MOPSO excels in balancing multiple objectives, delivering superior outcomes in sustainability metrics. This research contributes to the advancement of sustainable construction practices by offering a practical tool for optimizing retrofitting projects in alignment with environmental and economic priorities. The findings provide valuable insights for practitioners seeking to integrate sustainability into decision-making processes, addressing the pressing need for environmentally conscious infrastructure development. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： MOPSO Multi Objective optimization Sustainable construction practices Sustainable retrofitting Time-cost-environmental trade-off

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Blockchain in Evolving Computing Paradigms: A Beginner's Guide for Review and Future Directions 11

Blockchain in Evolving Computing Paradigms: A Beginner's Gui...

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11th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2023

作者： Mahapatra, Abhijeet Mishra, Kaushik Majhi, Santosh Kumar Pradhan, Rosy Veer Surendra Sai University of Technology Department of Computer Science and Engineering Odisha Burla India GITAM Deemed to be University Department of Computer Science and Engineering Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9798350326147

The evolvement of different computing paradigms as complementary to Cloud computing has transfigured the world into a smarter place. This extension of Cloud computing has been believed as cutting-edge technology to improve the processing power of the resource constraint computing nodes. However, due to location unawareness, the security of the data plays a significant role both for the service provider and consumers. This challenge led to the development of blockchain technology as a prominent solution for providing security and privacy. This paper aims to briefly survey blockchain technology and its application as a solution in evolving computing paradigms. In addition, a secured task offloading framework using blockchain in the loT-Fog-Cloud paradigm has also been proposed. Furthermore, this paper investigates some of the open issues and challenges in the aforementioned computing paradigms and proposes future scope for the above challenges. This survey elucidates the vision of blockchain technology in these paradigms and would be a helping hand for researchers and academicians for a transparent picture of this newer technology. © 2023 IEEE.

关键词： Edge computing

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