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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Suganya, R. Prakash, B. Srm Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Kattankulathur India Srm Institute of Science and Technology Department of Computing Technologies Tamilnadu Kattankulathur India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

Vehicular Ad Hoc Networks (VANETs) are important for intelligent transportation systems, permitting vehicles to communicate with one another and nearby infrastructure in order to enhance road safety, traffic management and infotainment services. But the VANET communication is a high energy consumption technology, which may become a big challenge of large-scale power efficiency being in some real scenarios where huge number vehicles are needed to implement such V2I or V2V services sustaining. This paper conducts a analysis of different transmission stratagems for energy-efficient communication strategies among vehicles in VANETs, along with the focus to incorporate deep learning accelerators into an optimized architecture framework for enhancing communication processes. The paper presents an overview of VANET architecture and communication protocols, and then reviews the techniques to save energy in a VANET including efficient mechanisms for Transmission Power Control (TPC), some energy-aware routing protocols, as well as data aggregation realizations. Next, the paper introduces deep learning models that can adaptively optimize VANET transmission by forecasting network states and updating strategies on-the-fly. This analysis emphasizes hybrid solutions where deep learning is combined with traditional communication strategies to improve the energy efficiency apparent on a system level. Finally addresses the current challenges in the field and the need for advanced AI-driven techniques. This bibliometric analysis provides a comprehensive overview of energy-efficient communication strategies in VANETs, offering insights into how deep learning can contribute to the development of more sustainable vehicular networks. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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IoT-Supported Indoor Navigation System Using Machine Learning Technique 12th

IoT-Supported Indoor Navigation System Using Machine Learnin...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Bhuvana, S. Vidhya, K. Sudharsan, S. Suhita, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819788354

Indoor Navigation System (INS) supports seamless movement of objects within confined spaces in smart environments. In this paper, a novel INS that relies on ESP32-based Received Signal Strength Indication (RSSI) measurements for precise estimation of distance using K-Nearest Neighbour (K-NN) algorithm is proposed. The system architecture includes three ESP32 nodes functioning as clients, and ESP8266 combined with ESP32 acting as a server. To overcome the limitations of Global Positioning System (GPS) within an indoor environment, Machine Learning (ML)-based prediction models are employed to estimate distance between nodes and predict user locations along with the distance between source and destination. This approach not only addresses the challenges posed by GPS in indoor settings, but also shows the potential of ESP32 devices and predictive algorithms for enhancing navigation accuracy and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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Applications of Machine Learning in Cloud Forensics

Applications of Machine Learning in Cloud Forensics

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Theertani, Malluri Reddy Valeti, Neha Solanki, Darshini Kumar, Kakelli Anil Vellore Institute of Technology Vellore Computer Science and Engineering Vellore India Vellore Institute of Technology Vellore Sr. School of Computer Science Engineering Vellore India

ISBN: (纸本)9798350333602

Cloud computing has recently experienced rapid expansion, making it a major target for cybercrimes. Though cloud computing has many advantages, there are also substantial security issues related to confidentiality, integrity, privacy, and availability. Being a relatively new discipline, cloud forensics faces numerous challenges and issues with interpreting and analyzing data. Forensic investigators and law enforcement confront numerous difficulties in data collection, data protection, and evidence access. Several sophisticated models have been proposed in recent years aiming to accelerate the entire investigation process or address several issues that arise frequently in forensic investigations. This review paper seeks to comprehend the significance of various machine learning models which would help cloud forensics advance significantly. It also provides a detail information on cloud forensics as well as the research trends that have been going on in this field over the past few years. A detailed comparative analysis of different approaches of machine learning on development of cloud forensics has been presented in this research study. © 2023 IEEE.

关键词： Data acquisition

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Classification of Disease in Potato Leaves using Deep Learning Approach 1

Classification of Disease in Potato Leaves using Deep Learni...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Prakash, Vallakatti Bhanu Shirisha, Dhanavath Reddy, Gopu Suryaprakash Sowjanya, M. Hariharan, Shanmugasundaram Kukreja, Vinay Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350372748

Agriculture is the backbone for every country in the world. and Farmers are the ones who predominantly take care of the crops and fields. In the present paper, we mainly focus on the diseases that are occurring in the farm fields mainly on potato leaves. Basically the leaf diseases occur because of the sudden climate change, improper nourishment, over usage of fertilizers, which involves high amount of NPK (Nitrogen, Phosphorus, Potassium). This study proposes a novel algorithm, which will help the farmers to detect the leaf diseases in the early stages, and eradicating them before growing. For this CNN (Convolutional Neural Network) algorithm, in order to detect the diseases that are present on the leaves by analyzing the leaves, perform feature extraction and provides recommendations and suggestions in order to prevent the disease. It mainly analyses the object, extracts the features and detect the diseases. This will help to eliminate the financial losses for the farmers, helps in yielding the more production of crops and also eradicate the disease completely from its roots, so that it won't occur next time. Additionally, this study incorporates the recommendation system in two distinct ways: first, it bases the recommendation on crop cultivation prior to the commencement of agriculture, and second, it bases the recommendation on crop curing following agriculture, that is, throughout the crop's growth phase. © 2024 IEEE.

关键词： Crops

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Reliable Informational Data and Secured Deviation Notification over Networks Using IOT

Reliable Informational Data and Secured Deviation Notificati...

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2023 IEEE International Conference on Recent Trends in Electronics and Communication, ICRTEC 2023

作者： Vyshali Rao, K.P. Ramisetty, Srividya Dhanalakshmi, M. Cmr Institute of Technology Department of Information Science and Engineering Bangalore India New Horizon College of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798350396195

The need of uninterrupted services from servers, networks and databases in the current environment relying completely on computing technologies. This paper implements Automatic database duplication with one master database server and multiple slave database servers. The proposed system ensures slave database server replicates data of master server which can be used to restore services in case master server fails. Existing method does this with human intervention which is a time consuming process. Therefore, the proposed system ensures to implement automatic failover capabilities with relatively low downtime and notifying the same using smart alert system. The alert mechanism includes sending an encrypted message notifying the master failure, any deviation from normal working of automatic up gradation software. The messages exchanged are AES encrypted using Message Queuing Telemetry Transport (MQTT) as IOT protocol. The proposed system implemented with Arduino, Sensors and java keycards. Results prove automatic failover utilizes less resources and system downtime is almost zero compared to manual failover. Analysis of performance, System downtime, time estimation is carried out to check the time required for key generation and encryption. © 2023 IEEE.

关键词： Database systems

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Analysis of Resource Utilization in Lightweight Cryptographic Algorithms 1

Analysis of Resource Utilization in Lightweight Cryptographi...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Rahul, Ch Kousarr, Noushheen Yadav, T Abhishek Keerthi, P. Hariharan, Shanmugasundaram Kukreja, Vinay Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350372748

In the dynamic landscape of the Internet of Things (IoT), the demand for efficient lightweight cryptographic (LWC) algorithms transcends traditional security considerations. This research addresses the compelling need to explore and enhance LWC algorithms to accommodate the distinctive challenges posed by resource-constrained devices in the expansive IoT ecosystem. This study analyzes key LWC algorithms - Ascon, Gift, Elephant, Tiny Jambu, Isap, Sparkle, PHOTON-Beetle, Romulus and Grain - evaluating the performance through various metrics such as context switches, page faults, CPU utilization, and memory usage. The existing discourse on IoT security often overlooks a comprehensive evaluation of LWC algorithms, emphasizing the critical importance of this research in bridging that gap. Beyond mere security applications, the study broadens its focus to address the multifaceted challenges of optimizing cryptographic algorithms for efficiency and resource utilization within IoT environments. By examining context switches and page faults, the research sheds light on the adaptability of these algorithms to dynamic IoT contexts. Furthermore, the study introduces innovative ideas for performance enhancements, extending the scope beyond security to contribute to the overall efficiency of IoT ecosystems. This work strives to meet the evolving demands of the IoT landscape, offering insights and solutions that transcend conventional security paradigms. © 2024 IEEE.

关键词： Network security

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Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

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computer Systems science & engineering 2023年第7期46卷 255-271页

作者： T.Satyanarayana Murthy P.Udayakumar Fayadh Alenezi E.Laxmi Lydia Mohamad Khairi Ishak Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelanganaIndia Department of Computer Science and Engineering Aditya Engineering CollegeSurampalemAndhra PradeshIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia

The recent developments in smart cities pose major security issues for the Internet of Things(IoT)*** security issues directly result from inappropriate security management protocols and their implementation by IoT gadget ***-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)*** this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their *** recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying *** current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT *** presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT *** accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square *** detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this ***,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition *** proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct *** comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.

关键词： False data injection attack security internet of things deep learning coot optimization algorithm

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Skin Lesion Decode A CNN-SVM Convergence for Enhanced Diagnostic Accuracy

Skin Lesion Decode A CNN-SVM Convergence for Enhanced Diagno...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Suryavanshi, Ankita Mehta, Shiva Rana, Samir Manwal, Manika Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Sharda University Computer Science and Engineering Sharda School of Engineering and Technology U.P. Greater Noida India

ISBN: (纸本)9798350361537

This research study investigates the development and evaluation of a merged CNN and SVM model for detecting and classifying skin lesions. The research systematically divides skin lesions into five categories that allow a thorough analysis of the model's diagnostic skills. The CNN-SVM model is developed and tested using a high-quality dataset, and its accuracy, recall, and F1 scores provide insights into specific performance aspects of the model. The results show a high level of precision for all the classes that vary from 92.95% for Class 1 to 95.95% for Class 5, which suggests that the model is good at making optimistic predictions. The recall measure, a sensitivity measure of the model, varies from 91.44% for Class 3 to 98.47% for Class 1, indicating the model's capability to recognize true positives correctly. The F1-score, which balances precision and recall, is always high, fluctuating from 93.00% for Class 3 to 96.28% for Class 5, reflecting the model's well-balanced categorization. Several types of averages are computed to assess the model's performance thoroughly. The macro average indicates accuracy of 94.70%, recall of 94.81%, and F1-score of 94.73%, with equal weight given to each class. With class distribution, the weighted average is as follows: accuracy - 94.74%, recall - 94.72%, F1-score - 94.71%. The micro average indicating the sum of contributions from all classes is 94.72% for all three parameters across the board. The model is entirely accurate, with an overall accuracy of 94.72397, showing the model's strong ability to generalize across different skin lesion types. The statistical measures confirm the CNN-SVM model as a practical approach to accurate and reliable classification of skin lesions, revealing high clinical potential. The paper's impressive experimental and statistical analyses demonstrate the power of the model and its ability to improve dermatological diagnostic procedures. © 2024 IEEE.

关键词： Neural networks

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Intelligent IoT Based Temperature and Humidity Monitoring System for Tobacco Curing Barn 5

Intelligent IoT Based Temperature and Humidity Monitoring Sy...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Chundawat, Dipendra Pal Singh Mary, Gerardine Immaculate Julian, Anitha School of Electronics Engineering Vellore Institute of Technology Vellore632014 India Saveetha Engineering College Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798331518097

A Cloud-based real-time temperature and humidity monitoring device that monitors the tobacco curing process, automates the alarm, notifies in case of any deviation from prescribed levels of temperature and humidity, transmits temperature and humidity data to the cloud, and compares the data against the specified ranges. This system predicts the future values of temperature and humidity so one can calculate the efficiency of the system. The system tackles the issue of the Barn Monitoring System using a low-cost controller. The main idea behind the Intelligent IoT-based temperature and humidity monitoring system is to maintain the temperature and humidity inside the barn in each stage of curing. The device has a sensor for temperature and humidity. If the humidity crosses the threshold value, it can alert the barn driver and farmer via a Buzzer, SMS, or e-mail. © 2024 IEEE.

关键词： Tobacco

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Unveiling Hidden Water Resources: Deep Learning and Remote Sensing for Subsurface Hydrology for Environmental Health

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 352-364页

作者： Babu, Jampani Satish G, Dinesh Suganya, R. Radhakrishnan, P. Sutar, Anand S, Alex David Rao, L. Malleswara Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP Vaddeswaram India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of Computer Science and Engineering (Data Science) New Horizon College of Engineering Karnataka Bengaluru India Department of ECE Tagore Engineering College Chennai India Department of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai India Department of Physics Sri Y N College (A) West Godavari District Andhra Pradesh Narsapur India

Water scarcity in arid regions necessitates innovative solutions to uncover and manage subsurface water resources for environmental health. This study explores the potential of GeoHydroNet, a specialized deep learning framework, in identifying hidden water resources within arid regions. Utilizing remote sensing data such as multispectral and hyperspectral imagery, geological maps, and soil moisture datasets, GeoHydroNet combines graph neural Networks (GNNs), AutoEncoders, and generative adversarial networks (GANs) to address challenges in subsurface hydrology. The model achieved an accuracy of 93.5%, surpassing traditional models like CNNs (85.4%), LSTMs (88.1%), and RNNs (82.6%). GeoHydroNet demonstrates significant precision (0.88), recall (0.87), and sensitivity to data imbalance (0.91). Its physics-informed learning approach incorporates hydrological principles, ensuring realistic predictions. By analyzing subsurface water patterns, recharge zones, and geological influences, the model not only enhances prediction accuracy but also aids in sustainable groundwater management. GeoHydroNet’s performance in identifying groundwater levels, with minimal errors ranging from 0.62 to 3.07%, indicates its robustness in tackling water scarcity challenges in data-constrained environments. These findings highlight its transformative role in hydrological modeling and sustainable water resource management in arid regions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquifers Arid regions Deep learning Generative adversarial networks GeoHydroNet Physics-informed learning Remote sensing Subsurface hydrology Sustainable water management

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