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An Energy, Mobility and Obstacle Aware Clustering based Intelligent Routing Protocol for FANET

Procedia computer science 2025年 252卷 934-943页

作者： J.G. Rajeswari R. Kousalya PH.D scholar Department of Computer Science Dr. N.G.P. Arts and Science college Coimbatore Tamilnadu Research Supervisor Dr. N.G.P. Arts and Science College Coimbatore Tamilnadu Associate Professor Christ University Bengaluru

The use of Flying Adhoc Networks (FANETs), also known as Unmanned Aerial Vehicles (UAVs), has increased in recent years. However, the fast movement of UAVs can lead to unreliable links and inefficient data transmission. To address this issue, the Intelligent-based Energy and Mobility-aware Clustering (IEMC) protocol has been developed, utilizing Battle Royale Optimization (BRO) for Cluster Head (CH) selection and a Deep Q-Learning (DQL)-based fast dynamic hello interval algorithm for path maintenance. Despite these advancements, FANETs still face challenges due to environmental obstacles affecting communication routes. To solve these issues, this article proposes an Intelligent-based Energy, Mobility, and Obstacle-aware Clustering (IEMOC) protocol for FANET routing. This protocol uses an intelligent Bezier route selection technique to deal with obstacles obstructing the paths of FANET nodes and a speed-based mobility prediction technique to reduce the impact of mobility during transmission. If link failure occurs due to an obstacle in the network, the IEMOC protocol selects an optimal alternative routing path via neighboring nodes based on its mobility awareness factor, link duration, network connectivity, and route availability, recovering the failed route without initiating the route discovery process. The effectiveness of the IEMOC protocol is validated through performance evaluations using the Network Simulator (NS)-2.35, and simulation results demonstrate that the IEMOC protocol outperforms conventional routing protocols in FANETs.

关键词： FANET IEMC protocol Obstacles Optimal routing Mobility Link duration Network connectivity Path availability

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Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13847-13867页

作者： Balasubramanian, E. Elangovan, E. Tamilarasan, P. Kanagachidambaresan, G.R. Chutia, Dibyajyoti Department of Mechanical Engineering Head-Centre for Autonomous System Research Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India Department of Electronics and Communication Engineering Centre for Autonomous System Research Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India Department of Aeronautical Engineering Centre for Autonomous System Research Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India Department of Computer Science and Engineering and Associate Editor Wireless Networks Springer Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Tamilnadu Chennai600062 India Department of Space North Eastern Space Applications Centre ISRO Meghalaya Umiam793103 India

Autonomous mission capabilities with optimal path are stringent requirements for Unmanned Aerial Vehicle (UAV) navigation in diverse applications. The proposed research framework is to identify an energy-efficient optimal path to achieve the designated missions for the navigation of UAVs in various constrained and denser obstacle prone regions. Hence, the present work is aimed to develop an optimal energy-efficient path planning algorithm through combining well known modified ant colony optimization algorithm (MACO) and a variant of A*, namely the memory-efficient A* algorithm (MEA*) for avoiding the obstacles in three dimensional (3D) environment and arrive at an optimal path with minimal energy consumption. The novelty of the proposed method relies on integrating the above two efficient algorithms to optimize the UAV path planning task. The basic design of this study is, that by utilizing an improved version of the pheromone strategy in MACO, the local trap and premature convergence are minimized, and also an optimal path is found by means of reward and penalty mechanism. The sole notion of integrating the MEA* algorithm arises from the fact that it is essential to overcome the stringent memory requirement of conventional A* algorithm and to resolve the issue of tracking only the edges of the grids. Combining the competencies of MACO and MEA*, a hybrid algorithm is proposed to avoid obstacles and find an efficient path. Simulation studies are performed by varying the number of obstacles in a 3D domain. The real-time flight trials are conducted experimentally using a UAV by implementing the attained optimal path. A comparison of the total energy consumption of UAV with theoretical analysis is accomplished. The significant finding of this study is that, the MACO-MEA* algorithm achieved 21% less energy consumption and 55% shorter execution time than the MACO-A*. moreover, the path traversed in both simulation and experimental methods is 99% coherent with each other.

关键词： Energy utilization

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Optimal Attack Path Planning based on Reinforcement Learning and Cyber Threat Knowledge Graph Combining the ATT&CK for Air Traffic Management System

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IEEE Transactions on Transportation Electrification 2024年 1-1页

作者： Liu, Chao Wang, Buhong Li, Fan Tian, Jiwei Yang, Yong Luo, Peng Liu, Zhouzhou Doctor in the Air Force Engineering University School Xi’an China Research Fellow with the Department of Electrical and Computer Engineering National University of Singapore Singapore Associate Professor at the Air Force Engineering University School Xi’an China Lecturer at the Air Force Engineering University School Xi’an China Professor in the Xi’an Aeronautical University Xi’an China

With the development of the Air Traffic Management System (ATM), the ATM network has transitioned from a closed physical isolation network to an open Cyber-Physical System (CPS). As a result, the surface of cyber-attacks is constantly expanding, and the degree of automation and correlation of attacks is constantly increasing. Traditional penetration testing attack path planning relies on the expertise of specific field experts, which can be time-consuming and resource-intensive when dealing with large networks. A large number of useful cyber security information are seriously fragmented and not integrated. In this paper, we aim to address these challenges by constructing a novel ATM system cyber threat knowledge graph called ATMCyKG. This knowledge graph is based on ATT&CK attack TTP style templates, including attack tactics, techniques, and processes. It defines entities and attributes, and their relationships. By combining ATT&CK tactics and techniques with the knowledge graph, we propose an attack path planning method based on ATMCyKG and integrate it into a reinforcement learning model. We use a variety of reinforcement learning algorithms to conduct comparative experiments and conduct a panoramic analysis of its attack process. Finally, the experimental results of the three reinforcement algorithms are analyzed and summarized. This paper introduces the ATMCyKG for the first time and utilizes Neo4j for its construction. From the perspective of an attacker, we utilized reinforcement learning to discover vulnerability sequences by selecting effective action sequences to achieve the desired target. By planning automatic attack paths, we can eliminate the reliance on expert experience, save manpower and time, and improve the operability and testing efficiency of automated penetration testing in ATM. This is of significant importance in ensuring aviation transportation safety and maintaining airspace order. IEEE

关键词： Reinforcement learning

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“Machine Learning-Based Food Edibility Prediction Using Neural Networks”

“Machine Learning-Based Food Edibility Prediction Using Neu...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Narendra Kumar S Ravi KM Arahanth M G Satheesh Pragathi Basavaraj Department of Biotechnology R V College of Engineering BE students Department of Computer Science and Engineering R V College of Engineering Scientific officer cum Assistant Professor R V College of Engineering

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Safety and the edibility of products is a must in the food industry. This research paper introduces a machine learning method for predicting whether food products can be eaten or not. A neural network model was created using various characteristics of the dataset including shelf life, manufacture date, optimum storage temperature and product type (solid, liquid or gas) to predict the edibility of a given item. The performance of the model was measured using accuracy, Mean Squared Error (MSE), and R-squared (R2). These findings demonstrate that neural networks are effective in classification tasks as they offer dependable means of forecasting if food is unsafe to eat.[1] Traditional methods have received a significant boost by this approach since it allows faster scalable automated predictions that could be integrated into larger food safety systems

关键词： Temperature measurement Solid modeling Accuracy Temperature Computational modeling Scalability Neural networks Predictive models Solids Food safety

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Sentiment Analysis on Drug Reviews Using Diverse Classification Techniques 3

Sentiment Analysis on Drug Reviews Using Diverse Classificat...

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3rd International Conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Preethaa, K.R.Sri Mouthami, K. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Research Professor Intelligent Construction Automation Center Kyungpook National University Deagu Korea Republic of

ISBN: (纸本)9781665474979

Free text reviews are abundantly distributed over the internet among the standard population of the world. Although online doctors were consulted during the pandemic, many drugs are taken without prescription by ordinary people. Reviews are distributed about the medicines through online feeds, posts and blogs, which are merely helpful in identifying the side effects and their adverse reactions in combination with another drug or standalone adverse reactions. Spelling mistakes, jargon and short names are normalized throughout the medical terminologies using Natural language processing (NLP) and RxNorm from the reviews extracted from these sources. The concept of Bidirectional Encoder Representations from Transformers (BERT) is used to mine the association rules for the drug interactions when used in conjugation with another drug, and the idea of Adaptive Fuzzy logic neural networks is used to validate the results of association rules derived from the BERT. Inconsistencies identified by the regulations generated were rejected efficiently by the Support vector machines (SVM), Radial Basis Function (RBF)and the system shows 60.11 percentage of accuracy with 10 cross-fold validation, whereas the results are equivalent to the accuracy obtained using SVM with RBF kernel. The data set is reduced from its dimension using Linear discriminant analysis (LDA) by applying the mining process and BERT on two drugs, namely 'Azithromycin' in conjugation with Buprenorphine has Type 'A' side effects, and Prozac in conjugation with Metronidazole shows different side effects. Deplin and Zoloft give very positive symptoms and are independent of dosage interactions. © 2022 IEEE.

关键词： Bidirectional Encoder Representations from Transformers (BERT) Linear discriminant analysis (LDA) Radial Basis Function (RBF) Support vector machines (SVM)

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Analysing RSA and PAILLIER homomorphic Property for security in Cloud

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Procedia computer science 2022年 215卷 240-246页

作者： Kundan Munjal Rekha Bhatia Research Scholar University College of Engineering Punjabi University Patiala City and 147002 India Associate Professor Department of Computer Science Punjabi University Patiala and 147002 India

One of the critical goals of advanced cryptography is to protect data privacy. Although protecting sensitive data has always been essential, it has become much more due to the widespread use of cloud services. Conventional encryption schemes have one drawback: data must be decrypted before processing. Best practices have long recommended that data must be secured at rest and in motion when using public cloud services, i.e., we need encryption at the storage as well as at processing levels. Homomorphic encryption is an advanced cryptographic approach in which the processing of encrypted data is possible. In this paper, we have presented the insights required for understanding homomorphic encryption approaches. We examined and compared two partial homomorphic techniques, RSA and Pallier algorithm based on efficiency, base method, and homomorphic property.

关键词： Cryptography Public key Algorithms Homomorphic Encyrption RSA Paillier Partial Homomorphic Encryption

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Hybrid optimal feature selection-based iterative deep convolution learning for COVID-19 classification system

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computers in Biology and Medicine 2024年 181卷 109031-109031页

作者： Santosh Kumar Patra, P. Tripathy, Biswajit Research Scholar Department of Computer Science and Engineering Biju Patnaik University of Technology Odisha Rourkela769015 India Professor Master of Computer Applications Einstein College of Computer Application and Management Odisha Khurda752060 India

The COVID-19 pandemic has necessitated the development of innovative and efficient methods for early detection and diagnosis. Integrating Internet of Things (IoT) devices and applications in healthcare has facilitated various functions. This work aims to employ practical artificial intelligence (AI) approaches to extract meaningful information from the vast amount of IoT data to perform disease prediction tasks. However, traditional AI methods need help in feature analysis due to the complexity and scale of IoT data. So, this work implements the optimal iterative COVID-19 classification network (OICC-Net) using machine learning optimization and deep learning approaches. Initially, the preprocessing operation normalizes the dataset with uniform values. Here, random forest infused particle swarm-based black widow optimization (RFI-PS-BWO) algorithm was used to get the disease-specific patterns from SARS-CoV-2 (SC2), and other disease classes, where patterns of the SC2 virus are very similar to those of other virus classes. In addition, an iterative deep convolution learning (IDCL) feature selection method is used to distinguish features from the RFI-PS-BWO data. This iterative process enhances the performance of feature selection by providing improved representation and reducing the dimensionality of the input data. Then, a one-dimensional convolutional neural network (1D-CNN) was employed to classify and identify the extracted features from SC2 with no virus classes. The 1D-CNN model is trained using a large dataset of COVID-19 samples, enabling it to learn intricate patterns and make accurate predictions. It was tested and found that the proposed OICC-Net system is more accurate than current methods, with a score of 99.97 % for F1-score, 100 % for sensitivity, 100 % for specificity, 99.98 % for precision, and 99.99 % for recall. © 2024 Elsevier Ltd

关键词： COVID-19 pandemic Internet of things Remote patient monitoring Machine learning optimization Particle swarm optimization Black widow optimization Iterative deep convolution learning

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Machine learning–based heart disease prediction system for Indian population: An exploratory study done in South India

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Medical Journal Armed Forces India 2021年第3期77卷 302-311页

作者： Maini, Ekta Venkateswarlu, Bondu Maini, Baljeet Marwaha, Dheeraj Research Scholar (Computer Science & Engineering) Dayananda Sagar University Bengaluru India Associate Professor (Computer Science & Engineering) Dayananda Sagar University Bengaluru India Professor Pediatrics Teerthanker Mahaveer Medical College & Research Centre Moradabad India Senior Software Engineer Microsoft India Hyderabad India

Background: In India, huge mortality occurs due to cardiovascular diseases (CVDs) as these diseases are not diagnosed in early stages. Machine learning (ML) algorithms can be used to build efficient and economical prediction system for early diagnosis of CVDs in India. Methods: A total of 1670 anonymized medical records were collected from a tertiary hospital in South India. Seventy percent of the collected data were used to train the prediction system. Five state-of-the-art ML algorithms (k-Nearest Neighbours, Naïve Bayes, Logistic Regression, AdaBoost and Random Forest [RF]) were applied using Python programming language to develop the prediction system. The performance was evaluated over remaining 30% of data. The prediction system was later deployed in the cloud for easy accessibility via Internet. Results: ML effectively predicted the risk of heart disease. The best performing (RF) prediction system correctly classified 470 out of 501 medical records thus attaining a diagnostic accuracy of 93.8%. Sensitivity and specificity were observed to be 92.8% and 94.6%, respectively. The prediction system attained positive predictive value of 94% and negative predictive value of 93.6%. The prediction model developed in this study can be accessed at http://***/predict/ Conclusions: ML-based prediction system developed in this study performs well in early diagnosis of CVDs and can be accessed via Internet. This study offers promising results suggesting potential use of ML-based heart disease prediction system as a screening tool to diagnose heart diseases in primary healthcare centres in India, which would otherwise get undetected. © 2020

关键词： Affordable healthcare Cardiovascular diseases Early diagnosis Machine learning

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Ethno medicine of Indigenous Communities: Tamil Traditional Medicinal Plants Leaf detection using Deep Learning Models

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Procedia computer science 2024年 235卷 1135-1144页

作者： G.B. Govindaprabhu M. Sumathi Research Scholar (MKU23PFOS10909) Madurai Kamaraj University Madurai. Head & Associate Professor PG & Research Department of Computer Science Sri Meenakshi Government Arts College for Women Madurai.

In indigenous communities, ethno medicine has played a prominent role in healing for centuries and provides valuable insight into the use of traditional medicinal plants. Using local plant leaves as therapeutic agents, this research explores the importance of traditional Tamil medicine. Traditional healers and practitioners can benefit from technological advances and collaborate with modern healthcare systems through these advances. Based on deep learning models, a novel approach for recognizing leaves of traditional Tamil medicinal plants is presented. Pharmaceutical companies are increasingly paying attention to plants that contain medicinal factors that have fewer side effects during treatment. By identifying plants with medicinal factors and identifying their medicinal uses, this work addresses the identification of plants with medicinal factors. To classify medicinal plants and their uses, we will use deep learning models and image processing techniques to identify Tamil Traditional Medical Plant (TTMP). The input dataset for this work includes 18 different traditional Tamil plant leaves such as Azadirachta Indica (Neem), Ocimum Tenuiflorum (Tulsi) and Trigonella Foenum-graecum (Fenugreek). Pre-processing steps are performed on the input plant image, including RBZR Augmentation, noise removal and grayscale conversion, to detect the image area that is important for plant type identification. To identify the core area of the plant, this work using the HGAW Segmentation algorithm. Deep learning model is trained with segmented plants and their labels as inputs. The aim is to digitize and make accessible ancestral medical knowledge by incorporating deep learning-based leaf recognition with 96.71% accuracy for the proposed EEXR model.

关键词： Type your keywords here separated by semicolons

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E-governance security via public key cryptography using elliptic curve cryptography

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Materials Today: Proceedings 2022年 49卷 3568-3573页

作者： Mudassir Peeran A Dr. A.R. Mohamed Shanavas Reserach Scholar Research Department of Computer Science Jamal Mohamed College Bharathidasan University India Associate Professor Research Department of Computer Science Jamal Mohamed College Bharathidasan University India

“In the current long stretches of worldwide financial emergency the Government of the non-industrial nations like India are confronting extreme test in keeping an effective organization in its center and associated areas inside a moderate spending plan. The utilization of cutting edge Information and Communication Technology (ICT) will assist with executing the Electronic Governance which will supplant its traditional structure to convey administrations to the general population at decreased rate. To do as such, the Citizen should have a multivariate electronic instrument which will particularly distinguish the Citizen in all regard and help to speak with the Government. This paper gives a thought on E-Governance Security utilizing public key cryptography. The public key cryptography procedure called Elliptic Curve Cryptography (ECC) become famous over the most recent couple of years. The fundamental fascination of ECC is that it requires some investment challenge for an interloper to break into the cryptographic framework. ECC utilizing 160 pieces key, gives a similar security as offered by the notable RSA utilizing 1024 pieces key, accordingly prompting lower preparing time, negligible transmission capacity use and rapid exchanges.

关键词： E-Governance RSA Elliptic curve cryptography (ECC) Encryption Public key cryptography

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