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Performance Analysis of Routing Protocols for FANETs

Performance Analysis of Routing Protocols for FANETs

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International Conference on Computing and Networking Technology (ICCNT)

作者： Anita Rani Vinay Bhardwaj Computer Application Lovely Professional University Phagwara INDIA Computer Science and Engineering Lovely Professional University Phagwara INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Routing protocols play an important role in unmanned aerial vehicle (UAV) communication networks. When transferring information in low-altitude environments, UAVs confront difficulties in ensuring efficient and reliable packet transmission between nodes. To address this scientific challenge, a routing protocol needs to be developed that can meet these requirements. In this paper, we have created a more realistic simulation environment based on NS-3 and conducted performance tests and comparisons on four classic routing protocols: Ad Hoc on-demand distance vector (AODV), Destination-Sequenced Distance-Vector Routing (DSDV), Dynamic Source routing (DSR) and optimized link state routing (OLSR). We have analyzed performance parameters such as packet delivery and throughput. Our findings demonstrate that alternative routing strategies may function effectively in several UAV communication network circumstances. These experiments generate quantitative data that determines the optimum pathways in every specific scenario. According to the data analysis, while utilized on networks of 50,100,200, and 250, the AODV protocol has an accuracy of 96%. The DSR protocol achieves 90% accuracy, the DSDV protocol 87% accuracy, and the OLSR protocol 80%. The results show that AODV outperforms other traditional approaches, resulting in a considerable improvement in network efficacy.

关键词： Technological innovation Accuracy Routing Autonomous aerial vehicles Throughput Routing protocols Vectors Communication networks Vehicle dynamics AODV

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Machine Learning-Driven Economic Modeling for Enhanced Unemployment Rate Prediction and Analysis

Machine Learning-Driven Economic Modeling for Enhanced Unemp...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Rakhi Arora Nitin Dixit Muskan Sihare Shreshtha Kinger Sneha Shivhare Komal Kulshreshtha Computer Science and engineering Institute of technology & Management Gwalior India Masters of Computer Application Vikrant University Gwalior India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

This study presents a comprehensive analysis of economic modeling using Python, specifically focusing on the prediction of unemployment rates. In response to the growing significance of data-driven decision-making in economics, our research explores the application of logistic regression and random forest classification models to comprehend and forecast unemployment status. The dataset utilized in this analysis encompasses historical unemployment rate data, serving as a valuable source for our modeling endeavors. Our methodology begins with logistic regression, establishing a baseline model to scrutinize the relationship between diverse economic indicators and the binary classification of unemployment status (above or below 10%). We evaluate the model's efficacy in capturing trends over time and identifying influential factors in determining unemployment status. Subsequently, we advance our approach by employing random forest classification, a sophisticated machine learning technique adept at capturing non-linear relationships and interactions within the data. Our investigation shows that both random forest classification and logistic regression are good at predicting jobless status. We conduct a comparative evaluation of these models, shedding light on their respective strengths and limitations. Additionally, we delve into the significance of feature selection, hyperparameter tuning, and model evaluation within the realm of economic modeling using Python.

关键词： Logistic regression Analytical models Accuracy Biological system modeling Computational modeling Predictive models Market research Unemployment Random forests Tuning

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Deep Learning-Based Diagnostic Tool for Brain Tumours 3

Deep Learning-Based Diagnostic Tool for Brain Tumours

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Singh, Arun Rakhra, Manik Singh, Dalwinder Sharma, Arvind Kumar, Rakesh Lovely Professional University Department of Computer Science & Engineering Jalandhar Phagwara India School of Computer Application Lovely Professional University Punjab Phagwara India Lovely Professional University Department of Mathematics Punjab Phagwara144411 India

ISBN: (纸本)9798350303360

In this work, we provide the results of our experiments on Brain Tumour Classification and explore their implications. MRI, short for magnetic resonance imaging, is a crucial piece of medical imaging technology that creates high-resolution pictures of the inside of a human body by combining radio waves, a strong magnetic field, and a computer. In this study, we employ a CNN-based Deep learning model to categorise cases of brain tumours. Common applications for CNNs (Convolutional Neural Networks) include video and image analysis. It processes images by first ingesting them and then convoluting them into smaller and smaller pieces. The convolutional layer analyses the input picture using a series of filters designed to identify elements like edges, curves, and patterns. Here, we constructed the CNN using data obtained from Kaggle. It contains roughly 3300 MRI scans of the brain, split into 4 groups (no tumours, meningioma tumours, glioma tumours, and pituitary tumours) and a total of 384 testing pictures. We used Keras to construct the model. The accuracy of the suggested project is quite high, coming in at 97.142%. The Confusion matrix is used to evaluate the precision. It is a data table for measuring the efficacy of an ML system. This provides the results of a comparison between the actual values in a dataset and the model's projected values in an easy-to-understand format. © 2023 IEEE.

关键词： Neuroimaging

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A Conjunctional Approach for Enhanced and Extended Security in Big data Environment 3

A Conjunctional Approach for Enhanced and Extended Security ...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Vishnupriyan, S. Benitta, D. Angeline Atab, Marzouq Yasser Shareef, Ashraf Mohammed Ali, Aqeel Al-Tahee, Mustafa Hindustan Institute of Science and Technology Department of Computer Application Chennai India National University of Science and Technology Collage of Engineering Dhi Qar Iraq Al-Farahidi University Medical Laboratory Techniques Department Baghdad Iraq

ISBN: (纸本)9798350399264

Today people's live in the "Era of Data". The data has been tortured and indulged in confession in many ways. The term big data deals with large volumes of data that streams in a fraction of a second and of-course in the format of unstructured data. As the data are big the challenges are high and the process of handling data is becoming so daunting. And the security issues of big data are becoming a great threat to the data which is very critical for the development of many organizations. As the security solution cannot be given as a one-stop solution a layered methodology has been proposed in this work for securing confidential data. It is a combination of heavy encryption traditional algorithm RSA and data masking technique which will lead to better security for the data. The reason why the RSA algorithm is proposed is that by using this algorithm it is very safe to exchange data over the internet, it maintains the confidentiality of the data to a high extent and this algorithm will maintain high toughness because breaking the keys through interceptors is difficult. The results and discussions of our proposed methodology will lead to an enhanced security system with fuzzy procedures which efficiently strengthen the system. © 2023 IEEE.

关键词： Big data

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Sentiment Analysis Based on Machine-Learning Classifiers for Datasets 8th

Sentiment Analysis Based on Machine-Learning Classifiers for...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Takale, Dattatray G. Deshmukh, Gopal B. Kashid, Shraddha S. Gawali, Piyush P. Mahalle, Parikshit N. Sule, Bipin Ashokrao, Patil Rahul Ghorpade, Pradip P. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Computer Engineering MIT ADT University Pune India Department of Artificial Intelligence and Data Sciences Vishwakarma Institute of Information Technology SPPU Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Computer Science and Application KTHM College Nashik India Computer Engineering Department Sandip University Mahiravani Trimbak Road Maharashtra Nashik India

ISBN: (纸本)9789819766772

The study of sentiment analysis in natural language processing (NLP) holds tremendous promise for extracting meaningful emotional cues from textual data as a part of its core capabilities. This paper conducts an extensive exploration of sentiment analysis techniques powered by machine-learning classifiers, tailored to accommodate a variety of datasets. As a critical tool for assessing public opinion and sentiment, this study guides you through the crucial steps of data acquisition, preprocessing, feature extraction, and classifier selection, which are integral to the assessment of public sentiment. The research examines machine-learning approaches to sentiment analysis using a wide range of datasets, including social media content, product reviews, news articles, and much more, to determine if they are adaptable and scalable. In this study, "a number of classification models are examined, including Naive Bayes, Support Vector Machines, Random Forests, and deep learning models, and how their performance varies depending on the type of data". As a result of this analysis, the paper emphasizes the importance of utilizing machine-learning classifiers to distill nuanced sentiments, enabling applications in a wide range of fields, from brand reputation management to political sentiment tracking. With impressive accuracy, precision, F-measure, and correctly classified instances, Naive Bayes demonstrates rapid learning capabilities, while One-R displays promise with 91% accuracy, 97% precision, and 92% F-measure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Machine Learning Techniques for Identifying Distressed Individuals on Social Media - A Survey

Machine Learning Techniques for Identifying Distressed Indiv...

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Big Data Analytics in Bioinformatics (DABCon), International Conference on

作者： Ankan Sadhukhan Sreejani Samanta Abhijit Pasari Sayantika Samui Dipayan Mandal Sandipta Ghosh Mrinmoy Guria Debrupa Pal Computer Science and Engineering Narula Institute of Technology Kolkata India Computer Application Narula Institute of Technology Kolkata India

ISBN: (数字)9798350378047

ISBN: (纸本)9798350378054

The pervasive presence of social media platforms has turned them into reservoirs of human emotions, providing a distinctive perspective into the intricate tapestry of digital sentiments. This research delves into the realm of emotional upheaval prevalent in online spaces, aiming to uncover signs of distress through the utilization of machine learning algorithms. By scrutinizing extensive volumes of social media data, this survey seeks to establish a pioneering framework for predicting the identification of individuals undergoing emotional distress. Many papers are published on machine learning approaches over the past few decades. In this paper, we provide a comprehensive survey of all best machine learning approaches in the current year to study or identify distress calls on social video through sentiment and media analysis. Furthermore, the challenges and benefits of using Machine Learning, along with its shortcomings, are summarized. Finally, open issues and their consequences for further research in the field of identifying individuals who are mentally depressed on social media are presented.

关键词： Surveys Ethics Machine learning algorithms Social networking (online) Surge protection Machine learning Mental health Predictive models Real-time systems Surges

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Smart Water Management for a Sustainable Tomorrow: A Digital Twin Approach for Rural India

Smart Water Management for a Sustainable Tomorrow: A Digital...

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Delhi Section Conference (DELCON), IEEE

作者： Dinesh Kumar Singh Vishnu Sharma Department of Computer Science and Engineering FCE Poornima University Jaipur India Department of Computer Science and Application FCE Poornima University Jaipur India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Rural areas in India, such as Randullabad village in Satara, are facing significant water challenges related to UN SDG6. It includes inadequate water supply, inefficient distribution, and poor water quality. Current methods of water governance rely on manual data collection and limited use of sensors. This leads to lack of modelling, inaccurate estimation, delayed response times, and inefficient decision-making. These limitations results in water-related conflicts, water-wastage, and economic losses. To address these issues, a digital twin approach is proposed for sustainable water management. This approach integrates existing and historical data with new data from advanced sensors, using machine learning algorithms to create a virtual replica of the village's water supply network. The proposed methodology and high-level implementation architecture of Digital Twin of Randullabad Watershed and Groundwater (DToGW) could set new water-governance standards at the village level and potentially transform water management in rural communities.

关键词： Machine learning algorithms Costs Groundwater Biological system modeling Decision making Watersheds Water conservation Digital twins Stakeholders Intelligent sensors

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Statistical Analysis of Feature Selection and Clustering for Building the Optimised Drug Toxicity Prediction Model 7th

Statistical Analysis of Feature Selection and Clustering for...

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7th International Conference on Information and Communication Technology for Competitive Strategies , ICTCS 2022

作者： Kamal Kumar, Devender Sharma, Anuj Kumar Department of CSE Baba Mastnath University Rohtak India Department of Computer Science and Application Baba Mastnath University Rohtak India Department of CSE BRCM College of Engineering Bahal India

ISBN: (纸本)9789811996375

Artificial intelligence and machine learning are crucial in medication research and development. Because of the likelihood of toxicity, many pharmacological evaluations can be omitted. Toxicity prediction is critical for decreasing the cost and time spent on preclinical and clinical research for a drug. Machine learning has played a major part in medication development by specification, from structural design to clinical trial. Cluster analysis is used to improve the prediction model’s performance. This statistics focuses on analysing different features using different feature selection strategies. The deviations of the features are used to assign a score to them. Cluster analysis employs a variety of clustering algorithms. Various techniques for feature selection and clustering provide different results. The assessment of crucial aspects and cluster-wise analysis aids in the optimisation of a prediction model for forecasting medication toxicity. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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Transformative applications and Key Challenges of Generative AI 7

Transformative Applications and Key Challenges of Generative...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Kovari, Attila Katona, Jozsef Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary University of Dunaújváros Department of Institute of Engineering Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary

ISBN: (纸本)9798331531119

The paper discusses the integration and use of generative artificial intelligence technologies in education. Generative AI, such as OpenAI, in particular the GPT model, has fundamentally changed the way humans interact with machines. Advanced technologies in the field of generative AI in education are mostly used to personalize learning experiences by dynamically customizing learning materials according to the needs of learners, thus enabling the improvement of outcomes. This article explores the use of generative AI tools with the aim of assessing how useful students perceive these tools to be. The findings show mixed receptivity from users, highlighting the critical balance in using innovative AI capabilities through AI. © 2024 IEEE.

关键词： Generative adversarial networks

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HyperCalm Sketch: One-Pass Mining Periodic Batches in Data Streams 39

HyperCalm Sketch: One-Pass Mining Periodic Batches in Data S...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Liu, Zirui Kong, Chaozhe Yang, Kaicheng Yang, Tong Miao, Ruijie Chen, Qizhi Zhao, Yikai Tu, Yaofeng Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Zte Corporation China

ISBN: (纸本)9798350322279

Batch is an important pattern in data streams, which refers to a group of identical items that arrive closely. We find that some special batches that arrive periodically are of great value. In this paper, we formally define a new pattern, namely periodic batches. A group of periodic batches refers to several batches of the same item, where these batches arrive periodically. Studying periodic batches is important in many applications, such as caches, financial markets, online advertisements, networks, etc. We propose a one-pass sketching algorithm, namely the HyperCalm sketch, which takes two phases to detect periodic batches in real time. In phase 1, we propose a time-aware Bloom filter, namely HyperBloomFilter (HyperBF), to detect the start of batches. In phase 2, we propose an enhanced top-k algorithm, called Calm Space-Saving (CalmSS), to report top-k periodic batches. We theoretically derive the error bounds for HyperBF and CalmSS. Extensive experiments show HyperCalm outperforms the strawman solutions 4× in term of average relative error and 13.2× in term of speed. We also apply HyperCalm to a cache system and integrate HyperCalm into Apache Flink. All related codes are open-sourced. © 2023 IEEE.

关键词： Error analysis

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