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7th IEEE International conference for Convergence in technology, I2CT 2022

作者： Bhagyalakshmi, M. Benedict, Shajulin Indian Institute of Information Technology Kottayam Dept. of Computer Science and Engineering Kerala 686635 India

ISBN: (纸本)9781665421683

Smart cities have marked an incessant push to modernize technologies for easing citizens. Smarter manholes and planning the associated logistics have become a crucial component for assisting city authorities while coining decisions. This article proposes an automatic manhole depth analysis system using sensors mounted on manhole units. The sensor data collected in cloud services are utilized to schedule logistics using the proposed Threshold Acceptance (TA) algorithm. The algorithm attempts to minimize the distance between manhole operation sites and to improve the cost efficiencies associated with the operations. The proposed method was experimented at the IoT Cloud Research laboratory;the proposed TA-enabled logistics schedule produced a faster logistics plan to rectify manhole issues of streets based on the IoT-enabled monitoring system. The experiments manifested the cost efficiency improvement of over 38 percent and distance efficiency of over 78 percent when compared to the traditional approaches of handling manhole issues. © 2022 IEEE.

关键词： Internet of things

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Convolutional Neural Networks based Enhanced Forest Monitoring System for Early Fire Detection 7

Convolutional Neural Networks based Enhanced Forest Monitori...

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7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Chatragadda, Adarsh Chalasani, Sai Harsha Vardhan Challa, Nagasupriya Gupta, N. Venkata Ramana Oleti, Prasanna Pravallika Amarendra, K. Prasad. V. Potluri Siddhartha Institute of Technology Dept. of Computer Science Engineering Vijayawada India Koneru Lakshmaiah Education Foundation Dept.of Computer Science Engineering Vijayawada India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

The vital reason behind environmental damage, species extinction, contaminated air is Forest fires. An efficient monitoring system can help us to detect and prevent forest fires such that the damage is prevented. Because wildfires are harder to identify in their initial stages, a quicker and more precise detection approach might help decrease the incidence of damage they did. This research study describe a strategy for automated and advanced fire detection built on such systems with greater level of dependability and require no service or human contact. The proposed study focuses on demonstrating variety of algorithms that are used for the wildfire detection and provide the best algorithm that can be as effective as a resultant. Nevertheless, this research work reviews some of the key deep learning methods such as CNN, VGG19 and DenseNet that can be used to build a forest fire monitoring system. © 2022 IEEE.

关键词： Fires

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Talk to your data: Enhancing Business Intelligence and Inventory Management with LLM-Driven Semantic Parsing and Text-to-SQL for Database Querying 4

Talk to your data: Enhancing Business Intelligence and Inven...

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4th International Conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Zhu, Jerry Bazaz, Saad Ahmed Dutta, Srimonti Anuraag, Bhavaraju Haider, Imran Bandopadhyay, Srijita University of Waterloo Faculty of Mathematics ON Canada National University of Computer and Emerging Sciences Dept. of Computer Science Islamabad Pakistan Indian Institute of Technology Guwahati School of Agro and Rural Technology Guwahati India Vellore Institute of Technology Dept. of Electronics and Communication Engineering India Indian Institute of Technology Kharagpur Dept. of Industrial and Systems Engineering Kharagpur India University of Engineering and Management Dept. of Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798350369786

This paper delves into the potential of Large Language Models (LLMs) in revolutionizing business intelligence and inventory management through semantic parsing and text-to-SQL methodologies. It assesses various LLM models, such as DIN-SQL, DSP, NSQL, GPT, CoPilot, and LLaMa, elucidating their capabilities and contributions. Two critical analyses are presented here. The first compares cutting-edge LLM models using cosine similarity and cost efficiency metrics. The second analysis enhances GPT's precision through prompt engineerings, like few-shot techniques, and explores frameworks like DIN-SQL, NSQL, and DSP. DIN-SQL substantially boosts accuracy, and NSQL demonstrates potential in specific scenarios. This research underscores the transformative potential of LLM-driven models in business intelligence and inventory management. DIN-SQL, in particular, emerges as a game-changer with the potential to reshape inventory management practices. GPT showcases its versatility through fine-tuning for tasks beyond conventional programming, while CoPilot offers a cost-effective alternative. This study emphasizes the importance of cost-effectiveness in real-world applications, with LLaMa and CoPilot being practical choices. NSQL, with its budget-friendly and semi-accurate solution, holds promise for semantic parsing in growing companies. These insights are a foundation for further innovation, promising unmatched efficiency and competitiveness across industries in the evolving Artificial intelligence landscape. © 2023 IEEE.

关键词： Structured Query Language

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SEC-EnD: Stacked Ensemble Correlation-based Feature Selection Method for Emotion Detection

SEC-EnD: Stacked Ensemble Correlation-based Feature Selectio...

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2022 IEEE Silchar Subsection Conference, SILCON 2022

作者： Parui, Sricheta Samanta, Debasis Chakravorty, Nishant Indian Institute of Technology Advanced Technology Development Center Kharagpur721302 India Indian Institute of Technology Dept. of Computer Science and Engineering Kharagpur721302 India Indian Institute of Technology School of Medical Science and Technology Kharagpur721302 India

ISBN: (纸本)9781665471008

Because the brain signal is so complex, choosing the right features from the EEG signal has become a major area of research and is still important if you want to receive reliable findings. Due to the extremely high dimensionality of EEG data, redundant features and the dimensionality curse are frequently present, and the classification method frequently fails to produce the intended results. We have chosen an optimum feature set for emotion recognition using certain well-known feature selection techniques to address these issues. But most of them are unstable in nature, and for different training subsets, different types of feature selection methods work best. In this research, we provide SEC-END, a stacked ensemble correlation-based feature selection method for emotion detection that can combine and stack different feature selection methods to find the best features. This method has a 3-level feature selection technique, which helps to finetune the selection of features. After integrating the entire feature set, we took the features that each approach had in common and used the union quorum method to eliminate the features that were unnecessary. Then, using four classifiers-Random Forest, Decision Tree, SVM, and KNN-we tested the feature set. Regardless of the training set and classifier, the feature subset is outperforming other state-of-the-art techniques for all the dimensions. The experiment is also carried out with different sizes of windows for EEG signals so that the optimum window size can be recognized and used for further experiments. © 2022 IEEE.

关键词： Emotion Recognition

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Adaptive Privacy and Lightweight Security in Healthcare IoT: Challenges, Solutions and Future Directions

Adaptive Privacy and Lightweight Security in Healthcare IoT:...

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Innovation in Computing and engineering (ICE), International Conference on

作者： Mukul Chauhan Tayyab Khan Dept. of Computer Science & Engineering Indian Institute of Information Technology Sonepat India

ISBN: (数字)9798350380460

ISBN: (纸本)9798350380477

IoT in healthcare has transformed patient care by enabling and implementing the real-time monitoring for the end patients or users. However, the development of connected devices introduces significant privacy and security challenges, especially while securing the healthcare data. In this paper, a comparative review of adaptive privacy and lightweight security mechanisms in the area of healthcare IoT is shown. A comparison of current methods based on criteria including data security, privacy preservation, scalability, resource efficiency, and real-time implementation is one of the major contributions. Furthermore, this review recognizes the research gaps in the recent researches and highlights promising techniques, including advanced encryption, federated learning, and the edge computing. By addressing these challenges, the study aims to guide the development of robust, efficient, and scalable solutions that ensure privacy and security in dynamic healthcare IoT environments.

关键词： Privacy Data privacy Technological innovation Reviews Scalability Medical services Real-time systems Encryption Security Edge computing

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A Comparative Analysis: Autism Spectrum Disorder Among Children Using Classification Models 2

A Comparative Analysis: Autism Spectrum Disorder Among Child...

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Srikar, Y. Himaanshu, V. Suryanarayana, G. Lnc Prakash, K. Saketha Rama, B.V. Vardhaman College of Engineering Dept: of CSE Hyderabad India Vardhaman College of Engineering Dept: of Information Technology Hyderabad India Hyderabad India Indian Institute of Information Technology Design and Manufacturing Dept: of Computer Science Engineering Kancheepuram Chennai India

ISBN: (纸本)9798350344882

Autism is a neurological condition marked by challenges with communication and social engagement, some recurring interests, and habits. It is a very complex and diverse condition that affects individuals, and there is no prescribed treatment for autism. Machine learning models have such potential to understand autism and provide more effective interventions for autistic patients. However, learning models are particularly challenging to read, making it challenging to understand how the model makes decisions and identify any biases in the model. These are important in the context of research on autism, where it is possible to assess the effects of inaccurate diagnosis or improper treatment recommendations. In this work, various algorithms are applied to an autism spectrum disorder (ASD) dataset with 931 samples and 103 features. Experimental results show that the predictive model Random Forest gives the highest accuracy, precision, recall, and Fl-Score with 100% compared to LR, GNB, KNN, SVM, and ID3. © 2023 IEEE.

关键词： Diagnosis

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PraaKrum: A Practical Byzantine-Resilient Federated Learning Algorithm

PraaKrum: A Practical Byzantine-Resilient Federated Learning...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Prabhleen Kukreja V. Mahendran Dept. of Computer Science and Engineering Indian Institute of Technology Tirupati India

Federated Learning (FL) is considered as a suitable paradigm for intelligent data analytics over Internet of Thing (IoT) devices. While the data-privacy preserving feature of FL is useful, the lack of data auditing abilities of the participating IoTs creates a potential attack surface. To this end, a potential attacker can perform data/model poisoning attacks on the FL. To protect from such attacks, a popular theoretically proven algorithm for FL namely, Krum [1] was proposed. From an implementation perspective, the Krum algorithm needs to be provided with apriori information such as the total number of attackers in each round, which is impractical. It is not feasible to know, in advance, which of the participating IoT clients are attackers. Furthermore, in the presence of mobile Parameter Server (PS) which stays in the WiFi coverage area consisting of stationary clients for an unknown time, the implementation of Krum algorithm in such practical setting is not investigated, to the best of our knowledge. With an aim to provide a practical implementation of a resilient FL framework, for the first time, this paper investigates the sensitivity of the attackers information towards the robustness offered by the Krum-based FL algorithm. The insights gained from the sensitivity analysis are used to develop a novel and Practical algorithm of Krum (PraaKrum) for FL for a WiFi network with mobile PS. The developed analysis is helpful in both understanding the key resilient characteristics offered by the Krum algorithm and providing the research community with a guideline towards the practical implementation of the attacker-resilient Krum-based FL algorithm in a mobile wireless setting.

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Fog Computing based Privacy Preservation via Distributed Access Control System in Blockchain technology 1

Fog Computing based Privacy Preservation via Distributed Acc...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Thatikonda, Ramya Nandihal, Priya Priyanka, M.S. Mathapati, Mahantesh PhD in Information Technology University of the Cumberlands Williamsburg United States Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept. of Aiml Bengaluru India Dept.of Cse Bengaluru India

ISBN: (纸本)9798350316469

The proliferation of loT -enabled gadgets means that ever more information is being created and transmitted over the web. The release of sensitive loT data, however, is a privacy breach. Sensitive information generated by loT devices poses a serious security risk since it is transmitted across an unsecured network. The storage issue may be managed with the help of an external cloud. Cloud storage has the potential to compromise users' privacy. To prevent unauthorised access and maintain confidentiality during data transmission, current systems include symmetric encryption and access restrictions. Blockchain is an exciting distributed ledger technology with the potential to safeguard stored information against manipulation by unscrupulous actors. To protect information from Internet of Things devices, this article suggests using a blockchain-based decentralised access control system. Fog computing and the idea of an alliance chain provide the basis for the suggested approach. In order to encrypt and divide data at the network's edge, fog nodes are employed. Homomorphic encryption is used on the third-party cloud to combine data that has been partitioned and encrypted. The suggested system provides dynamic and granular access control for loT data, mitigating the risk of a single point of failure in access control. Results from experiments showed that this approach effectively protects the confidentiality of data collected from Internet of Things devices. © 2023 IEEE.

关键词： Blockchain Data Privacy Fog computing Homomorphic encryption Internet of Things Malicious Tampering

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Automated Cognate Detection as a Supervised Link Prediction Task with Cognate Transformer

arXiv

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arXiv 2024年

作者： Mahesh Akavarapu, V.S.D.S. Bhattacharya, Arnab Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur India

Identification of cognates across related languages is one of the primary problems in historical linguistics. Automated cognate identification is helpful for several downstream tasks including identifying sound correspondences, proto-language reconstruction, phylogenetic classification, etc. Previous state-of-the-art methods for cognate identification are mostly based on distributions of phonemes computed across multilingual wordlists and make little use of the cognacy labels that define links among cognate clusters. In this paper, we present a transformer-based architecture inspired by computational biology for the task of automated cognate detection. Beyond a certain amount of supervision, this method performs better than the existing methods, and shows steady improvement with further increase in supervision, thereby proving the efficacy of utilizing the labeled information. We also demonstrate that accepting multiple sequence alignments as input and having an end-to-end architecture with link prediction head saves much computation time while simultaneously yielding superior performance. © 2024, CC BY.

关键词： Automation

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A Likelihood Ratio Test of Genetic Relationship among Languages

arXiv

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arXiv 2024年

作者： Akavarapu, V.S.D.S. Mahesh Bhattacharya, Arnab Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur India

Lexical resemblances among a group of languages indicate that the languages could be genetically related, i.e., they could have descended from a common ancestral language. However, such resemblances can arise by chance and, hence, need not always imply an underlying genetic relationship. Many tests of significance based on permutation of wordlists and word similarity measures appeared in the past to determine the statistical significance of such relationships. We demonstrate that although existing tests may work well for bilateral comparisons, i.e., on pairs of languages, they are either infeasible by design or are prone to yield false positives when applied to groups of languages or language families. To this end, inspired by molecular phylogenetics, we propose a likelihood ratio test to determine if given languages are related based on the proportion of invariant character sites in the aligned wordlists applied during tree inference. Further, we evaluate some language families and show that the proposed test solves the problem of false positives. Finally, we demonstrate that the test supports the existence of macro language families such as Nostratic and Macro-Mayan. © 2024, CC BY.

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