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Communication, computer sciences and Engineering (IC3SE), International Conference on

作者： Sriprasadh K Shamik Palit B Pravallika Manjunatha Reena Lenka Atul Singla Department of Computer Science and Applications Faculty of Science and Humanities SRM Institute of Science and Technology Vadapalani Chennai Department of Computing Science and Software Engineering RAK Campus University of Stirling UAE Institute of Aeronautical Engineering Hyderabad Department of Mechanical Engineering New Horizon College of Engineering Bangalore Symbiosis Institute of Management Studies Symbiosis International (Deemed) University Pune Lovely Professional University Phagwara India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

A machine learning-based e-commerce personalised recommendation system helps address the issue of information overload that inevitably arises when consumers have more and more options in e-commerce, leading to an increasingly complex structure. Customer segmentation is a crucial component of contemporary marketing strategy since it enables companies to effectively adjust their advertising campaigns and customise their communications with customers. Through the analysis of enormous quantities of consumer behaviour data and the discovery of trends that can be utilised for dividing customers into segments, ML algorithms provide a potent tool for automating the procedure of customer segmentation. We report our research and results for identifying a customer's segment. In order to perform machine learning, we integrate the Python Tensorflow library with Pandas for manipulating data frames. To address any discrepancies or gaps in the collected client data, it is refined. Key features are found, and to find hidden patterns and relationships, an exploratory analysis of the data is done. After that, we decide on a suitable machine learning technique to divide the clientele into various groups. We also experiment with several clustering models, including conventional machine learning techniques.

关键词： Consumer behavior Social networking (online) Machine learning Companies Market research Real-time systems Libraries

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Paramagnon heat capacity and anomalous thermopower in anisotropic magnetic systems: Understanding interlayer spin correlations in a magnetically disordered phase

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Physical Review B 2024年第5期109卷 054418-054418页

作者： Fatemeh Heydarinasab Morteza Jazandari Md Mobarak Hossain Polash Jahanfar Abouie Daryoosh Vashaee Department of Physics Faculty of Science University of Sistan and Baluchestan Zahedan 98167-45845 Iran Department of Physics Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan 45137-66731 Iran Department of Materials Science and Engineering North Carolina State University Raleigh North Carolina 27606 USA Department of Electrical and Computer Engineering North Carolina State University Raleigh North Carolina 27606 USA

The interplay between entropy transport and charge carriers–paramagnon interaction in the Onsager linear system has been a subject of debate due to the limited theoretical and experimental understanding of paramagnon heat capacity. In this study, we investigate this interplay in an anisotropic layered magnetic system using cluster mean-field theory with spin quantum correlations. By examining spin correlation functions between different spins with various types of clustering, we derive the spin correlation function as a function of distance and temperature for the interlayer clusters both below and above the magnetic order phase transition. Our analysis reveals that paramagnons characterized by pronounced spin correlations among interlayer nearest-neighbor spins exhibit a nonzero heat capacity, providing valuable insights into the dynamics of entropy transport. The findings align with experimental observations, lending strong support to the validity of the paramagnon-drag thermopower concept. This study sheds light on the intricate dynamics and thermodynamic properties of paramagnons, advancing our understanding of entropy transport in complex systems.

关键词： Entropy Specific heat Spin dynamics Thermoelectric effects

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Investigating Blockchain Security Mechanisms for Tamper-Proof Data Storage

Investigating Blockchain Security Mechanisms for Tamper-Proo...

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Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： P. Thilakavathy S. Jayachitra Anurag Aeron Narendra Kumar Syed Sumera Ali M. Malathy Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of ECE PSNA College of Engineering and Technology Dindigul Tamilnadu India Computer Science and Engineering School of Computing DIT University Dehradun Department of Computer Science and Engineering Amity University Ranchi Jharkhand India Electronics and Computer Engineering Department CSMSS Chh Shahu College of Engineering Aurangabad Maharashtra India Department of Computer Science and Engineering Vel Tech High Tech Dr Rangarajan Dr Sakunthala Engineering College Avadi Chennai India

Across a wide range of sectors, blockchain technology has emerged as a game-changing option for secure data storage. This research dives into the mechanisms that enable blockchain to protect data integrity and immutability. This research investigates how blockchain technology reduces the chances of data tampering and unauthorized adjustments by examining its underlying concepts and features, such as decentralization, cryptography, consensus procedures, and immutability. Blockchain networks are decentralized, so they don't rely on any one entity to function. This eliminates one potential point of failure or manipulation. Data is protected from tampering and disclosure thanks to the use of hash functions and digital signatures, two examples of strong cryptographic algorithms. By confirming and agreeing on transactions, consensus methods like Proof of Work (PoW) and Proof of Stake (PoS) keep the blockchain ledger up-to-date and accurate. By linking blocks together and recording timestamps, blockchain makes it practically impossible to alter data after it has been recorded. The distributed ledger's availability to the public and ease of use encourage accountability and deter dishonest participants. Smart contracts eliminate the need for third parties by automating operations based on predetermined rules that cannot be altered after they have been created. This study also delves into blockchain fork resistance, incentives for truthful involvement, and other network security measures. It also covers the security implications of both permissioned and permissionless blockchains. Blockchain technology provides strong security procedures, but it has several drawbacks that must be taken into account. These include issues with scalability and the possibility of security flaws in smart contracts. Therefore, securing security and performance requires cautiously considering particular use case needs and picking the right blockchain platform and configuration. For blockchain networ

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Optimizing Crop Traits with ANN and Genetic Innovations for Sustainable Agriculture

Optimizing Crop Traits with ANN and Genetic Innovations for ...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Radha Mahendran Ramanakar Reddy Danda Asma Anjum Rashmi Abhay Kamde Bhagyashree Shailesh Firake Jayshree Suhas Wasnik Department of Bioinformatics Vels Institute of Science Technology and Advanced Studies Chennai India Architect Hyderabad Telangana India Department of Computer Science and Engineering HKBK College of Engineering Visvesvaraya Technological University Bangalore India Department of Electrical Engineering K J Somaiya Polytechnic Mumbai Maharashtra India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Sustainable agriculture requires new strategies to tackle difficulties such as rising food demand, environmental pressures, and resource constraints. This study introduces a sophisticated approach that combines artificial neural networks (ANNs) with genetic advancements to enhance crop characteristics. The system attains enhanced accuracy (98.1%), F1 score (0.97), and reduced RMSE (0.012) by utilising high-dimensional genetic, phenotypic, and environmental data, surpassing baseline models. The technique integrates ANN predictions with gene-editing frameworks, guaranteeing accurate interventions and effective trait optimisation. The results demonstrate considerable improvements in forecast accuracy and resource efficiency compared to current techniques, creating a scalable, data-driven framework to promote sustainable agriculture practices worldwide.

关键词： Precision agriculture Technological innovation Adaptation models Accuracy Visual analytics Crops Artificial neural networks Genetics Sustainable development Optimization

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Optimizing Crop Traits with ANN and Genetic Innovations for Sustainable Agriculture

Optimizing Crop Traits with ANN and Genetic Innovations for ...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Mahendran, Radha Danda, Ramanakar Reddy Anjum, Asma Kamde, Rashmi Abhay Firake, Bhagyashree Shailesh Wasnik, Jayshree Suhas Vels Institute of Science Technology and Advanced Studies Department of Bioinformatics Chennai India Architect Telangana Hyderabad India Hkbk College of Engineering Visvesvaraya Technological University Department of Computer Science and Engineering Bangalore India K J Somaiya Polytechnic Department of Electrical Engineering Maharashtra Mumbai India

ISBN: (纸本)9798331521394

关键词： Precision agriculture

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Air quality monitoring for Smart eHealth system using firefly optimization and support vector machine

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International Journal of Information Technology (Singapore) 2021年第5期13卷 1847-1859页

作者： Rahi, Pankaj Sood, Sanjay P. Bajaj, Rohit Kumar, Yogesh University Institute of Computing Chandigarh University Gharuan Mohali Punjab India Health Informatics Division Centre for Development of Advanced Computing Mohali Punjab India Department of Computer Science and Engineering Chandigarh University Gharuan Mohali Punjab India Department of Computer Engineering Chandigarh Group of Colleges Landran Mohali Punjab India

The air quality monitoring system systematically monitors the level of pollutants in the air by measuring the concentration of the particular pollutant in the surrounding and the outside environment. The strategy for developing the monitoring system should ensure the acceptable quality of data, to store and record the data in the database, the analysis of data and to present the result. In this concern, we have proposed a smart system of air quality monitoring that help in preventing and controlling airborne allergies and reducing the burden of disease and the cost of treatment. The healthcare data layers and healthcare APIs for standardizing the smart health predictive analytics were derived using meta-heuristic and Machine learning algorithms. This work mainly focused on improving the existing expert systems of air quality monitoring. In concern of this, the detail study of various terms related to air quality monitoring has been done and proposed a new approach that able to gives a better outcomes. In the proposed work, the meta-heuristic firefly optimization has been applied to optimize the selected features during feature selection process and then further classified by using support vector machine which predict the index level and gives better precision and recall of 95.7% and 93.1% respectively and accuracy of 94.4% while compare it with the existing approaches. © 2021, Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Air quality monitoring system eHealth System Firefly optimization Health issues Support vector machine

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Bc²FL: Double-Layer Blockchain-Driven Federated Learning Framework for Agricultural IoT

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IEEE Internet of Things Journal 2024年第4期12卷 4362-4374页

作者： Qingyang Ding Xiaofei Yue Qinnan Zhang Zehui Xiong Jinping Chang Hongwei Zheng School of Management Beijing Union University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Tampines Singapore Blockchain and Privacy Computing Technology R&D Department Beijing Academy of Blockchain and Edge Computing Beijing China

With the flourishing of the Agricultural Internet of Things (AIoT), analyzing large-volume sensor data has become a regular requirement for agricultural decision-making. Federated learning (FL), which facilitates scattered AIoT devices to train models collaboratively, has gained significant attention. However, traditional FL poses challenges in AIoT scenarios, such as wide geo-distribution, heterogeneous data distribution, and high-device risks. Existing works tend to be one-sided and remain unclear on how to tackle these issues thoroughly in AIoT. To fill the gap, we present Bc2FL, a double-layer blockchain-based FL framework, which enhances both learning efficiency and security for AIoT. The double-layer blockchain, coupled with a two-stage consensus algorithm, drives the hierarchical FL process to enable efficient and reliable agricultural knowledge-sharing. In addition, Bc2FL adopts an adaptive model aggregation algorithm to dynamically tune noise levels based on the model quality, further improving the learning security and model credibility. Finally, the extensive experimental results demonstrate that Bc2FL not only improves the model accuracy by up to 21.17% compared with the state-of-the-art baselines, but also enhances the privacy protection within an additional error of only 2.1%.

关键词： Blockchains Internet of Things Privacy Computational modeling Federated learning Data models Biological system modeling Adaptation models Accuracy Servers

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An IoT based Voice Assisted Smart Residential Appliance Control Mechanism using Intelligent Node MCU Controller

An IoT based Voice Assisted Smart Residential Appliance Cont...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Aswathy K Cherian H. Mercy R. Reshma K. Sivakami R. Krishnamoorthy R. Thiagarajan Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science And Technology Chennai India Department of Computer Science and Engineering Jerusalem College of Engineering Chennai India Dept of Computer science Justice Basheer Ahmed Sayeed college for Women Chennai India Department of ECE Nehru Institute of Engineering and Technology Coimbatore India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India Department of IT Prathyusha Engineering College Chennai Tamilnadu India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The idea of a gadget operated by a remote or switch is today considered antiquated. This is the technology of the future. The article presents a novel approach of managing smart appliances with voice commands, which is referred to as Voice Assisted Smart Appliance Control (VASAP). In order to assess the efficacy and performance of the suggested system, it is compared against the traditional GPRS Assisted Smart Appliance Control (GPRSSAC). An Android app requires the user to speak commands. Voice instructions may be recognized and transmitted to the system using this program. These commands are decoded by a receiver and decoder, which then send them over a serial connection to the Arduino controllers. The system's software and hardware are developed in accordance with this plan, and the system's wireless communication technique, which integrates Wi-Fi and RF (radio frequency), is also established. The systems functional needs inform the design of two sets of control schemes-one for voice control and one for button control-with the goal of making the system more compassionate. For the voice control solution to work, the user must first read out from the smart terminal the control instructions that have been pre-programmed into the system's voice library. Once the recognition is confirmed, the user will be able to command their household appliances. Under the key control scheme, smart terminal keys are used to operate household appliances. In order to collect and store audio, verify family members, and recognize voice commands, the upper computer uses an algorithm that was studied in this study. The lower computer collects environmental information in real-time and wirelessly controls home facilities. Upon startup, family members are asked to confirm their voice commands. Following this, the system uses real-time environmental information and the results of voice command recognition to formulate a control command. Through the serial port, the command to operate the ass

关键词： Wireless communication Home appliances Bluetooth Home automation Operating systems Wheelchairs Speech recognition Real-time systems Relays Wireless fidelity

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On Relaxed Locally Decodable Codes for Hamming and Insertion-Deletion Errors

arXiv

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arXiv 2022年

作者： Block, Alex Blocki, Jeremiah Cheng, Kuan Grigorescu, Elena Li, Xin Zheng, Yu Zhu, Minshen Department of Computer Science Purdue University United States Center on Frontiers of Computing Studies Peking University United States Advanced Institute of Information Technology Peking University United States Department of Computer Science Johns Hopkins University United States

Locally Decodable Codes (LDCs) are error-correcting codes C: Σn → Σm, encoding messages in Σn to codewords in Σm, with super-fast decoding algorithms. They are important mathematical objects in many areas of theoretical computer science, yet the best constructions so far have codeword length m that is super-polynomial in n, for codes with constant query complexity and constant alphabet size. In a very surprising result, Ben-Sasson, Goldreich, Harsha, Sudan, and Vadhan (SICOMP 2006) show how to construct a relaxed version of LDCs (RLDCs) with constant query complexity and almost linear codeword length over the binary alphabet, and used them to obtain significantly-improved constructions of Probabilistically Checkable Proofs. In this work, we study RLDCs in the standard Hamming-error setting, and introduce their variants in the insertion and deletion (Insdel) error setting. Standard LDCs for Insdel errors were first studied by Ostrovsky and Paskin-Cherniavsky (Information Theoretic Security, 2015), and are further motivated by recent advances in DNA random access bio-technologies (Banal et al., Nature Materials, 2021), in which the goal is to retrieve individual files from a DNA storage database. Our first result is an exponential lower bound on the length of Hamming RLDCs making 2 queries (even adaptively), over the binary alphabet. This answers a question explicitly raised by Gur and Lachish (SICOMP 2021) and is the first exponential lower bound for RLDCs. Combined with the results of Ben-Sasson et al., our result exhibits a "phase-transition"-type behavior on the codeword length for some constant-query complexity. We achieve these lower bounds via a transformation of RLDCs to standard Hamming LDCs, using a careful analysis of restrictions of message bits that fix codeword bits. We further define two variants of RLDCs in the Insdel-error setting, a weak and a strong version. On the one hand, we construct weak Insdel RLDCs with almost linear codeword length and const

关键词： Decoding

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Fidelity and diversity metrics for validating hierarchical synthetic data: Application to residential energy demand

Fidelity and diversity metrics for validating hierarchical s...

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IEEE International Conference on Big Data

作者： Swapna Thorve Anil Vullikanti Henning S Mortveit Samarth Swarup Madhav V. Marathe Department of Computer Science Biocomplexity Institute University of Virginia Network Systems Science and Advanced Computing Biocomplexity Institute University of Virginia Department of Engineering Systems and Environment Biocomplexity Institute University of Virginia

ISBN: (纸本)9781665480468

Synthetic data is gaining rapid importance in many application domains due to privacy issues, bias, lack, or simply unavailability of real data. It is important that the synthetic data be a good representation of real data for successfully completing the task at hand. Thus, devising characteristic validation metrics is crucial and remains an open problem in many domains (e.g., image generation). Good validation metrics must be able to disentangle the differences between the quality and the variability coverage of the synthetic data. We propose to use a 3-dimensional metric (precision α, recall β, coverage γ) to describe the fidelity and diversity of the synthetic data. In this paper, we improve on existing definitions of precision, recall, and coverage to extend to large scale time series data. Traditional nearest neighbor manifolds from the literature are replaced by unsupervised learning techniques such as clustering to deal with large scale fine resolution time series while computing the validation metrics. The proposed metrics are employed to validate synthetic data in the domain of residential energy demand. In addition, we extend these definitions to datasets that have a natural hierarchical structure. We propose a hierarchical data-tree model in which precision, recall, and coverage can be computed at multiple inherent (and/or custom) levels of groupings of the data.

关键词： Measurement Manifolds Image synthesis Time series analysis Machine learning Big Data Data models

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