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Development of Blockchain Integrated Agricultural Land Lease Framework with a Voting-Based Consensus Protocol Using Event-B 4th

Development of Blockchain Integrated Agricultural Land Leas...

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4th international conference on Advanced Network Technologies and intelligent computing, ANTIC 2024

作者： Verma, Sudhani Yadav, Divakar Chandra, Girish Department of Computer Science and Engineering Institute of Engineering and Technology AKTU-Jankipuram Uttar Pradesh Lucknow226021 India

ISBN: (纸本)9783031837821

India’s agriculture sector, contributes 18% to the national GDP, remains largely unorganized. this study proposes a blockchain-based solution to digitize land lease contracts, aiming to reduce risks of document forgery and fraud. the proposed system has transactions such as stamp procurement, registration of land and users etc. which are validated and stored using blockchain. To ensure fairness in execution of transactions, a voting-based consensus algorithm is also proposed. Compared to conventional consensus methods, the proposed algorithm achieves a 39% increase in throughput by reducing message exchange overhead. the proposed algorithm is formally verified using Event-B. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Blockchain Consensus Protocols distributed Ledger technology (DLT) Event-B Formal Methods

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ADMEdge: Accelerating Edge computing through Co-design for Enhanced Data Mobility

ADMEdge: Accelerating Edge Computing Through Co-design for ...

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17th Asian conference on intelligent Information and Database Systems, ACIIDS 2025

作者： Kiet, Tran Anh thinh, Tran Ngoc Ngo, thai Le, Long Tan 268 Ly Thuong Kiet District 10 Ho Chi Minh City Viet Nam Thu Duc Ho Chi Minh City Viet Nam School of Computer Science The University of Sydney SydneyNSW Australia

ISBN: (纸本)9789819660049

In edge computing, optimizing data mobility is crucial for minimizing latency and maximizing bandwidth efficiency by processing data closer to its source. this reduces the need to transfer large amounts of data to centralized servers, enabling faster insights and real-time decision-making. However, data mobility challenges in embedded machine learning systems arise from inefficient data transfer, software, and algorithmic issues that result in latency, CPU utilization that hinders Internet of things (IoTs) performance and reliability. this paper introduces ADMEdge (Accelerating Data Mobility at the Edge), a novel software/hardware co-design framework aimed at optimizing data movement in FPGA-based System-on-Chip (SoC) edge devices through the utilization of Direct Memory Access (DMA), buffer, and shared memory techniques. Testing is conducted on a Kria KV260 IoT SoC, which incorporates a PicoRV32 RISC-V core and a Deep Neural Network accelerator, using representative datasets to simulate real-world scenarios. Incorporating shared memory significantly reduces latency by removing the recompilation step, leading to a 9.5-fold speedup in real-time image processing at higher workloads. Alongside a 49% performance gain from buffering techniques, data throughput scales efficiently with larger data sizes, with DMA demonstrating up to 226.98 times faster communication in specific scenarios. Overall, these optimizations yield a 44% improvement in system performance, highlighting the potential of ADMEdge to enhance edge computing applications in the Internet of things. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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Improving Stress Detection with Synthetic Datasets: GPT-4o-Mini and Transformer Model Evaluation

Improving Stress Detection with Synthetic Datasets: GPT-4o-...

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17th Asian conference on intelligent Information and Database Systems, ACIIDS 2025

作者： Badran, Nabeel Le, John Le, thanh Uchiya, Takahiro School of Computing and Information Technology University of Wollongong Northfields Avenue WollongongNSW2522 Australia School of Business Faculty of Business and Law University of Wollongong Wollongong Australia Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya Japan

ISBN: (纸本)9789819660070

the rise of mental health issues as a public health concern necessitates efficient detection methods. Social media is now an integral part of daily life, where users frequently share stress-related experiences. this study investigates synthetic data generation techniques to expand datasets and improve the accuracy of stress analysis in social media posts using machine learning models. Using the OpenAI GPT-4o-mini model through its API, we implement two text augmentation techniques, paraphrasing and inspirational augmentation, generating diverse datasets with original, paraphrased, and inspirationally augmented texts. Evaluations on BERT and DistilBERT show that the combined dataset led to the best result, with BERT achieving an accuracy of 84.74%, compared to 73.94% with the baseline. Our experimental evaluation, conducted on real-world user posts, demonstrates that synthetic datasets significantly improve stress analysis accuracy, highlighting its potential for mental health detection in social media. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Natural language processing systems

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A Feature Transformation Technique for Improving Ensemble Learning Systems

A Feature Transformation Technique for Improving Ensemble L...

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17th Asian conference on intelligent Information and Database Systems, ACIIDS 2025

作者： Nguyen, Truong thanh Vu, Hieu Dang, Truong Elyan, Eyad Vu, thanh Son Nguyen, Tien thanh School of Computing Engineering and Technology RGU Aberdeen United Kingdom School of Information and Communication Technology Thuyloi University Hanoi Viet Nam

ISBN: (纸本)9789819660049

In this study, we propose a feature transformation approach to improve the performance of Ensemble Learning Systems. Our method operates on the predictions of base classifiers within an ensemble system, known as meta-data, which are produced by applying a Cross-Validation procedure on the training data using various learning algorithms. the goal is to transform the meta-data to obtain a better combined output. Specifically, we compute the center vectors associated with each class label on the meta-data. We then increase the dimensionality of the meta-data by concatenating it with residuals, which are obtained by subtracting each vector in the meta-data from the associated center vector. A classifier is then trained on this transformed meta-data to create the combiner which will be used to combine outputs of base classifiers for ensemble prediction. Our method is compared with three well-known ensemble methods: deep forest (gcForest), Decision Template (DT), Sum rule, and the same ensemble without transformation. We evaluated the experimental methods on 22 UCI datasets and used the Friedman and Nemenyi tests to statistically compare their performances. the results show that our method outperforms all base classifiers and the benchmark algorithms on experimental datasets. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Security Enhancement in MANET using Modified Crow Search Optimization Algorithm for IoT Applications

Security Enhancement in MANET using Modified Crow Search Opt...

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Emerging Smart computing and Informatics (ESCI), conference on

作者： R. Swathi Subhadra Perumalla Arun M D.V.N. Bharathi E. Srinivas V. Gokula Krishnan Department of EEE P.V.P. Siddhartha Institute of Technology Vijayawada India Department of CSE Vignana Bharathi Institute of Technology Ghatkesar Hyderabad India Department of ECE Panimalar Engineering College Chennai Tamil Nadu India Department of ECE S.R.K.R. Engineering College Bhimavaram A.P India Department of ECE Anurag University Ghatkesar Hyderabad India Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Mobile Ad hoc Networks (MANETs) are the integral part of advanced IoT applications, offering flexibility as well as scalability in dynamic network conditions. However, their distributed nature and reliance on wireless communication become them extremely vulnerable to security threats. this paper suggested a Modified Crow Search Optimization (MCSO) algorithm to improve the security of MANETs for IoT based applications. the recommended approach explores the intelligent foraging behavior of crows to provide secure transmission from malicious attacks. the MCSO algorithm is assessed against established methods in terms of convergence analysis. the performance of MCSO is assessed through latency as well as throughput analysis, signifying its greater efficiency over standing procedures such as SASFO, CHHSO and CSA. the latency examination discloses that MCSO reliably attains the lowest latency, fluctuating from 0.01 to 0.15, compared to larger latencies identified in CHHSO (0.03–0.16), CSA (0.03– 0.19) as well as SASFO (0.05–0.20). Furthermore, the throughput analysis shows that MCSO preserves the maximum throughput, begining at 96.7% and dropping minimally to 90.4% by the 200 th round. these outcomes authenticate MCSO’s effectiveness in dropping latency and preserving higher throughput, thus making it a robust as well as reliable result for improving performance of a network in MANETs, mainly in IoT applications.

关键词： Wireless communication Scalability Network security throughput Internet of things Reliability Mobile computing Optimization Mobile ad hoc networks Convergence

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Task Scheduling in distributed Real-Time Systems Using Hybrid Model Based on ACO-GA 4th

Task Scheduling in Distributed Real-Time Systems Using Hybr...

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4th international conference on Advanced Network Technologies and intelligent computing, ANTIC 2024

作者： Sharma, Anchal Sharma, Sangeeta thakur, Sanat Kumar, Ankush Mahato, Dharmendra Prasad National Institute of Technology Hamirpur Himachal Pradesh Hamirpur177 005 India

ISBN: (纸本)9783031837821

Task scheduling in distributed real-time systems (DRTS) is a complex problem due to the heterogeneous nature of processors and the need for efficient resource utilization. this paper presents a novel hybrid model combining Ant Colony Optimization (ACO) and Genetic Algorithm (GA) to address the task scheduling problem. the proposed model aims to minimize system cost and response time while maximizing system reliability by leveraging the exploration capabilities of ACO and the solution refinement strength of GA. the hybrid algorithm operates in two phases: the first phase uses ACO to generate initial task assignments by dynamically exploring the search space based on pheromone trails and heuristic information. In the second phase, GA refines these solutions through crossover and mutation operators optimizing the task allocation further. Experimental results demonstrate that the proposed ACO-GA hybrid model significantly improves task scheduling performance compared to standalone algorithms by achieving better resource utilization, by reducing execution time, and by enhancing the system reliability. this approach is especially effective for real-time applications where optimal task assignment is critical to system performance. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ant colony optimization

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An Adaptive Discrete Human Learning Optimization for TSP-Like Problems 8th

An Adaptive Discrete Human Learning Optimization for TSP-Lik...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wang, Ling Kang, Xing Wang, Mengzhen Yao, Jun Pfaender, Fabien Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronics Engineering and Automation Shanghai University Shanghai200444 China Sino-European School of Technology Shanghai University Shanghai200444 China

ISBN: (纸本)9789819602933

Human learning optimization (HLO) is a novel inborn binary meta-heuristic algorithm inspired by a simple human learning model. Although HLO, as a binary algorithm, can directly solve discrete (integer) problems, the efficiency may be significantly spoiled for the large-scale discrete problems due to the curse of dimension. therefore, an adaptive discrete human learning optimization for TSP-like problems (ADHLOT) is proposed in which the random learning operator, individual learning operator, social learning operator and the re-learning operation of HLO are extended or re-designed to improve the efficiency of the algorithm for TSP-like problems. Furthermore, an adaptive strategy is used in ADHLOT to dynamically adjust the social learning rate for achieving a better balance between exploration and exploitation. Finally, ADHLOT is evaluated by solving TSP benchmark problems and compared with recent meta-heuristic algorithms. the experimental results show that the ADHLOT has obvious advantages and shows promising potential for applications. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Energy-Efficient Edge Query Processing for Smart City Using Query Prediction

Energy-Efficient Edge Query Processing for Smart City Using...

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17th Asian conference on intelligent Information and Database Systems, ACIIDS 2025

作者： Gandhi, Kalgi Bhise, Minal Distributed Databases Group DA-IICT Gujarat Gandhinagar India

ISBN: (纸本)9789819660049

the swift proliferation of edge devices in large-scale systems poses challenges in resource utilization and data management, with limited processing power, memory, and bandwidth, especially for resource-intensive join operations. While Edge Query Processing (EQP) accelerates hash joins using equi-height binning, it is inefficient for real-time processing and scans unnecessary cachelines. this paper introduces Energy-Efficient Edge Query Processing (EEEQP), which optimizes hash joins by predicting query workloads in advance, making it ideal for real-time edge query processing. EEEQP has three modules Pre-processing, Prediction, and Blocking and Hashing, which compute bins based on predicted workloads. It also handles query execution and optimizes CPU, RAM, and I/O usage. Evaluations using a Benchmark dataset and a Real-World dataset from the Smart City domain demonstrate significant improvements in the Percentage of Cachelines Read PCR, Query Execution Time QET, and Resource Utilization. Real-world PCR and QET show 49% and 8% improvement, respectively. the proposed technique has been demonstrated to work well for scaled and skewed data. Although PCR is an indirect measure of energy consumption, the presented work also measures energy consumption directly by performing energy-efficiency experiments. EEEQP shows 2%, 38%, and 46% gain in CPU, RAM, and I/O, respectively. this enhances system performance and supports sustainable practices in database management, making EEEQP a promising choice for resource-constrained edge applications. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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NOBGP: A Novel Optimized Balanced Graph Partitioning Algorithm 19th

NOBGP: A Novel Optimized Balanced Graph Partitioning Algorit...

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19th CCF conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Chen, Jiebin Hu, Ziqiang Ye, Renjie Zhang, Qishan Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Xianda College of Economics and Humanities Shanghai International Studies University Shanghai China

ISBN: (纸本)9789819623723

Large-scale graphs have become prevalent with the advent of the big data era. distributed graph computing systems are commonly used for processing and analyzing large-scale graphs, with graph partitioning being a key prerequisite for their efficient computation. Graph partitioning aims to balance the load across partitions while minimizing the number of cut-edges. Moreover, it should achieve high efficiency and scalability. However, the existing popular graph partitioning algorithms do not fully take into account the internal topology of real-world graphs, which affects the final partition quality and convergence. Meanwhile, they easily fall into the local optimum due to partition load constraints. this paper introduces a Novel Optimized Balanced Graph Partitioning algorithm (NOBGP). First, we propose an initialization strategy based on label propagation of core vertices to achieve initial partitions with good locality and accelerate convergence. Second, we optimize the label propagation process to ensure balanced partitions and propose a probability-based disruption strategy to avoid the local optimum. We implement NOBGP on the distributed graph computing framework GraphX. Extensive experimental results on real-world graphs show that the proposed algorithm is scalable and performs better than the existing algorithms. We also run PageRank and Louvain applications using the graph partitioning results to demonstrate the efficiency of our algorithm. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph algorithms

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Detection and Mitigation of IoT Based DDoS Attack Using Extended MUD Enabled Device Profiling Techniques 4th

Detection and Mitigation of IoT Based DDoS Attack Using Exte...

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4th international conference on Advanced Network Technologies and intelligent computing, ANTIC 2024

作者： thiruppathi, K. Jaidhar, C.D. Society for Electronic Transactions and Security Chennai India Information Technology Department National Institute of Technology Karnataka Surathkal India

ISBN: (纸本)9783031837821

In today’s landscape, the burgeoning Internet of things (IoT) infrastructure underscores the imperative for implementing top-tier security measures to safeguard the IoT realm. this domain has permeated various sectors, including automotive, smart cities, healthcare, industries, and the power sector. the rising ubiquity of IoT devices has drawn the attention of malicious actors, presenting a significant risk of exploitation in insecure, constrained environments. Among the foremost threats in the IoT domain is the distributed Denial of Service (DDoS) attack, capable of swiftly devastating entire IoT infrastructures. To address this issue, this work proposes a detection and mitigation model aimed at thwarting DDoS attacks in IoT environments. A hybrid feature selection technique is proposed to identify the most effective features for detecting attacks, and Convolutional Neural Network (CNN) is used to identify the suspicious IoT nodes. Further utilized the extended Manufacturer Usage Description (MUD) enabled device profiling techniques to compare the malicious node profiles with existing benign MUD profiles to find the malicious nodes. Linux IPtables is enabled to efficiently filter DDoS attacks. the proposed work is for detecting IoT DDoS attacks, alongside a self-reliant mitigation strategy to effectively filter these attacks. this strategy aims to minimize the impact of blocking legitimate network traffic from IoT devices. the effectiveness of the proposed hybrid feature selection was evaluated by using the CICIoT2023 dataset. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Denial-of-service attack

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