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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Gupta, Sunil Gayathri, N. School of Computer & System Sciences Department of Computer & System Sciences Jaipur National University Jaipur India Galgotias University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665456876

A construction investigation and configuration process that is imposed on the development of a software item is known as the software development life cycle. Testing is now an important and crucial phase in the software development life cycle in every organisation. Fundamentally important to the software development cycle is testing. Testing should begin early in the software development process to prevent potential bugs, reduce costs, and aid in determining requirements. Software testing is done with the intention of strengthening software and preventing problems. The testing phase of the software development life cycle is critical and vital. However, the manner in which it is finished varies from one association to the next. Software testing has evolved into a component of development, and it is wiser to start testing in the early phases in order to avoid problems by fixing bugs at the end. Additionally, the purpose of testing in the software development life cycle is to improve steadfast quality, execution, and other crucial components that may be characterized as SRS. The SDLC consist an explanatory plan that described to create, schedule, and maintain specific software. The organizational framework that explains the tasks carried out at each level of the software development procedure. © 2022 IEEE.

关键词： Software testing

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Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

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IEEE Access 2024年 12卷 82443-82468页

作者： Sana, Laraib Nazir, Muhammad Mohsin Yang, Jing Hussain, Lal Chen, Yen-Lin Ku, Chin Soon Alatiyyah, Mohammed Alateyah, Sulaiman Abdullah Por, Lip Yee Lahore College for Women University Department of Computer Science Punjab Lahore54000 Pakistan Universiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia The University of Azad Jammu and Kashmir Department of Computer Science and IT Neelum Campus Azad Kashmir13230 Pakistan King Abdullah Campus The University of Azad Jammu and Kashmir Department of Computer Science and IT Azad Kashmir Muzaffarabad13100 Pakistan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia Prince Sattam Bin Abdulaziz University College of Sciences and Humanities-Aflaj Department of Computer Science Al-Kharj16278 Saudi Arabia Qassim University College of Computer Department of Computer Engineering Buraydah52571 Saudi Arabia

The ever-expanding Internet of Things (IoT) landscape presents a double-edged sword. While it fosters interconnectedness, the vast amount of data generated by IoT devices creates a larger attack surface for cybercriminals. Intrusions in these environments can have severe consequences. To combat this growing threat, robust intrusion detection systems (IDS) are crucial. The data comprised by this attack is multivariate, highly complex, non-stationary, and nonlinear. To extract the complex patterns from this complex data, we require the most robust, optimized tools. Machine learning (ML) and deep learning (DL) have emerged as powerful tools for IDSs, offering high accuracy in detecting and preventing security breaches. This research delves into anomaly detection, a technique that identifies deviations from normal system behavior, potentially indicating attacks. Given the complexity of anomaly data, we explore methods to improve detection performance. This research investigates the design and evaluation of a novel IDS. We leverage and optimize supervised ML methods like tree-based Support Vector Machines (SVM), ensemble methods, and neural networks (NN) alongside the cutting-edge DL approach of long short-term memory (LSTM) and vision transformers (ViT). We optimized the hyperparameters of these algorithms using a robust Bayesian optimization approach. The implemented ML models achieved impressive training accuracy, with Random Forest and Ensemble Bagged Tree surpassing 99.90% of accuracy, an AUC of 1.00, an F1-score, and a balanced Matthews Correlation Coefficient (MCC) of 99.78%. While the initial deep learning LSTM model yielded an accuracy of 99.97%, the proposed ViT architecture significantly boosted performance with 100% of all metrics, along with a validation accuracy of 78.70% and perfect training accuracy. This study demonstrates the power of our new methods for detecting and stopping attacks on Internet of Things (IoT) networks. This improved detection offers

关键词： Intrusion detection

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Molecular Code-Division Multiple-Access for Nano-Sensor Networks: Signaling, Detection, and Performance

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IEEE Sensors Journal 2025年第12期25卷 23003-23017页

作者： Gao, Weidong Shi, Lu Yang, Lie-Liang Wuxi University College of Electronic Information Engineering Jiangsu Province Engineering Research Center of Photonic Devices and System Integration for Communication Sensing Convergence Wuxi214105 China Chongqing University of Science and Technology School of Intelligent Technology and Engineering Chongqing401331 China University of Southampton School of Electronics and Computer Science SO17 1BJ United Kingdom

o accomplish relatively complex tasks of, such as, monitoring in vivo, information collected by different nano-sensors (NSs) is usually sent via multiple-access channels to fusion centers (FCs) for further processing.o accomplish relatively complex tasks of, such as, monitoring in vivo, information collected by different nano-sensors (NSs) is usually sent via multiple-access channels to fusion centers (FCs) for further processing.T Relying on two types of molecules, in this paper, a molecular code-division multiple-access (MoCDMA) scheme is designed for multiple NSs to simultaneously send information to an access-point (AP) in a diffusive molecular communications (DMC) environment. We assume that different NSs may have different distances from AP, which generates ‘nearfar’ effect. Correspondingly, the uniform and channel-inverse based molecular emission schemes are proposed for NSs to emit information molecules. To facilitate the design of different signal detection schemes, the received signals by AP are represented in different forms. Specifically, by considering the limited computational power of nano-devices, three low-complexity detectors are designed in the principles of matched-filtering (MF), zero-forcing (ZF), and minimum mean-square error (MMSE). The noise characteristics in MoCDMA systems and the complexity of various detection schemes are analyzed. The error performance of the MoCDMA systems with various molecular emission and detection schemes is demonstrated and compared. Our studies and performance results demonstrate that MoCDMA constitutes a promising scheme for supporting multiple-access transmission in DMC, while the channel-inverse based transmission can ensure the fairness of communication qualities (FoCQ) among different NSs. Furthermore, different detection schemes may be implemented to attain a good trade-off between implementation complexity and communication reliability. © 2001-2012 IEEE.

关键词： Mean square error

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DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching 39

DAMPER: A Dual-Stage Medical Report Generation Framework wit...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Diagnosis

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Evaluation of the Software Development Life Cycle Model with Value of Testing and Quality Control 1

Evaluation of the Software Development Life Cycle Model with...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Singh, Vikram Annamalai, S. School of Computer & System Sciences Department of Computer & System Sciences Jaipur National University Jaipur India Galgotias University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665456876

One of the most noteworthy, essential, and important issues in the PC world is software project development. It is a portion of the life cycle for developing frameworks (SDLC). The goal of the SDLC is to reduce the likelihood of failure and disappointment while improving the quality of the software product. The structure that includes multiple tasks and assignments to be completed during the software development process is known as the SDLC. In order to examine existing models, have included five of these SDLC models in this paper: the cascade model, the angular model, the twisting model, and the iterative model. As a result, support and upgrades for the software should be based on current requirements for long-term and stable usage. This study illustrates the concept of testing and its role in quality assurance, experimentation and test levels, and the ongoing planning, execution, and observation of tests. This outline was created with the intention of focusing on the current SDLC sequence.. Approx 300 circulations were selected in addition to the crucial accentuation carried out with embeddings inquiry toward the open-source data collections. © 2022 IEEE.

关键词： Quality control

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An Investigation of the Current State of Digital Platforms and Their Prospects for the Future 1

An Investigation of the Current State of Digital Platforms a...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Rao, Mahainder Kumar, S Rakesh School of Computer & System Sciences Department of Computer & System Sciences Jaipur National University Jaipur India Galgotias University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665456876

Researchers in the field of information systems are becoming more interested in this subject as a result of the increased use of digital platforms as a new way to organise social and commercial activities. As a result, over the past ten years, the literature on digital platforms has greatly increased. However, as platforms change and a number of problems remain unsolved, academics call for greater research on the subject. Therefore, a review of the existing literature is necessary to better understand what has been accomplished and the potential avenues for future study. A thorough review of the literature on digital platforms, which included 100 publications from IS and management journals, was done by our team. The study's key findings point to some ambiguity in how digital platforms are currently conceptualised. They also emphasise how multifaceted digital platforms are, which reflects the variety of ways they are used in real life. © 2022 IEEE.

关键词： Human resource management

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MeHyper: Accelerating Hypergraph Neural Networks by Exploring Implicit Dataflows 31

MeHyper: Accelerating Hypergraph Neural Networks by Explorin...

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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Zhao, Wenju Yao, Pengcheng Chen, Dan Zheng, Long Liao, Xiaofei Wang, Qinggang Ma, Shaobo Li, Yu Liu, Haifeng Xiao, Wenjing Sun, Yufei Zhu, Bing Jin, Hai Xue, Jingling Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China National University of Singapore School of Computing 119077 Singapore Guangxi University School of Computer Electronics and Information NanNing530004 China University of New South Wales School of Computer Science and Engineering SydneyNSW2052 Australia

ISBN: (纸本)9798331506476

Hypergraph Neural Networks (HGNNs) are increasingly utilized to analyze complex inter-entity relationships. Traditional HGNN systems, based on a hyperedge-centric dataflow model, independently process aggregation tasks for hyperedges and vertices, leading to significant computational redundancy. This redundancy arises from recalculating shared information across different tasks. For the first time, we identify and harness implicit dataflows (i.e., dependencies) within HGNNs, introducing the microedge concept to effectively capture and reuse intricate shared information among aggregation tasks, thereby minimizing redundant computations. We have developed a new microedge-centric dataflow model that processes shared information as fine-grained microedge aggregation tasks. This dataflow model is supported by the Read-Process-Activate-Generate execution model, which aims to optimize parallelism among these tasks. Furthermore, our newly developed MeHyper, a microedge-centric HGNN accelerator, incorporates a decoupled pipeline for improved computational parallelism and a hierarchical feature management strategy to reduce off-chip memory accesses for large volumes of intermediate feature vectors generated. Our evaluation demonstrates that MeHyper substantially outperforms the leading CPUbased system PyG-CPU and the GPU-based system HyperGef, delivering performance improvements of 1,032.23 × and 10.51 ×, and energy efficiencies of 1,169.03 × and 9.96 ×, respectively. © 2025 IEEE.

关键词： Redundancy

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Enhancing Data Security in Networking Through AES Encryption and Steganography: A Comparative Analysis of DES and AES Algorithms 3

Enhancing Data Security in Networking Through AES Encryption...

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Balodi, Ayush Malik, Priyansh Rawat, Prerna Das, Purushottam Singh, Karan Manjul, Manisha Graphic Era Hill University Computer Science and Engineering Uttarakhand India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand India School of Computer and System Science Jawaharlal Nehru University New Delhi India Dseu Gb Pant Okhla Campus I Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350342338

Information transfer has accelerated in the modern era. Numerous security difficulties were caused by the increasing transmission. The most popular methods to address this issue are cryptography and steganography. Steganography is the method of hiding secret data with a non- secret file to avoid data breaches whereas cryptography uses programs to encrypt and decrypt the data to make it secure. They both need to be combined so as to build a secure network that is not easily visible for hackers to breach as it looks like average data, despite the fact that these methods are frequently used together to reach higher degrees of security, a highly secure system is still required to transfer information through any communication channel while reducing the possibility of intrusion. In this paper, we suggest a data encryption system that makes use of effective AES and DES cryptography techniques. We present a way for secure data transfer by combining these encryption techniques with the technique of steganography. Our method effectively employs steganography techniques by inserting the encrypted text within an image's pixels. In addition to improving the security of data hiding compared to conventional techniques, this paper also enables us to assess the advantages and disadvantages of AES and DES in the context of data encryption and steganography. © 2023 IEEE.

关键词： AES (Advanced Encryption Standard) Cryptography DES (Data Encryption Standard) Image data hiding Steganography

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Zero-phase CARIMA Filtering and Application in Wind-storage system Sizing and Power Dispatch Optimization

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CSEE Journal of Power and Energy systems 2024年第6期10卷 2283-2295页

作者： Wei Wang Peng Chen Guorui Ren Jizhen Liu Fang Fang Zhe Chen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Department of Energy Technology Aalborg University9220 AalborgDenmark

Hybrid energy storage system(HESS)is an effective way to mitigate wind power fluctuations on multi-time scale,and can improve influence of large-scale grid-connected wind power on stability and reliability of power system.A novel methodology named zero-phase controlled auto-regressive integrated moving-average(CARIMA)filter is proposed to integrate HESS to smooth wind power ***,a design method for zero-phase CARIMA filter is provided,and then used to determine grid-connected power for a wind storage system and size *** reasons,direct current(DC)component caused by energy storage efficiency and grid-connected power delay caused by phase shift,for causing superfluous energy storage configuration are *** addition,a nonlinear programming scheduling strategy considering battery degradation is *** imbalance caused by efficiency difference during dynamic adjustment of energy storage output power is ***,thermostatically controlled loads(TCLs)are integrated in sizing and scheduling HESS to reduce energy storage demand and improve operating conditions of energy ***,effectiveness of the proposed strategy is verified by a case study.

关键词： CARIMA demand response energy storage optimization wind power zero-phase

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection systems

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computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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