BenchCouncil Transactions on Benchmarks, Standards and Evaluations最新文献

Exposing financial shenanigans: The role of Indian accounting standards (Ind AS) in enhancing corporate accountability and governance 揭露财务诡计：印度会计准则（Ind AS）在加强公司责任和治理中的作用

BenchCouncil Transactions on Benchmarks, Standards and Evaluations Pub Date : 2025-07-11 DOI: 10.1016/j.tbench.2025.100228

Sunil Kumar

{"title":"Exposing financial shenanigans: The role of Indian accounting standards (Ind AS) in enhancing corporate accountability and governance","authors":"Sunil Kumar","doi":"10.1016/j.tbench.2025.100228","DOIUrl":"10.1016/j.tbench.2025.100228","url":null,"abstract":"<div><div>The Indian Accounting Standards (Ind AS) play a pivotal role in reducing financial impropriety. These standards significantly enhance the accountability, accuracy, and transparency of financial reporting, thereby serving an essential function in deterring financial malfeasance. Such malfeasance includes deceptive accounting practices, misleading reporting, and the distortion of earnings, all of which undermine investor confidence, disrupt market integrity, and adversely affect the economy. The Ind AS, aligned with the International Financial Reporting Standards (IFRS), provide a comprehensive and robust framework that substantially improves the quality of financial reporting. The article outlines the significant benefits of Ind AS for financial reporting, such as increased transparency and accuracy. It presents case studies illustrating how the application of the standard has effectively addressed and mitigated financial discrepancies. Furthermore, the article examines the challenges organisations face in adopting Ind AS, including the complexities of transitioning from previous accounting standards and the need for extensive system reforms and personnel training. By elucidating these challenges, the article offers a thorough analysis of the effectiveness of Ind AS in addressing financial malpractice. It emphasises its role in fostering a more transparent and responsible financial reporting environment.</div></div>","PeriodicalId":100155,"journal":{"name":"BenchCouncil Transactions on Benchmarks, Standards and Evaluations","volume":"5 3","pages":"Article 100228"},"PeriodicalIF":0.0,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144697003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An investigation into the preparation and evaluation of the physio-mechanical properties of glass-cotton, glass-jute, and glass-banana fiber-reinforced epoxy composite materials 玻璃-棉、玻璃-黄麻和玻璃-香蕉纤维增强环氧复合材料的制备及其物理力学性能的研究

BenchCouncil Transactions on Benchmarks, Standards and Evaluations Pub Date : 2025-06-01 DOI: 10.1016/j.tbench.2025.100218

Alberuni Aziz , Farjana Parvin , Md. Kajol Hossain

{"title":"An investigation into the preparation and evaluation of the physio-mechanical properties of glass-cotton, glass-jute, and glass-banana fiber-reinforced epoxy composite materials","authors":"Alberuni Aziz , Farjana Parvin , Md. Kajol Hossain","doi":"10.1016/j.tbench.2025.100218","DOIUrl":"10.1016/j.tbench.2025.100218","url":null,"abstract":"<div><div>Fibrous composite materials are gaining popularity in various applications because of their exceptional attributes, such as high strength-to-weight ratio, high impact resistance, near-zero thermal expansion, and good corrosion resistance. These materials combine two or more fibrous materials with several physical and chemical properties to create a material with enhanced properties. The development of sustainable and environmentally friendly composite materials is increasing day by day to reduce environmental pollution and promote a more sustainable future. This research explores the physical and mechanical characteristics of cotton-glass, banana-glass, and jute-glass-reinforced epoxy composites, aiming to define their suitability for various applications. Tensile strength, flexural strength, and water absorption are the fundamental properties evaluated in this work. The hand lay-up technique was used to fabricate the composite, which involves manually layering the fiber and the matrix material. The study's findings provide significant insights into the potential application of composite materials in various industrial settings. Moreover, using sustainable and eco-friendly composite materials can help reduce environmental pollution. Although glass fiber is not biodegradable, it is easily recyclable. Other fibers used in this study are biodegradable, so it is a sustainable approach. In summary, studying the mechanical properties of composite materials provides valuable insights into their potential use in lightweight and durable diverse applications. Continued research may lead to more advanced composite materials with enhanced features for broader applications.</div></div>","PeriodicalId":100155,"journal":{"name":"BenchCouncil Transactions on Benchmarks, Standards and Evaluations","volume":"5 2","pages":"Article 100218"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative study of deep learning models for Parkinson’s disease detection 深度学习模型在帕金森病检测中的比较研究

BenchCouncil Transactions on Benchmarks, Standards and Evaluations Pub Date : 2025-06-01 DOI: 10.1016/j.tbench.2025.100219

Abdulaziz Salihu Aliero, Neha Malhotra

{"title":"Comparative study of deep learning models for Parkinson’s disease detection","authors":"Abdulaziz Salihu Aliero, Neha Malhotra","doi":"10.1016/j.tbench.2025.100219","DOIUrl":"10.1016/j.tbench.2025.100219","url":null,"abstract":"<div><div>Parkinson’s disease (PD) is a progressive neurodegenerative disorder that affects movement and cognition, impacting millions of people worldwide. The diagnosis of PD primarily relies on clinical tests, which can often result in delayed identification of the disease. Recent advancements in data-driven methods using deep learning have demonstrated potential for improving early diagnosis by utilizing clinical and vocal inputs. This study conducted a comparative analysis of five deep learning models: Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), Autoencoder, and Generative Adversarial Network (GAN), specifically for the detection of PD using vocal biomarkers. Among these models, the MLP achieved the highest predictive accuracy at 97.4 %. The RNN, GRU, and Autoencoder models attained a similar accuracy rate of 87.2 %. In contrast, the GAN model yielded an accuracy of only 76.9 %. The UCI vocal dataset from Kaggle was utilized in this research, along with extensive data preprocessing techniques to address missing values. Performance evaluation was conducted using multiple metrics. The results indicate that deep learning models can effectively diagnose PD using voice data, suggesting their potential to enhance diagnostic accuracy and support clinical decision-making. Furthermore, these models are feasible for large-scale integration into clinical workflows.</div></div>","PeriodicalId":100155,"journal":{"name":"BenchCouncil Transactions on Benchmarks, Standards and Evaluations","volume":"5 2","pages":"Article 100219"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid deep learning model for identifying the cancer type 用于识别癌症类型的混合深度学习模型

BenchCouncil Transactions on Benchmarks, Standards and Evaluations Pub Date : 2025-06-01 DOI: 10.1016/j.tbench.2025.100211

Singamaneni Krishnapriya , Hyma Birudaraju , M. Madhulatha , S. Nagajyothi , K.S. Ranadheer Kumar

{"title":"Hybrid deep learning model for identifying the cancer type","authors":"Singamaneni Krishnapriya , Hyma Birudaraju , M. Madhulatha , S. Nagajyothi , K.S. Ranadheer Kumar","doi":"10.1016/j.tbench.2025.100211","DOIUrl":"10.1016/j.tbench.2025.100211","url":null,"abstract":"<div><div>Despite current advances, cancer remains one of the biggest health challenges globally, and diagnosis must be made earlier to begin treatment. In this work, we introduce a hybrid deep learning-based framework for accurate cancer type and subtype identification by using pre-trained convolutional neural networks, custom deep learning networks, and traditional machine learning classifiers. I have achieved accurate results on more complex cancer datasets using advanced architectures of CNN + LSTM and attention-based models, along with the pre-trained models of VGG19, Xception, and AmoebaNet. Model reliability and interpretability are further improved using ensemble techniques such as confidence-based and XOR fusion. Experimental results in multiple multimodal datasets demonstrate the effectiveness of our hybrid approach by improving precision, recall, and F1 scores in various types of cancer. However, they have promising results and remain challenging to deploy for rare cancer subtypes or explain to gain clinical adoption. The proposed framework provides a basis for personalized cancer by developing machine learning innovations to advance precision medicine.</div></div>","PeriodicalId":100155,"journal":{"name":"BenchCouncil Transactions on Benchmarks, Standards and Evaluations","volume":"5 2","pages":"Article 100211"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144239765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating public bicycle sharing system in Ahmedabad, Gujarat: A multi-criteria decision-making approach 古吉拉特邦艾哈迈达巴德市公共自行车共享系统评估：多标准决策方法

BenchCouncil Transactions on Benchmarks, Standards and Evaluations Pub Date : 2025-06-01 DOI: 10.1016/j.tbench.2025.100220

T.S. Shagufta , Dimpu Byalal Chindappa , Seelam Srikanth , Subhashish Dey

{"title":"Evaluating public bicycle sharing system in Ahmedabad, Gujarat: A multi-criteria decision-making approach","authors":"T.S. Shagufta , Dimpu Byalal Chindappa , Seelam Srikanth , Subhashish Dey","doi":"10.1016/j.tbench.2025.100220","DOIUrl":"10.1016/j.tbench.2025.100220","url":null,"abstract":"<div><div>This study evaluates the existing Public Bicycle Sharing System (PBSS) at Ahmedabad, Gujarat by applying four decision-making methods such as Analytic Hierarchy Process (AHP), Fuzzy AHP, Analytic Network Process (ANP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The study aims to identify the most effective strategies for improving PBSS, focusing on safety, infrastructure, user convenience, and environmental impact. The analysis shows that Enhanced Non-Motorized Transport (NMT) Infrastructure and Expansion of Bicycle Networks are the preferred alternatives across all methods. Personal safety and safe cycling infrastructure are identified as critical factors influencing the success of PBSS. Socio-demographic data reveals a male-dominant user base, with financial barriers and safety concerns limiting broader adoption. Positive perceptions of cycle design are noted, though electric and hybrid cycles are preferred due to climatic conditions. Monthly variations in ridership demonstrate significant fluctuations, peaking at 68,529 rides in March, underscoring the need for targeted interventions during peak periods. The study provides a robust framework for transport planners, emphasizing safety, inclusivity, and affordability. Future research should focus on expanding electric cycle options and enhancing gender inclusivity in PBSS.</div></div>","PeriodicalId":100155,"journal":{"name":"BenchCouncil Transactions on Benchmarks, Standards and Evaluations","volume":"5 2","pages":"Article 100220"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LLMs: A game-changer for software engineers? 法学硕士：软件工程师的游戏规则改变者？

BenchCouncil Transactions on Benchmarks, Standards and Evaluations Pub Date : 2025-03-01 DOI: 10.1016/j.tbench.2025.100204

Md. Asraful Haque

引用次数: 0

Regulatory landscape of blockchain assets: Analyzing the drivers of NFT and cryptocurrency regulation 区块链资产的监管格局：分析NFT和加密货币监管的驱动因素

BenchCouncil Transactions on Benchmarks, Standards and Evaluations Pub Date : 2025-03-01 DOI: 10.1016/j.tbench.2025.100214

Junaid Rahman , Hafizur Rahman , Naimul Islam , Tipon Tanchangya , Mohammad Ridwan , Mostafa Ali

{"title":"Regulatory landscape of blockchain assets: Analyzing the drivers of NFT and cryptocurrency regulation","authors":"Junaid Rahman , Hafizur Rahman , Naimul Islam , Tipon Tanchangya , Mohammad Ridwan , Mostafa Ali","doi":"10.1016/j.tbench.2025.100214","DOIUrl":"10.1016/j.tbench.2025.100214","url":null,"abstract":"<div><div>The study analyzes the global regulatory landscape for blockchain assets, particularly cryptocurrencies and non-fungible tokens, focusing on the motivations behind policymaker actions, the diversity of regulatory approaches, the challenges posed by decentralized technologies and provide future regulatory pathways. The study uses a conceptual and mixed-method approach, combining qualitative and quantitative content analysis of 59 peer-reviewed articles selected through the PRISMA framework. Findings reveal that regulation is primarily driven by concerns over consumer protection, financial stability, anti-money laundering, taxation, and environmental sustainability. Regulatory responses vary widely, ranging from the harmonized MiCA framework in the EU to the fragmented enforcement model in the U.S., along with diverse strategies across Asia. Stablecoins, DeFi, and CBDCs emerge as major regulatory frontiers. The study recommends adopting regulatory sandboxes, promoting international coordination, enforcing environmental standards, and building regulatory capacity in emerging economies to balance innovation with risk mitigation. It also highlights the importance of industry self-regulation and technology-assisted compliance in decentralized finance. The limitation of this study is that it relies solely on secondary data sources, which may limit the accuracy of real-time policy impact assessments. Future research should focus on empirical validation and dynamic policy modeling to enhance global governance of digital assets.</div></div>","PeriodicalId":100155,"journal":{"name":"BenchCouncil Transactions on Benchmarks, Standards and Evaluations","volume":"5 1","pages":"Article 100214"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluatology’s perspective on AI evaluation in critical scenarios: From tail quality to landscape 评估学在关键场景下的人工智能评估视角：从尾部质量到景观

BenchCouncil Transactions on Benchmarks, Standards and Evaluations Pub Date : 2025-03-01 DOI: 10.1016/j.tbench.2025.100203

Zhengxin Yang

{"title":"Evaluatology’s perspective on AI evaluation in critical scenarios: From tail quality to landscape","authors":"Zhengxin Yang","doi":"10.1016/j.tbench.2025.100203","DOIUrl":"10.1016/j.tbench.2025.100203","url":null,"abstract":"<div><div>Tail Quality, as a metric for evaluating AI inference performance in critical scenarios, reveals the extreme behaviors of AI inference systems in real-world applications, offering significant practical value. However, its adoption has been limited due to the lack of systematic theoretical support. To address this issue, this paper analyzes AI inference system evaluation activities from the perspective of Evaluatology, bridging the gap between theory and practice. Specifically, we begin by constructing a rigorous, consistent, and comprehensive evaluation system for AI inference systems, with a focus on defining the evaluation subject and evaluation conditions. We then refine the Quality@Time-Threshold (Q@T) statistical evaluation framework by formalizing these components, thereby enhancing its theoretical rigor and applicability. By integrating the principles of Evaluatology, we extend Q@T to incorporate stakeholder considerations, ensuring its adaptability to varying time tolerance. Through refining the Q@T evaluation framework and embedding it within Evaluatology, we provide a robust theoretical foundation that enhances the accuracy and reliability of AI system evaluations, making the approach both scientifically rigorous and practically reliable. Experimental results further validate the effectiveness of this refined framework, confirming its scientific rigor and practical applicability. The theoretical analysis presented in this paper provides valuable guidance for researchers aiming to apply Evaluatology in practice.</div></div>","PeriodicalId":100155,"journal":{"name":"BenchCouncil Transactions on Benchmarks, Standards and Evaluations","volume":"5 1","pages":"Article 100203"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tensor databases empower AI for science: A case study on retrosynthetic analysis 张量数据库为科学赋予AI力量：一个关于反合成分析的案例研究

BenchCouncil Transactions on Benchmarks, Standards and Evaluations Pub Date : 2025-03-01 DOI: 10.1016/j.tbench.2025.100216

Xueya Zhang , Guoxin Kang , Boyang Xiao , Jianfeng Zhan

{"title":"Tensor databases empower AI for science: A case study on retrosynthetic analysis","authors":"Xueya Zhang , Guoxin Kang , Boyang Xiao , Jianfeng Zhan","doi":"10.1016/j.tbench.2025.100216","DOIUrl":"10.1016/j.tbench.2025.100216","url":null,"abstract":"<div><div>Retrosynthetic analysis is highly significant in chemistry, biology, and materials science, providing essential support for the rational design, synthesis, and optimization of compounds across diverse Artificial Intelligence for Science (AI4S) applications. Retrosynthetic analysis focuses on exploring pathways from products to reactants, and this is typically conducted using deep learning-based generative models. However, existing retrosynthetic analysis often overlooks how reaction conditions significantly impact chemical reactions. This causes existing work to lack unified models that can provide full-cycle services for retrosynthetic analysis, and also greatly limits the overall prediction accuracy of retrosynthetic analysis. These two issues cause users to depend on various independent models and tools, leading to high labor time and cost overhead.</div><div>To solve these issues, we define the boundary conditions of chemical reactions based on the Evaluatology theory and propose BigTensorDB, the first tensor database which integrates storage, prediction generation, search, and analysis functions. BigTensorDB designs the tensor schema for efficiently storing all the key information related to chemical reactions, including reaction conditions. BigTensorDB supports a full-cycle retrosynthetic analysis pipeline. It begins with predicting generation reaction paths, searching for approximate real reactions based on the tensor schema, and concludes with feasibility analysis, which enhances the interpretability of prediction results. BigTensorDB can effectively reduce usage costs and improve efficiency for users during the full-cycle retrosynthetic analysis process. Meanwhile, it provides a potential solution to the low accuracy issue, encouraging researchers to focus on improving full-cycle accuracy.</div></div>","PeriodicalId":100155,"journal":{"name":"BenchCouncil Transactions on Benchmarks, Standards and Evaluations","volume":"5 1","pages":"Article 100216"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ethical and regulatory challenges in machine learning-based healthcare systems: A review of implementation barriers and future directions 基于机器学习的医疗保健系统中的伦理和监管挑战：对实施障碍和未来方向的回顾

BenchCouncil Transactions on Benchmarks, Standards and Evaluations Pub Date : 2025-03-01 DOI: 10.1016/j.tbench.2025.100215

Shehu Mohammed, Neha Malhotra

{"title":"Ethical and regulatory challenges in machine learning-based healthcare systems: A review of implementation barriers and future directions","authors":"Shehu Mohammed, Neha Malhotra","doi":"10.1016/j.tbench.2025.100215","DOIUrl":"10.1016/j.tbench.2025.100215","url":null,"abstract":"<div><div>Machine learning significantly enhances clinical decision-making quality, directly impacting patient care with early diagnosis, personalized treatment, and predictive analytics. Nonetheless, the increasing proliferation of such ML applications in practice raises potential ethical and regulatory obstacles that may prevent their widespread adoption in healthcare. Key issues concern patient data privacy, algorithmic bias, absence of transparency, and ambiguous legal liability. Fortunately, regulations like the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the FDA AI/ML guidance have raised important ways of addressing things like fairness, explainability, legal compliance, etc.; however, the landscape is far from risk-free. AI liability is another one of the gray areas approaching black, worrying about who is liable for an AI medical error — the developers, the physicians, or the institutions. The study reviews ethical risks and potential opportunities, as well as regulatory frameworks and emerging challenges in AI-driven healthcare. It proposes solutions to reduce bias, improve transparency, and enhance legal accountability. This research addresses these challenges to support the safe, fair, and effective deployment of ML-based systems in clinical practice, guaranteeing that patients can trust, regulators can approve, and healthcare can use them.</div></div>","PeriodicalId":100155,"journal":{"name":"BenchCouncil Transactions on Benchmarks, Standards and Evaluations","volume":"5 1","pages":"Article 100215"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0