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Determining the most important indicators affecting the failure risk of conventional and sharia rural banks
This study identifies key variables influencing the risk of failure in Indonesia's Conventional and Sharia Rural Banks (BPR and BPRS) and proposes strategies to mitigate these risks. Using the Analytical Network Process (ANP) method, the study engaged 11 respondents, including banking practitioners from Conventional and Islamic Commercial Banks, Rural Banks, Islamic Rural Banks, and academics. Data were analyzed with Super Decision software and Excel. Results reveal four critical variables: 1) macroeconomic, 2) microprudential, 3) macroprudential, and 4) bank internal variables. The welfare aspect of macroeconomic variables emerged as the most significant, followed by the liquidity indicator in microprudential variables and the internal resilience indicator in macroprudential variables. These findings guide strategies to enhance banking performance and reduce failure risks. Regulators and the government should prioritize macroeconomic welfare indicators to strengthen the banking system and address factors contributing to BPR and BPRS failures. 2025, IGI Global Scientific Publishing. All rights reserved. -
A Systematic Approach for Predicting Cybersecurity Attacks in IoT using CNN-LSTM with HABCABO
IoT has transformed how devices work together. Now, billions of connected devices may share data across smart homes, energy systems, and environmental monitoring. In Internet of Things ecosystems, rapid IoT expansion has made them very vulnerable, which makes them easy targets for cyberattacks. Hackers can break into IoT devices that don't have enough protection to stop services, steal data, and invade privacy. This paper shows how to use deep learning using CNNs and LSTM networks and the HABCABO optimization algorithm to deal with these new dangers. After careful sequencing, scaling, and noise reduction, filter-based feature selection uses statistical methods to keep the most important information. To get the best detection, the CNN-LSTM model is trained with features that are carefully regulated. The suggested model is more accurate than CNN and LSTM approaches, with an accuracy rate of 98.04 %. These results show that the model can find and stop IoT cybersecurity threats. In conclusion, CNN-LSTM and HABCABO are strong and smart ways to make sure that IoT infrastructure is safe and reliable right now. 2025 IEEE. -
Enhancing Human Resource Management With Fuzzy Logic and Neural Networks for Personalized Performance Management
Human resource management (HRM) encounters that is making more challenging some exact, fair and person related performance appraisals on a large scale. Traditional methods often fail to capture the richness of human behaviour and tend to be opaque to interpretation. The proposed study contributes a new hybrid approach of Fuzzy Logic and Neural Network for enhanced personalized performance management. The facts are presented qualitatively by the fuzzy inference system in linguistic terms while for numerical features, the neural network analyses such that it can find complex relationship patterns. This methodology ensures the simplicity and high predictability. The model trained on Kaggle dataset achieved an accuracy of 94.7%, F1-score of 0.942, precision of 0.945, recall of 0.940 and AUC-ROC of. 976 which were higher compared to baseline approaches like Logistic Regression and Decision Trees respectively. The solution helps HR professionals make sense of relevant information into employee performance and developmental needs, which are highlighted in real time. The results suggest that combining rule-based reasoning and machine learning enhances personalisation and offers more transparent human resources practices. This study provides a foundation for the next generation of intelligent HRM systems enabling adaptive decision support in various organizational settings. 2026 IEEE. -
Digital entrepreneurship in modern techno world: mapping the literature and future research agenda
Digital entrepreneurship (DE) leverages internet services for business and financial gain. This study reviews past research, highlighting trends and gaps. Using the POWER framework and PRISMA techniques, 733 articles were analysed with VOSViewer and manual text analysis. Key findings include 2023 having the most publications, the UK leading in published articles, the journal Technological Forecasting for Social Change having the highest impact, and A. Ghezzi being the most influential author. Trending topics are entrepreneurs, digital entrepreneurship, and sustainability. Emerging themes include digital entrepreneurship ecosystems, opportunities for women and education, entrepreneurial funding, government adoption, and digital technologies. Future research should focus on entrepreneurial education, AI innovations, digital venture performance, and IoT adoption. Frameworks like lean start-up, business model innovation, and value creation can enhance DE performance, with further exploration encouraged by the identified future research agenda. Copyright 2026 Inderscience Enterprises Ltd. -
Journey to Transportation and Logistics Management Using Drone
The speedy adoption and amalgamation of drone technology in every sector of life have correlated environmental implications. It has come to reality due to revolutionization on the technology front and the movement for a digitized world by adopting digitization in the course of action. The drones have the capacity to decrease the rate of carbon emissions at significant levels in transportation and logistics management. So, this is becoming the need of an hour to apply the technology for the well-being of an individual. The comprehensive study and assessment in this chapter will address the implications of an environment connected to drone-based transportation and logistics operations. It will comprise the assessment based on academic studies, industry reports, and environmental assessments in terms of carbon emissions, energy consumption, and ecological footprint. The chapter will highlight the concerns and contributions in view of mitigating the environmental implications linked with the deployment of drones in transportation and logistics, enabling stakeholders to develop strategies that foster sustainability in the industry. 2026 Scrivener Publishing LLC. -
Cutting across the Durand: Water dispute between Pakistan and Afghanistan on river Kabul
All nations firmly believe in the absolute sovereignty over the waters flow in their areas and that only riparian states have any legal right, apart from an agreement, to use the water from the shared river. To address some of their water concerns, the co-riparian states compete to have more quantity of waters. Significantly, no water agreement exists between upper riparian Afghanistan and lower riparian Pakistan, despite sharing nine big and small rivers. The simmering water dispute between them on the River Kabul is rarely noted mainly because it is overshadowed by their political tensions, differences, and the dispute over the Durand Line. Using an analytical framework, this article examines three aspects of the River Kabul water dispute: its context, identifying the challenges that hinder a formalized bilateral agreement from being implemented, and its future. 2020 Policy Studies Organization -
A comparison of recommendation algorithms based on use of linked data and cloud
Recommendation generation is a critical need in today's time. With the advent of big data and the increasing number of users, generation of most suitable recommendation is essential. There are many issues already associated with recommendations such as data acquisition, scalability, etc.. Moreover, the users today look to get best recommendations at the minimum effort on their side. Thus it becomes difficult to manage such huge amount of information, extract the needed data and present it to the user with least user involvemen t. In this research, we surveyed some recommendation algorithms and analyze their applications on an open cloud server which uses linked data to generate automated recommendations. 2018 Authors. -
KESMR: A Knowledge Enrichment Semantic Model For Recommending Microblogs
In today's world, there's an enormous amount of information available on the Internet. Because of this, it's become really important to come up with better and smarter ways to search for things online. The old-fashioned methods, like just matching certain words or using statistics, don't work so well anymore. They often suggest web pages that are irrelevant. As the Semantic Web keeps getting bigger, it needs algorithms for the best fit. In this paper, a way to measure how different the words used for web search. This helps in suggesting the most relevant web pages. A special algorithm that can change its settings. Our proposed method demonstrates 94% accuracy. 2023 IEEE. -
The repercussions of teaching in the digital era: A boon or bane?
Recently, the world has experienced a big change due to the pandemic controlling our lives. The change is also experienced by the education sector. The pandemic has forced the whole system to go digital overnight. Although the learning system has been slowly moving towards digitalisation for a considerable period of time now, the social media platform is taking over the traditional method of learning. The study has 61 respondents and the data is collected through a questionnaire. The paper applies regression analysis with the help of SPSS. The development of digital learning platforms provides an alternative to offline learning. This recent spread of the e-learning environment was fast forwarded in the COVID period. The independent variables of the paper are professional skills and ethical proficiency in online learning. These challenges are evaluated, and their impact is assessed on the learning outcome. The chapter limits its approach to professional and ethical scales by ignoring the other important variables that decide the learning outcome of the students. However, the current body of literature has a very narrow approach to the learning system. So, the chapter inspires us to cover the gap through the proposed framework model for teaching practices in the digital era. The chapter intends to develop the teaching skills and moral conscience of the academicians and ease the learners into the new path of education. The future scope makes it definite for the growing learners to understand the advancement in technology and its repercussion on the skill development process. 2024 River Publishers. All rights reserved. -
Trademark Confusion in the Era of Big Data Algorithmic Branding: Consumer Decpetion and Conpetition Law Challenge
This chapter, per the authors, examines how Big Data and AI have transformed trademark deception from sign-based imitation to algorithmically driven perception distortion. It explains how digital platforms collect and analyze massive behavioral datasets to rank, recommend, and position brands in ways that influence consumer belief about origin without copying any mark. Algorithmic practices such as competitor keyword bidding, recommendation bias, and ranking manipulation generate large-scale confusion by shaping what consumers see first and trust most. While global jurisprudence, including the LOrl v. eBay decision, recognizes platform-facilitated deception, Indian law still interprets confusion through traditional frameworks distinguishing infringement of the mark from deception of consumer belief. This chapter, per the authors, argues that AI-mediated market architecture produces deception without infringement, creating evidentiary gaps and competitive distortions that require algorithmic transparency, marketplace accountability, and an updated trademarkcompetition interface Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
SUM SIGNED GRAPHS II
In this paper, the study of sum signed graphs is continued. The balancing and switching nature of the graphs are analyzed. The concept of rna number is revisited and an important relation between the number and its complement is established. 2023, Krasovskii Institute of Mathematics and Mechanics. All rights reserved. -
Sum Signed Graphs, Parity Signed Graphs and Cordial Graphs
Signed graphs are graphs with every edge is signed either positive or negative. Given an n vertex graph, the vertices are bijectively labelled from 1 to n. A signed graph is a sum signed graph if and only if every edge is signed negative whenever the sum of the vertex labels exceeds n and every edge is signed positive whenever the sum of the vertex labels does not exceed n. For a parity signed graph, an edge receives a negative sign, if the end vertices are of opposite parity and a positive sign otherwise. Cordial signed graphs are the ones with the difference between the total number of negative edges and the positive ones is at most 1. We discuss the connection between sum signed labeling with parity signed labeling and cordial labeling. The absolute cordial condition for graphs satisfying sum signed labeling will be analyzed 2023, IAENG International Journal of Applied Mathematics.All Rights Reserved. -
Negative Domination inNetworks
We introduce s-domination in signed graphs which is based on the number of negative edges between the dominating set and its complement. The s-domination in both the positive and negative homogeneous signed graph will be studied for each value of s. As a special case, the properties of s-domination in sum signed graphs will be analyzed. The maximum value of s for a graph for which the s-domination exists is identified. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Sum signed graphs - I
Let G=(V,E) be a simple graph, f: V(G) ? {1, 2, ..., |V(G)|} be a bijective function and ?: E(G) ? {+,-} be a mapping such that ? (uv)=+, whenever f(u)+f(v) ? n and ? (uv)=-, whenever f(u)+f(v)>n. Then, S=(G,f,?) is said to be a sum signed graph. In this paper, we initiate the study of sum signed graphs. Also, we find rna number for some classes of graphs and present some of the characteristics of sum signed graphs. 2020 Author(s). -
Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications
Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures. 2022 River Publishers. -
Predicting Stock Market Movements Through Multisource Data Fusion Graphs: An Approach Employing Graph Convolutional Neural Network
The stock market plays an important role in the capital market, and investigating price fluctuations in the stock market has consistently been a prominent subject for researchers. The application of soft computing techniques to predict and categorize stock market movements is a significant research challenge that has gathered considerable attention from researchers. Although several studies highlight the significance of incorporating information from two sources in stock movement prediction, the potential of advanced graphical techniques for modeling and analyzing multi-source data remains an unattended research area. This study aims to address this gap by introducing a novel model that utilizes multi-source data fusion graphs to predict future market movements. The primary challenge involves establishing a model that can effectively gather the relationships among various data sources and employ this understanding to improve prediction performance. Compared to several existing methods relying only on historical data or sentiment data, which show limited predictive power and lack generality, the proposed approach seeks to overcome these limitations. The proposed model integrates various information sources, including historical prices, news data, Twitter data, and technical indicators for predicting future stock market trends. This presented method involves constructing a subgraph map for each data type to capture events from both rising and falling markets. Then, a Gated Recurrent Unit (GRU) is employed to aggregate the subgraph nodes. These aggregated nodes are then integrated with a Graph Convolutional Neural Network (GCNN) to classify the multi-source graph, therefore achieving stock market trend prediction effectively. To further validate its effectiveness, the presented model is applied to Indian stock market data, demonstrating its feasibility in fusing multi-source stock data and establishing its suitability for effectively predicting stock market movements. 2024 Seventh Sense Research Group -
Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data
Determining the trend of the stock market is a complex task influenced by numerous factors like fundamental variables, company performance, investor behavior, sentiments expressed in social media, etc. Although machine learning models support predicting stock market trends using historical or social media data, reliance on a single data source poses a serious challenge. This study introduces a novel Explainable artificial intelligence (XAI) to address a binary classification problem wherein the objective is to predict the trend of the stock market, utilizing an integration of multiple data sources. The dataset includes trading data, news and Twitter sentiment, and technical indicators. Sentiment analysis and the Natural Language Toolkit are utilized to extract the qualitative information from social media data. Technical indicators, or quantitative characteristics, are therefore generated from trade data. The technical indicators are fused with the stock sentiment features to predict the future stock market trend. Finally, a machine learning model is employed for upward or downward stock trend predictions. The proposed model in this study incorporates XAI to interpret the results. The presented model is evaluated using five bank stocks, and the results are promising, outperforming other models by reporting a mean accuracy of 90.14%. Additionally, the proposed model is explainable, exposing the rationale behind the classifier and furnishing a complete set of interpretations for the attained outcomes. 2024, American Scientific Publishing Group (ASPG). All rights reserved. -
Harnessing Medical Databases and Data Mining in the Big Data Era: Advancements and Applications in Healthcare
In the contemporary period of Big Data, the healthcare industry is witnessing a transformative paradigm shift, propelled by the convergence of medical databases and data mining technology. This research paper delves into the multifaceted application of this synergy, offering a comprehensive overview of its implications and opportunities. With the exponential growth of healthcare data, the utilisation of medical databases serves as the bedrock for data mining techniques, fostering critical advancements in diagnosis, treatment, and patient care. Through this research, we explore the integration of electronic health records, genomic data, and clinical databases, unveiling new dimensions of predictive analytics, patient profiling, and disease monitoring. Moreover, we assess the ethical and privacy concerns entailed in this data-rich landscape, emphasising the need for robust governance and security measures. Our paper encapsulates the evolving landscape of health care, demonstrating the immense potential and the ethical responsibilities accompanying this groundbreaking merger of technology and medicine in the period of Big Data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Green marketing: Exploring concepts, strategies, and future trends
Green marketing research is becoming more and more well-liked in academia and business. Both companies and customers today recognize the value of eco-friendly products due to increased awareness of how companies respond to various factors contributing to environmental degradation. One should understand the meaning, opportunities, and threats associated with green marketing to harness the benefits of green marketing. This book chapter aims to explore various aspects of green marketing, including its evolution throughout the years, opportunities, threats, the future of green marketing, etc. To sum up, this chapter aims to gain an in-depth understanding of green marketing and how companies could use it to their advantage. Successful implementation will be possible only if associated threats are carefully analyzed and understood. Therefore, a part of this chapter will be dedicated to understanding the threats associated with green marketing strategies. 2023, IGI Global. All rights reserved.