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Data-Driven Malware Detection: Exploring Supervised Machine Learning Approaches
Malicious software must be detected in order to protect sensitive data and systems in the digital era, as sophisticated malware is posing serious risks to cybersecurity. By examining supervised machine learning approaches with a particular focus on Random Forest, Logistic Regression, and Decision Trees, this research proposes a data-driven approach to malware detection. These algorithms are trained to recognize patterns indicating malware by using labeled datasets containing four types of malwares, Ransomware, Trojan, Virus, and Worm. The performance of these algorithms is comprehensively investigated in the paper, with comparisons made between their accuracy, precision, recall, and F1-score. Based on the experimental results, Random Forest (96% accuracy) performed better in terms of robustness and accuracy of detection than both Logistic Regression (91%) and Decision Trees (84%). Logistic Regression provided faster computation at the expense of less accurate detection. Decision trees, while relatively simple to comprehend, performed moderately and they overfit the data. The studys conclusion highlights the significance of choosing the appropriate model in accordance with particular cyber security requirements, outlining the advantages and disadvantages of every approach as well as their practical applicability in real-time malware detection systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Data-Driven Strategies for Twitter Engagement: Hashtag Recommendations and API Insights
Twitter is a great way to connect with people worldwide, and one of the best ways to do that is by using hashtags. A hashtag is a keyword or phrase attached to a particular topic, and users can use it to find related tweets. Using a hashtag relevant to the needs or for business can increase the tweets visibility and make it easier for people to see the content they want. It can hugely help content creators who want to increase engagement and influence their tweets. This research introduces TagMate, a hashtag recommender system for Twitter that offers significant benefits. By accessing the tweets using Twitter API and after analyzing and performing algorithms, recommendations for hashtags can be obtained. The Twitter API allows access to various information about the account, including followers, tweets, content, etc. This information can be used to generate recommendations for hashtags related to the business. The system will generate hashtags according to the tweet and recommend trending or popular hashtags to increase their reach or engagement on the Twitter platform. Using the API, a dashboard can be created showing which hashtags are being used most frequently and which are most popular. This information can help create more relevant and engaging tweets, attracting more followers and interest. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Data-Driven Sustainability: Revolutionizing Hospital Supply Chains through Big Data Analytics
Purpose: Despite the growing interest in Big Data Analytics Capabilities (BDAC), its significant impact on hospital operations and supply chains in shaping hospital performance remains elusive. The study investigates the pivotal role of BDAC within the framework of hospital supply chains across India. Drawing upon the Resource-Based View, Dynamic Capability View, and Organisation Information Processing Theory, this research explores the intricate relationships among the organization's capability factors, BDAC, and hospital performance indicators. Design/Methodology/Approach: A conceptual model was developed and empirically tested using survey data collected from 446 hospital managers. The analysis was carried out by using partial least square-structural equation modeling (PLS-SEM). Findings: The results of this study support the significant mediating impact of BDAC on Operational Flexibility, Supply Chain Sustainability, and Organisation Revenue leading to the enhancement of organizational performance. The findings highlight the strategic importance of cultivating BDAC to improve operational efficiency and overall effectiveness in the context of Indian multispeciality hospitals. Originality/Value: This research contributes to the existing knowledge by highlighting the relationship between organization capability factors, BDAC, and performance indicators in the different settings of Indian multispeciality hospitals. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Data-Driven Transformation of Hospitality Supply Chains Using AI-Powered Segmentation
The increasing complexity of supply chain operations in the hospitality sector demands data-driven strategies for efficient resource utilization and service delivery. This study proposes an artificial intelligence (AI)-driven framework leveraging unsupervised machine learning to uncover hidden patterns in patient-related operational data sourced from a publicly available dataset. The research applies clustering algorithmsK-Means, DBSCAN, and Agglomerative Hierarchical Clusteringto segment patient prof iles based on key variables such as length of stay, procedure type, room category, equipment usage, and staffing needs. Principal Component Analysis (PCA) was employed for dimensionality reduction and cluster visualization. The optimal number of clusters was identified using the Elbow Method, with K-Means yielding the highest silhouette score. Comparative analysis of all clustering models revealed varying strengths in noise detection, interpretability, and handling of sparse features. The results demonstrate how intelligent segmentation can support dynamic resource planning, targeted supply allocation, and improved operational responsiveness in hospital-based hospitality systems. This work contributes to the growing domain of AI-enabled supply chain analytics and of fers a practical pathway for enhancing decision-making in smart hospitality environments. 2025 IEEE. -
Data-driven triumph: CRM sales insights revolutionize customer retention
Context: The examination of the CRM data is anchored in a comprehensive analysis of sales performance metrics, with a significant role played. It was found a gap in the literature, considering the scarcity of pertinent case studies within the academic literature. Method: The geographical factor is paramount in this analysis, as it unveils divergent results across different regions. Moreover, the venture into predictive analytics for sales forecasting, capitalizing on CRM primary data spanning from 2018 to 2023, facilitating more informed decision-making. The sample comprises around 1500 Business to Business customer clusters for in-depth analysis is considered. Findings: From the Business Intelligence analysis, it was found the presence of long-standing customers with a lower purchase rate, favouring average industrial product models preferred by the customers. Conclusion:The study also explores the link between CRM can shape business strategies, enhance customer relationships, and boost organizational performance and customer retention. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Data: A Key to HR Analytics for Talent Management
The past few years have witnessed a significant rise in job openings across various industries worldwide. This trend has highlighted the need for companies to plan and recruit better talent to keep up with the demand for skilled employees. As a result, Human Resource (HR) professionals are now using workforce planning and HR analytics to address the challenges of finding and retaining the right employees. With the help of technological advancements in HR systems, businesses are leveraging data and insights to understand workplace dynamics better. Workforce planning has thus become crucial for organizations of all sizes to ensure they have the necessary talent to achieve their goals in the present and future. This chapter delves deeper and examines the importance of workforce planning and how HR analytics can help companies achieve their strategic objectives. Talent Management is about analyzing the workforce skill requirements of the organization. It needs a strategic plan to ensure the appropriate people are in the right roles at the right times. Talent Management is a crucial element of every businesss performance. In this process, data play a pivotal role in evaluating the existing workforce and planning for future workforce needs. Based on this, a strategy is developed to fill gaps or prospective shortages. Organizations can achieve their goals by using talent planning and collecting data about upcoming projects and skill requirements based on market needs. For example, talent planning is essential in the healthcare sector to guarantee that hospitals and clinics have enough doctors, nurses, and other healthcare workers to fulfill the rising demand for healthcare services. HR analytics is the key to talent management, enabling organizations to stay competitive, enhance productivity, and achieve long-term strategic objectives. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Data: An Anchor for Decision- Making to Build the Future Workforce Management System
In this digital era, the change in business environments and the nature of work lead to skill gaps. Training the workforce on desired skill sets must fill these skill gaps. Data play a crucial role in identifying the skills needed and helping organizations to plan the future workforce. Data is essential for any organizations growth and success in the dynamic market. Knowing the skill set in advance allows organizations and individuals to plan the business and skill requirements well. The way work is done may be impacted by these structural changes as the world is changing swiftly. Building the abilities necessary for the uncertain environments of the present and future environments is also crucial for training the employees. However, such skills must first be acknowledged and appreciated before being developed. Empirical data must support the methodology for valuing such abilities and skills. This chapter outlines the significance of data in skill identification for individuals to be future-ready. Finding the most relevant abilities in a given environment is the first step toward their formalization and acceptance at the systems level. It also presents the importance of creating skill matrices for students and organizations. The skill matrix objectively quantifies skill value for specific occupations and the possible trajectories to acquire those skill sets. This metric will allow policymakers to navigate this fast-changing workforce landscape and focus resources to ensure that skills are needed as students transition into the workforce and have skills that enable them to transition. 2024 selection and editorial matter, Alex Khang, Sita Rani, Rashmi Gujrati, Hayri Uygun, and Shashi Kant Gupta; individual chapters, the contributors. -
Database aware memory reconstruction for object oriented programming paradigm
Data storage is a big challenge in front of industries and researchers when its growing enormously. Traditional data storage strategy was fulfilling the business needs till the data was in structured format. But now due to Internet of Things (IoT) compatible devices unstructured data is more than structured one. In such cases traditional data storage strategy won't work efficiently. Initially data storage devices used to store the data irrespective of its logical storage. It means the record was stored either in array format or block format. Such type of storage was not matching physical and logical structure. Logically, structured data is generated as an attribute of particular entity, but physically it gets stored in a sequential memory storage either as file or as memory block. Object Based Storage pattern(OBS) stores the data in the way object gets generated by the programmer and accordingly memory is allocated for that object. Object contains all the data related to that particular entity which makes it easy to retrieve it back. Current study includes comparative advantages, operations and study of different service providers of object-based storage. We also focused on the current need of object-based storage structure for big data analysis and cloud computing. International Journal of Scientific and Technology Research. All rights reserved. -
Dataset exploring organizational culture of K-12 schools
Culture can be understood as an explicit social product arising from social interaction as an intentional or unintentional consequence of behavior. Educational Institutions culture differs from other organizational cultures as it impacts teachers' performance and students' learning. In this survey the definition of organizational culture used is given by Schein, The deeper level of basic assumptions and beliefs that are, learned responses to the group's problems of survival in its external environment and its problems of internal integration; are shared by members of an organization; that operate unconsciously; and that define in a basic taken -for-granted fashion in an organization's view of itself and its environment [1]. The data contains 1158 cases collected from K-12 School teachers on their perception of values and beliefs of their organizational culture using the OCTAPACE scale. Convenience sampling is used to obtain the data from teachers. The questionnaire was administered personally to teachers from sixty-five Private aided, Private unaided and Government schools. The eight dimensions measuring values and beliefs of Educational Institutions organizational culture are Pro-action, Authenticity, Openness, Collaboration, Experimenting, Trust, Confrontation and Autonomy. Descriptive statistics are computed from the dataset. The dataset can be used by researchers for meta analysis on organizational culture and school management can explore in depth the need for an organizational culture of autonomy, experimenting, collaboration and openness among teachers. 2022 The Authors -
Date Palm: Genomic Designing for Improved Nutritional Quality
Date palm (Phoenix dactylifera L.) is one of the oldest fruit trees known where a significant amount of breeding has been carried out to improve various agronomic and nutritional characteristics. Numerous studies have been done to improve the nutritional composition and quality of the fruit due to its significant biological properties. Various strategies have been formulated for improving the agronomic characters through biofortification as well as preserving through postharvesting techniques. Modern breeding practices using molecular markers have significantly helped to identify the phenotypic, as well as genotypic, diversity for the selection of superior date palm cultivars, advanced agronomic characters like nutritional quality, disease resistance, and yield. Availability of the whole genome sequence, organellar sequence, and genetic map of date palm has helped breeders in modification and improvement of the characteristics. With the availability of bioinformatic tools and gene editing knowledge, the nutritional composition of date palm can be effectively manipulated to develop better crops along with good agronomic characters and resistance to diseases. The authors have compiled the nutritional composition of date palm fruits and detail the strategies to edit the genome and improve nutritional quality. Springer Nature Singapore Pte Ltd. 2023. -
DATING APPS, FORMS OF ABUSE AND PERSONALITY TYPE
[No abstract available] -
DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre
The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches. 2021 M. S. Mekala et al. -
DC-DC and DC-AC Converters with Bi-Directional Capabilities for EV Applications
Abstract: This paper presents the design and implementation of a compact bidirectional DC-DC converter coupled with a DC-AC inverter for electric vehicle (EV) motor-drive applications. Both propulsion and regenerative braking modes are made possible by the suggested architecture, which facilitates smooth power transfer between a 48 V battery and a series- wound AC motor. Additionally, the inverter provides controlled AC stimulation for a dependable motor operation, while a high- efficiency bidirectional DC-DC converter controls battery power flow during acceleration and recovers energy during braking. In order to maintain dynamic stability under changing load circumstances, an Arduino Nano microcontroller uses a proportional-integral (PI) control method to regulate motor speed and current. Cross-conduction losses are decreased and MOSFET switching safety is improved by customized PWM pulse generation with dead-time insertion. 2026 IEEE. -
DDoS Intrusions Detection in Low Power SD-IoT Devices Leveraging Effective Machine Learning
Security and privacy are significant concerns in software-defined networking (SDN)-applied Internet of Things (IoT) environments, due to the proliferation of connected devices and the potential for cyberattacks. Hence, robust security mechanisms need to be developed, including authentication, encryption, and distributed denial of service (DDoS) attack detection, tailored to the constraints of low-power IoT devices. Selecting a suitable tiny machine learning (TinyML) algorithm for low-power IoT devices for DDoS attack detection involves considering various factors such as computational complexity, robustness in dealing with heterogeneous data, accuracy, and the specific constraints of the target IoT device. In this paper, we present a two-fold approach for the optimal TinyML algorithm selection leveraging the hybrid analytical network process (HANP). First, we make a comparative analysis (qualitative) of the machine learning algorithm in the context of suitability for TinyML in the domain of SD-IoT devices and generate the weights of suitability for TinyML applications in SD-IoT. Then we evaluate the performance of the machine learning algorithms and validate the results of the model to demonstrate the effectiveness of the proposed method. Finally, we see the effect of dimensionality reduction with respect to features and how it affects the precision, recall, accuracy, and F1 score. The results demonstrate the effectiveness of the scheme. 1975-2011 IEEE. -
DDoS Intrusions Detection in Low Power SD-IoT Devices Leveraging Effective Machine Learning
Security and privacy are significant concerns in software-defined networking (SDN)-applied Internet of Things (IoT) environments, due to the proliferation of connected devices and the potential for cyberattacks. Hence, robust security mechanisms need to be developed, including authentication, encryption, and distributed denial of service (DDoS) attack detection, tailored to the constraints of low-power IoT devices. Selecting a suitable tiny machine learning (TinyML) algorithm for low-power IoT devices for DDoS attack detection involves considering various factors such as computational complexity, robustness in dealing with heterogeneous data, accuracy, and the specific constraints of the target IoT device. In this paper, we present a two-fold approach for the optimal TinyML algorithm selection leveraging the hybrid analytical network process (HANP). First, we make a comparative analysis (qualitative) of the machine learning algorithm in the context of suitability for TinyML in the domain of SD-IoT devices and generate the weights of suitability for TinyML applications in SD-IoT. Then we evaluate the performance of the machine learning algorithms and validate the results of the model to demonstrate the effectiveness of the proposed method. Finally, we see the effect of dimensionality reduction with respect to features and how it affects the precision, recall, accuracy, and F1 score. The results demonstrate the effectiveness of the scheme. 1975-2011 IEEE. -
De novo synthesis of 2,2-bis(dimethylamino)-3-alkyl or benzyl 2,3-dihydroquinazolin-4(1H)-one compounds
A new versatile and efficient strategy for the synthesis of 2,2-bis(dimethylamino)-3-alkyl or benzyl 2,3-dihydroquinazoline-4(1H)-one compounds has been developed by one-pot multicomponent reaction with isatoic anhydride, amines followed by in situ-generated Vilsmeier reagent. The reaction has also been studied with different amines and solvents. 2017 Taylor & Francis. -
Dead Names, Living Futures Reflections from a Dera in Bhopal
Lance Larsens poem captures a particular kind of parental dissonance: the desire to honour ones child as they are now, while occasionally startled by a past that appears in mundane momentscontrasting the experiences of transpeople in a collective home in Bhopal. 2026, Economic and Political Weekly. All rights reserved. -
Dealing with missing values in a relation dataset using the DROPNA function in python
Python provides a rich data structure library called PANDAS, which provides fast and efficient data transformation and analysis. The word PANDAS is an abbreviation of Python Data Analysis Library. PANDAS facilitate optimized and dynamic data structure designs work with "relational" or "labeled" data. Python's approach is meant to provide a high-level, high-performance building block that can be used to do real-world analysis of data. PANDAS Library is allowing users to import data from different file formats, such as CSV, SQL, Microsoft Excel etc. [1]. It helps in data preparation, as well as in data modeling, for those projects, which aims data analysis for the extraction of information. Python's future will be built on this layer for statistical computing. In addition to discussing future areas of work and growth opportunities for statistics and data analytics applications built on Python, the study provides details about the language's design and features [2]. In this research paper, we intend to solve the problem of missing values in a dataset using the DROPNA function in Python using PANDAS library. 2023 Scrivener Publishing LLC. -
Death of Vernaculars and Language Hegemony: An ethnography of the higher education sector in 21st century India
The paper examines how new age pedagogies and neoliberal policies consciously work towards naturalizing English languages hegemony in institutions of Higher Education (IHE) in India. An ethnographic study the paper foregrounds the precarious positioning of non-English Indian languages vis-vis the pervading discourses of internationalization and education as job/skill oriented. Hegemony of English in the present is coupled with a restructuring of language departments as well as fleeting market demands for human capital. The paper also brings into question the role of the Internet and related technologies in reorganizing the linguistic dynamics of HE. Instead of democratizing, the Internet produces new monopolies in knowledge production, controls knowledge traffic from global North to South and further legitimizes the language hegemony. The paper argues that, in the last two decades, the neoliberal rupture has been leading HE institutions to a death of vernaculars within their physical, cultural and academic spaces. 2024, Hiroshima University,Research Institute for Higher Education,. All rights reserved. -
Death Rituals and Change Among Hindu Nadars in a South Indian Village
This article examines changes in the death rituals performed among Hindu Nadars in a South Indian village. It emphasises the importance of understanding ritual changes within their specific micro-level local contextual framework, including changing social structures at household and village level. This empirical evidence showcases how changing rituals connected to death reflect various adaptations through imitation, substitution and alteration of specific ritual elements and performants. It also identifies emerging class distinctions among Nadars and their connection with changes in rituals associated with death. This analysis of the changes depicts how Nadars use ritual actions in pragmatic ways, symbolically expressing and realising their aspirations for status enhancement through such ritual performances. 2021 SAGE Publications.