INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES
ISSN 2322-0821(E), ISSN 2394-9910(P)
An ISO 9001:2015 Certified Publication
Volume 13 Issue 4 October-December 2025
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Preliminary CFD Evaluation of Selected Hypotheses for a Wind Energy Concentrator
Country- INDIA
Tarun Kumar Ahirwar, Dr. Paulami Sahu, Dr. Prashant V. Baredar
PAPER ID: IRJEAS04V13I4001
Published: Oct 2025
Journal: IRJEAS, Volume 13, Issue 4
Pages: 01-11
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Tarun Kumar Ahirwar, Paulami Sahu, Prashant V. Baredar. Preliminary CFD Anomaly Identification in Cyber Logs Through Machine Learning Techniques. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(4), pp. 01-11, 2025. https://doi.org/10.55083/irjeas.2025.v13i04001
Abstract:
This paper presents a preliminary computational fluid dynamics (CFD) assessment of four conical frustum windconcentrator configurations (Models A–D) intended for augmentation of small wind turbines in low-wind-speed regions. The designs share a fixed 3:1 inlet-to-outlet area ratio and differ only in frustum length and wall angle, providing a controlled comparison of geometric effects. Steady Reynolds-averaged Navier–Stokes (RANS) simulations with the Realizable k-ε model in ANSYS Fluent quantify outlet velocity amplification (V_Amp = V_out / V_in), radial uniformity and turbulence kinetic energy (TKE). Model B (short and steep taper) achieves the highest V_Amp, whereas longer designs (Models C and D) yield more uniform outflow at the expense of reduced gain. Model A strikes an intermediate balance. The results establish clear geometry–performance trade-offs that are practically relevant: overly high tapers risk non-uniform, turbulent outflow, while long ducts add material and drag but offer diminishing returns. This paper includes only the preliminary interpretation derived from test-run CFD observations. These CFD trends form the basis for turbine integrated modeling and controlled experimentation in subsequent work.
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Anomaly Identification in Cyber Logs Through Machine Learning Techniques
Country- INDIA
Ram Dwivedi, Nitesh Gupta, Anurag Shrivastava
PAPER ID: IRJEAS04V13I4002
Published: Oct 2025
Journal: IRJEAS, Volume 13, Issue 4
Pages: 12-20
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Ram Dwivedi, Nitesh Gupta, Anurag Shrivastava. Anomaly Identification in Cyber Logs Through Machine Learning Techniques. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(4), pp. 12-20, 2025. https://doi.org/10.55083/irjeas.2025.v13i04002
Abstract:
With the rising volume and sophistication of cyber attacks, traditional rule-based security systems have become insufficient to detect emerging and unknown threats. Machine Learning (ML) provides a dynamic and scalable solution for anomaly detection in cyber security logs, enabling systems to identify unusual patterns with minimal human intervention. This study offers a thorough framework that combines supervised, unsupervised, and semi-supervised models for anomaly identification using machine learning.
Using the HDFS log dataset, we implement and evaluate Isolation Forest, Auto encoder, and Random Forest algorithms. The results demonstrate that ML techniques significantly enhance detection accuracy and reduce false positives, with Random Forest achieving the highest performance across all evaluation metrics.
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Overcoming Operational Bottlenecks: A Quantitative Analysis of the Benefits of Digital Weighbridge Integration with PCS 1X and Sagar Setu
Country- INDIA
Venkata Ramana Akkaraju
PAPER ID: IRJEAS04V13I4003
Published: Oct 2025
Journal: IRJEAS, Volume 13, Issue 4
Pages: 21-37
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Venkata Ramana Akkaraju. Overcoming Operational Bottlenecks: A Quantitative Analysis of the Benefits of Digital Weighbridge Integration with PCS 1X and Sagar Setu. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(4), pp. 21-37, 2025. https://doi.org/10.55083/irjeas.2025.v13i04003
Abstract:
Indian ports process more than 90% of the country’s trade volume, which makes efficient operations a key to economic competitiveness. Conventional manual weighbridge procedures have been recognized as a cause of bottlenecks for years, leading to long truck turnaround times, inaccurate data, and decreased throughput. In an effort to solve these issues, this research assesses the effect of implementing digital weighbridge systems in combination with the Port Community System (PCS 1x) and the SAGAR SETU mobile application on total operating performance. A six-month observation study was carried out in two of India’s largest seaports, comparing pre- and post-integration performance measures. The most important indicators were average truck turnaround time, weighment error rates, and levels of throughput per day. Statistical methods like paired t-tests were used to compare differences in performance, whereas regression analysis established integration-throughput improvement relationship. The findings show a 28% decrease in mean truck turnaround time, a 62% decrease in weight-related mistakes, and a 15% improvement in daily truck processing rate, even during peak cargo load periods. The results emphasize the capability of digital synchronization and real-time data sharing in making logistics processes more efficient, minimizing manual interventions, and enhancing decision-making effectiveness. This study offers policy formulation insights under the Sagarmala program with a focus on the urgency for accelerated digital integration in Indian ports. This research not only establishes the operational advantages of automation but also provides a model for scalability, furthering India’s vision of becoming a world-class port infrastructure and globally competitive supply chain nation. These results—showing a 28% reduction in truck turnaround time, a 62% decline in weighbridge errors, and a 15% rise in daily throughput—demonstrate that digital weighbridge integration not only enhances operational efficiency but also yields measurable financial returns, making it a scalable solution aligned with the Sagarmala program and India’s Maritime Vision 2030.
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The Evolution of Phishing Detection: From Static Signatures to Autonomous Generative Agents
Country- INDIA
Ashwarya Singh, Kalpana Mishra
PAPER ID: IRJEAS04V13I4004
Published: Oct 2025
Journal: IRJEAS, Volume 13, Issue 4
Pages: 38-47
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Ashwarya Singh, Kalpana Mishra. The Evolution of Phishing Detection: From Static Signatures to Autonomous Generative Agents. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(4), pp. 38-47, 2025. https://doi.org/10.55083/irjeas.2025.v13i04004.
Abstract:
Phishing is still one of the most common and harmful threats in cybersecurity. It is the main way that most data breaches happen. As a result, it is very important to create strong and quick detection methods. This paper offers an extensive literature analysis on the progression of phishing detection systems, outlining their development from initial static methods to the contemporary state-of-the-art. We start by looking at classic solutions, such blacklist-based and heuristic-based systems, and pointing out how they don’t work well against new, zero-day threats. Next, we look at the big change that machine learning brought about, which made it possible to create more flexible solutions by using feature engineering from URLs and visual similarity analysis of webpages. A lot of attention is being paid to the rise of reference-based detection systems, which check the validity of web pages by comparing them to a database of real brands. We critically examine advanced dynamic systems such as DynaPhish, which try to automate knowledge base expansion, revealing their intrinsic fragility and reliance on inflexible logic. Lastly, we look at the cutting edge of phishing detection, which is defined by the use of generative AI and autonomous agents. We contend that Large Language Model (LLM)-powered agents, endowed with human-like reasoning, multi-modal analysis, and dynamic tool utilisation, constitute a possible remedy to the shortcomings of previous approaches. This study brings together the most important progress, points out ongoing problems, and suggests that the future of phishing defence rests in making smart, self-driving systems that can think and change in real time to deal with the changing nature of modern phishing threats.
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The Evolution of SMS Phishing (Smishing) Detection: A Comprehensive Review of Heuristic, Machine Learning and Natural Language Processing Techniques
Country- INDIA
Yogita Rajput, Kalpana Mishra
PAPER ID: IRJEAS04V13I4005
Published: Nov 2025
Journal: IRJEAS, Volume 13, Issue 4
Pages: 48-59
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Yogita Rajput, Kalpana Mishra. The Evolution of SMS Phishing (Smishing) Detection: A Comprehensive Review of Heuristic, Machine Learning and Natural Language Processing Techniques. International Research Journal of Engineering & Applied Sciences (IRJEAS). 13(4), pp. 48-59, 2025. https://doi.org/10.55083/irjeas.2025.v13i04005
Abstract:
Smartphones are a primary target for cybercriminals, with “smishing” (SMS phishing) being one of the most pervasive threats. The immediacy and implicit trust of SMS messaging make these attacks highly effective, leading to significant financial and personal damage. This necessitates the development of robust, adaptive defenses. This paper provides a critical review of the evolution of smishing detection techniques. We begin by examining foundational static methods, such as signature-based and rule-based filters, and detail their inherent limitations against dynamic threats. The core of the review then analyzes the machine learning (ML) paradigm, breaking down the complete pipeline from Natural Language Processing (NLP) for text normalization to feature engineering and model classification.
However, this review argues that the field has entered a new, more challenging era. The foundational ML models that defined the 2010s are now being outpaced by a 2023-2025 surge in sophisticated, AI-generated campaigns and large-scale Phishing-as-a-Service (PhaaS) operations. We analyze this modern threat landscape, including the defenses deployed by commercial leaders like Google and Truecaller, which leverage on-device large language models (LLMs). Critically, we introduce an analysis of non-technical constraints, detailing how data privacy and legal mandates, such as the GDPR and CCPA, are a central design driver pushing the industry toward privacy-preserving, on-device architectures. This analysis indicates that the future of smishing detection lies not in a single “best” classifier, but in a multi-layered, on-device framework that can counter AI-generated content while remaining compliant with global privacy laws.
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ADEPT: An Autonomous, LLM-Powered Agent for Dynamic, Reference-Based Phishing Detection
Country- INDIA
Ashwarya Singh, Kalpana Mishra
PAPER ID: IRJEAS04V13I4009
Published:
Journal: IRJEAS, Volume 13, Issue 4
Pages: 81-96
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Ashwarya Singh, Kalpana Mishra. ADEPT: An Autonomous, LLM-Powered Agent for Dynamic, Reference-Based Phishing Detection. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(4), pp. 81-96,
2025. https://doi.org/10.55083/irjeas.2025.v13i04009.
Abstract:
Conventional phishing detection systems, which predominantly rely on static blacklists and rigid, signature-based heuristics, are increasingly ineffective against the dynamic and polymorphic nature of modern phishing attacks. Reference-based detection, which verifies a webpage’s authenticity against a known brand’s identity, offers a more robust approach but is critically hampered by the need to maintain a comprehensive and constantly updated knowledge base. This paper addresses this fundamental challenge by introducing ADEPT (Autonomous Dynamic Agent for Phishing Threat), a novel framework architected around a sophisticated Large Language Model (LLM) acting as an autonomous agent. We first present a granular error analysis of a state-of-the-art dynamic reference-based system, DynaPhish, revealing its brittleness and critical failures stemming from rigid logic and dependency on external APIs. To overcome these deficiencies, we designed and implemented the ADEPT framework, which equips an LLM agent with a multi-modal perception pipeline and a toolkit for real-time information retrieval from the web. The agent mimics human cognitive processes to dynamically investigate suspicious webpages, analyze visual and textual content, and reason about a brand’s identity without relying on a pre-existing static knowledge base. Through a series of rigorous experiments on a balanced dataset of 400 phishing and benign samples, the ADEPT framework, utilizing the GPT-4 model, achieved a phishing detection accuracy of 0.945. This represents a significant improvement over both the DynaPhish baseline (0.499 accuracy) and simpler LLM-based methods, empirically validating that an autonomous, agent-based approach provides a more resilient and effective solution to the pervasive threat of phishing.
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Recalibrating Employee Relations in the Post-Liberalisation Era: An Analytical Study of Strategic Human Resource Interventions and Policy Transformations
Country- INDIA
Shresta Stephen Vadlana, Dr. Geeta Tomar
PAPER ID: IRJEAS04V13I4010
Published: Nov 2025
Journal: IRJEAS, Volume 13, Issue 4
Pages: 97-111
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Shresta Stephen Vadlana, Dr. Geeta Tomar. Recalibrating Employee Relations in the Post-Liberalisation Era: An Analytical Study of Strategic Human Resource Interventions and Policy Transformations. International Research Journal of Engineering & Applied Sciences (IRJEAS). 13(4), pp. 97-111, 2025. https://doi.org/10.55083/irjeas.2025.v13i04010
Abstract:
Background / Context
Post-liberalisation India has experienced principal changes in economic policy, organisational management, and workforces. The accelerated globalisation and tech-led growth have pushed companies towards new human resource (HR) strategies for competitiveness and adaptability. The changes have also reshaped employee relations, impacting job security, social security, and labour market participation patterns across industries.
Problem / Gap
Even after many HR policy reforms, there is little empirical evidence regarding their impact on employment patterns, training take-up, and wage increases at the national level. While macroeconomic outcomes dominate most existing research, micro-level effects of liberalisation on workforce equality and staff well-being are rarely addressed.
This research seeks to critically explore the influence of post-liberalisation HR interventions and policy changes on employee relations, workforce structure, and work quality in India.
Methodology / Approach
Based on secondary data from the Periodic Labour Force Survey (PLFS) Annual Reports (2017–18 to 2022–23) released by the Ministry of Statistics and Programme Implementation (MOSPI), the research uses descriptive, trend, and comparative analysis. Important indicators like employment type ratios, training participation, wage increase, and labour force participation are calculated. The analytical approach combines HR policy assessment with national labour statistics to determine emerging trends in employment flexibility, workforce skill building, and income patterns.
Results / Findings
There is a persistent rise in self-employment and casual work, moderate advances in training participation, and persistent but unbalanced wage growth across industries. Regular wage employment is mostly stable but increasingly marked by fixed-term contracts. These tendencies suggest that as much as HR interventions have promoted organisational efficiency, issues of informality, gender inequality, and job insecurity continue.
Implications / Significance
The research highlights the imperatives of re-calibrated HR policies that offset efficiency with equity in the post-liberalisation era. Enhancing social security, enhancing inclusive skill development, and correcting gender-related imbalances can lead to sustainable worker relations. The findings inform policymaking for labour reforms, workforce planning, and inclusive human resource management in emerging economies.
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A New Machine Learning Framework for Smishing Detection Using Advanced Text Normalization
Country- INDIA
Yogita Rajput, Kalpana Mishra
PAPER ID: IRJEAS04V13I4011
Published: UR
Journal: IRJEAS, Volume 13, Issue 4
Pages: 112-124
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Yogita Rajput, Kalpana Mishra. A New Machine Learning Framework for Smishing Detection Using Advanced Text Normalization. International Research Journal of Engineering & Applied Sciences (IRJEAS). 13(4), pp. 112-124, 2025. https://doi.org/10.55083/irjeas.2025.v13i04011
Abstract:
The proliferation of smartphones has made SMS-based phishing, or “smishing,” a significant and pervasive cybersecurity threat. Traditional detection methods often fail to keep pace with the evolving and informal nature of SMS language, which is characterized by slang, abbreviations, and intentional misspellings. This linguistic noise fragments the feature space for machine learning classifiers, leading to reduced accuracy and higher false positive rates. This paper introduces a novel, two-stage machine learning framework designed specifically to address this challenge. The core innovation is the integration of an advanced text normalization engine as a critical preprocessing step before classification with a Naive Bayes model. The normalization module systematically converts non-standard SMS language into standardized English using a curated Lingo Dictionary, thereby consolidating the feature space and enhancing the semantic clarity of the message content. We conduct a rigorous empirical evaluation on a curated dataset derived from the public SMS Spam Collection. The results demonstrate that our framework achieves a detection accuracy of 96.2%, a True Positive Rate of 97.14%, and a False Positive Rate of just 3.87%. This represents a substantial improvement over a baseline model without normalization and outperforms several existing state-of-the-art approaches. The findings confirm that explicitly addressing linguistic variability is essential for building robust and effective smishing detection systems.
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Integration of Chaotic Map with Metaheuristic Walrus Algorithm for Enhanced Security of Image Encryption Method
Country- INDIA
Monika Gupta, Shashi Bala, Tarandeep kaur
PAPER ID: IRJEAS04V13I4012
Published: UR
Journal: IRJEAS, Volume 13, Issue 4
Pages: 125-133
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Monika Gupta, Shashi Bala, Tarandeep kaur. Integration of Chaotic Map with Metaheuristic Walrus Algorithm for Enhanced Security of Image Encryption Method. International Research Journal of Engineering & Applied Sciences (IRJEAS). 13(4), pp. 125-133, 2025. https://doi.org/10.55083/irjeas.2025.v13i04012
Abstract:
Image encryption (IE) methods have been used to secure the sensitive secret images and overcome the data breach attacks. In these methods, the chaotic map algorithms gained popularity over other security algorithms. However, these algorithms are highly sensitive to input parameters that impact the security of the encryption method. Therefore, in this paper, we have strengthened the security of the IE method by integrating the chaotic map with the metaheuristic walrus algorithm to hyper-tune the input parameters according to the chi-square objective function. In the presented methodology, the open-source database images were collected for evaluation purposes. Thereafter, it was passed through three stages of encryption to produce the final encrypted image. The first and last stages utilize the shuffling method to rearrange the secret image, while the second stage employs an exclusive-OR operation with a random key. The shuffling method index value and random key were generated using the 3-D chaotic map. According to the results, the proposed strategy achieves a high entropy value and reduced CC, SSIM, and PSNR values while passing the chi-square test.