Papers by Iqtiar M Siddique
Requirements Engineering (RE) is experiencing a significant transformation that requires urgent a... more Requirements Engineering (RE) is experiencing a significant transformation that requires urgent attention and creativity from industry experts to address the challenges of future systems and software development. At the core of this evolution is Requirements Engineering (RE), an essential process that defines, records, and maintains the fundamental aspects of software and systems design. With the rapid increase in complexity and interconnectedness across different industries, the issues linked to requirements engineering are becoming more pressing, significantly affecting costs, timelines, and overall performance. This article outlines current research trends in requirements engineering, focusing on emerging challenges and future pathways. We investigate advancements in RE techniques, methodologies, and tools, assessing their effects on building complex systems. Furthermore, we emphasize the growing significance of stakeholder engagement and agile practices in shaping requirements, highlighting their contributions to improving collaboration and adaptability. By evaluating the present state of practice and pinpointing critical areas of research concern, this article offers a guide for future requirements engineering studies, enabling researchers and practitioners to address the changing needs of software and systems engineering.
Social Science Research Network, 2024
Social Science Research Network, 2024
World Journal Of Advanced Research and Reviews, Apr 30, 2024
In this era of technological innovation, the integration of machine learning techniques with Geog... more In this era of technological innovation, the integration of machine learning techniques with Geographic Information Systems (GIS) has emerged as a transformative approach to spatial analysis and decision-making. This abstract explores the synergy between machine learning and GIS, highlighting their combined potential to unlock new insights from spatial data, automate analytical processes, and enhance predictive modeling capabilities. By leveraging machine learning algorithms such as neural networks, random forests, and support vector machines, GIS practitioners can address complex spatial challenges more effectively, ranging from land cover classification and urban growth modeling to environmental monitoring and disaster response. Through case studies and examples, this abstract demonstrates the practical applications of machine learning in GIS, illustrating its role in advancing our understanding of spatial phenomena and informing evidence-based decision-making across diverse domains. As the field continues to evolve, embracing the fusion of machine learning and GIS holds immense promise for unlocking the full potential of spatial analysis and shaping a more sustainable and resilient future. Illustrative case studies and examples showcase the practical applications of machine learning in GIS across diverse domains. From land cover classification and urban growth modeling to environmental monitoring and disaster response, machine learning algorithms offer versatile solutions to address a wide spectrum of spatial challenges. Through the fusion of machine learning and GIS, researchers and practitioners gain unprecedented insights into complex spatial phenomena, enabling them to make data-driven decisions that are both informed and actionable. Looking ahead, the fusion of machine learning and GIS holds immense promise for advancing our understanding of spatial dynamics and shaping a more sustainable and resilient future. As the field continues to evolve, embracing this interdisciplinary approach is essential for unlocking the full potential of spatial analysis, fostering innovation, and addressing pressing societal challenges at local, regional, and global scales. By leveraging the synergies between machine learning and GIS, we can chart a path towards a more data-driven, informed, and equitable world.
World Journal Of Advanced Research and Reviews, Mar 30, 2024
This review explores the synergistic relationship between advanced microscopy techniques and mech... more This review explores the synergistic relationship between advanced microscopy techniques and mechanical engineering, outlining their profound impact on materials science and mechanical system design. We delve into the multifaceted applications of electron microscopy, X-ray diffraction, and spectroscopic methods in understanding microstructural dynamics, mechanical properties, and failure mechanisms of materials integral to mechanical engineering. Through a comprehensive synthesis of recent research, we emphasize the pivotal role these techniques play in optimizing material performance, bolstering structural integrity, and driving innovation in mechanical design. By elucidating the intricate details of material behavior at the microscale, advanced microscopy contributes to informed decision-making in material selection and design processes. Furthermore, we address emerging trends and prospects, underscoring the continued synergy between advanced microscopy and mechanical engineering. This collaboration remains at the forefront of materials science and technology, promising ongoing advancements that will shape the future landscape of mechanical design and materials innovation.
World Journal of Advanced Engineering Technology and Sciences, Jan 30, 2024
This paper focuses on the application of Value Stream Mapping (VSM) within the context of the ele... more This paper focuses on the application of Value Stream Mapping (VSM) within the context of the electronics manufacturing industry, aiming to improve its operational efficiency and financial performance. The study thoroughly analyzes costs, integrating VSM to justify economic benefits. Data was collected directly from the manufacturing floor to create a current state map, enabling the identification of non-value-added activities and sources of waste. Areas for potential improvement were pinpointed to reduce or eliminate these inefficiencies. By implementing these proposed enhancements, the paper outlines a future state map for the electronics manufacturing process and presents the results of applying Value Stream Mapping. Comparing the current and future state maps, the study reveals that embracing Lean principles, in conjunction with Value Stream Mapping, can significantly benefit the electronics manufacturing industry. Specifically, it can reduce production lead times by 67.84% and decrease costs by 8.69%. This research underscores the financial implications of adopting Value Stream Mapping, illustrating that by combining Lean principles with VSM, industries can offer rapid customer responses at lower costs, a crucial factor for improving competitive performance in the existing market landscape.
World Journal Of Advanced Research and Reviews, Feb 28, 2024
Accurate prediction of accident risks plays a crucial role in proactively implementing safety mea... more Accurate prediction of accident risks plays a crucial role in proactively implementing safety measures and allocating resources effectively. This paper introduces an innovative approach aimed at improving accident risk prediction by harnessing unique data sources and extracting insights from diverse yet sparse datasets. Traditional models often face limitations due to a lack of diversity and scope in the available data, which hinders their predictive capabilities. In response to this challenge, our study integrates a broad spectrum of heterogeneous data, encompassing traffic flow, weather conditions, road infrastructure details, and historical accident records. To overcome the difficulties associated with sparse data, we employ advanced data science techniques such as feature engineering, imputation, and machine learning. The paper introduces a novel dataset that amalgamates various data types, establishing a robust foundation for our predictive model. Through meticulous analysis, we derive valuable insights from these diverse sources, significantly enhancing our ability to assess accident risks. The proposed approach offers numerous advantages, including the capacity to predict accidents in areas that were previously underrepresented and under varying conditions. We rigorously evaluate the model's performance through extensive experimentation and validate its accuracy using real-world accident data. Our results indicate substantial improvements in prediction accuracy compared to conventional models. This research contributes significantly to the field of accident risk prediction by highlighting the potential benefits of integrating heterogeneous sparse data and leveraging advanced data science techniques. The study underscores the importance of tapping into novel data sources and extracting concealed patterns and insights to promote safety and optimize resource allocation in accident-prone regions, fostering more secure environments.
Journal of computer science engineering and software testing, 2024
This study focuses on the application of data analytics algorithms for real-time monitoring in ad... more This study focuses on the application of data analytics algorithms for real-time monitoring in additive manufacturing processes. The utilization of advanced analytics plays a pivotal role in enhancing the quality control and efficiency of these manufacturing techniques. The research explores how data-driven insights can be harnessed to identify, analyze, and rectify deviations in the manufacturing process, ensuring optimal performance and product quality. By integrating sophisticated monitoring algorithms, the study aims to create a robust framework that continuously analyzes various parameters during additive manufacturing. This includes monitoring factors such as temperature, pressure, and material properties in real-time. The collected data is processed through advanced analytics tools to detect anomalies or deviations from the expected standards. The implementation of machine learning algorithms further facilitates predictive maintenance and proactive adjustments, contributing to the overall reliability and effectiveness of additive manufacturing processes. The outcomes of this research hold significant implications for industries relying on additive manufacturing technologies, providing a foundation for improved process control and product quality. The study contributes to the growing field of Industry 4.0 by showcasing the integration of data analytics as a key enabler for efficient and reliable additive manufacturing.
Journals of Mechatronics Machine Design and Manufacturing, 2024
The pursuit of operational excellence and continuous improvement in the manufacturing sector has ... more The pursuit of operational excellence and continuous improvement in the manufacturing sector has led to the convergence of two prominent methodologies, Lean and Six Sigma. This abstract provides an overview of the comprehensive framework discussed in this paper, which blends the principles of Lean and Six Sigma to drive transformative enhancements in manufacturing processes. The proposed framework extends beyond surface-level improvements, delving deep into the intricacies of production systems. It encompasses a holistic approach that addresses process efficiency, waste reduction, quality enhancement, and cost optimization. By synergizing Lean's emphasis on eliminating waste and Six Sigma's focus on process variation reduction, this framework approaches a comprehensive strategy for manufacturers to succeed in operational excellence. Through real-world case studies and practical insights, this paper highlights the tangible benefits of adopting the Lean and Six Sigma deep framework and its potential to revolutionize the manufacturing landscape.
This study focuses on the implementation of modern and intelligent logistics vehicles equipped wi... more This study focuses on the implementation of modern and intelligent logistics vehicles equipped with advanced tracking and security features. In response to the evolving landscape of logistics management, the proposed system integrates cuttingedge technologies to enhance efficiency and ensure the security of the entire logistics process. The core component of this implementation is the incorporation of state-of-theart tracking mechanisms, enabling real-time monitoring of vehicle locations and movements. Furthermore, the system addresses the paramount concern of security by introducing advanced security measures. Through the utilization of sophisticated tracking technologies and security protocols, the proposed logistics vehicles aim to safeguard both customer and provider data. The implementation includes the integration of QR code concepts, creating a binary image system that conceals sensitive information and ensures access only to authorized users. In addition to tracking and security, the study delves into the realm of information mining, employing techniques such as classification, clustering, and recommendation to extract meaningful patterns from vast datasets. Collaborative filtering techniques are incorporated to enhance customer experience by recommending services based on user preferences and historical data. This abstract encapsulates the comprehensive approach of deploying modern logistics vehicles, emphasizing their intelligence through advanced tracking, robust security measures, and data-driven insights. The proposed system aims to revolutionize logistics management, providing a seamless and secure experience for both customers and service providers in the dynamic logistics landscape.
Journal of data mining and management, 2024
Accurate accident risk prediction is paramount for proactive safety measures and resource allocat... more Accurate accident risk prediction is paramount for proactive safety measures and resource allocation. This paper introduces an innovative approach to enhance accident risk prediction by leveraging novel data sources and uncovering insights from heterogeneous sparse data. Traditional models often suffer from limitations in data diversity and scope, hindering their predictive capabilities. In response, our study integrates a wide range of heterogeneous data, including traffic flow data, weather conditions, road infrastructure, and historical accident records. To address the challenges of working with sparse data, we employ advanced data science techniques, including feature engineering, imputation, and machine learning. The paper presents a new dataset that combines diverse data types, providing a comprehensive foundation for our predictive model. Through rigorous analysis, we extract valuable insights from these heterogeneous sources to improve accident risk assessment. The proposed approach offers several advantages, including the ability to predict accidents in previously underrepresented areas and under varying conditions. We evaluate the model's performance through extensive experimentation and validate its accuracy against real-world accident data. Our findings demonstrate significant enhancements in prediction accuracy compared to conventional models. This research contributes to the field of accident risk prediction by showcasing the potential of heterogeneous sparse data integration and advanced data science techniques. It highlights the importance of utilizing novel data sources and the value of uncovering hidden patterns and insights to foster safer environments and more efficient resource allocation in accident-prone areas.
In this paper, we delve into the public discourse surrounding COVID-19 on Twitter to unearth the ... more In this paper, we delve into the public discourse surrounding COVID-19 on Twitter to unearth the collective sentiments, concerns, and spread of information during the pandemic. By leveraging a dataset of relevant tweets and corresponding ISO country codes, our analysis will map out the geographical and digital landscape of these conversations. The significance of this work lies in its potential to inform public health strategies, shape policymaking, and contribute to social research on crisis communication. Stakeholders ranging from health officials to the public have a vested interest in understanding the contours of this dialogue. Our objective is to craft a data-driven narrative through visualizations that reveal how the world engages with the pandemic on the digital front, providing actionable insights into global and local responses to COVID-19 using Machine Learning techniques.
This study conducts a comprehensive assessment of Bitcoin mining's sustainability, concen... more This study conducts a comprehensive assessment of Bitcoin mining's sustainability, concentrating on the incorporation of renewable energy sources such as hydroelectric, wind, solar, and geothermal power. It investigates the adaptability and potential constructive collaboration of these energy systems in the context of the Bitcoin mining scenario. This research presents a detailed understanding of the possibilities and challenges involved with integrating hydro, wind, solar, and geothermal resources into sustainable Bitcoin mining processes by methodically examining energy efficiency, environmental effect, and economic viability. The study contributes to the discussion on establishing a sustainable future for digital currencies by exposing significant connections between renewable energy technology and the cryptocurrency industry. To achieve the stated goals, the study carefully examines the energy usage strongly associated with Bitcoin mining and transaction processing. It investigates the elements that contribute to Bitcoin's energy consumption, addressing the environmental consequences of its energy-intensive activities. The study aims to find feasible techniques for reducing Bitcoin's carbon footprint, to cultivate a thorough understanding of the complex relationship between the digital currency revolution and its environmental consequences. It emphasizes the importance of balancing technical developments with environmental responsibilities. The concept contributes to establishing a conscious and sustainable future for the digital currency ecosystem by addressing the numerous components of Bitcoin's energy usage and advocating a collective commitment to protecting the environment in the face of advancing technology paradigms.
Journal of Industrial Mechanics, Dec 31, 2022
The global pandemic, particularly the COVID 19 crisis, has significantly impacted various manufac... more The global pandemic, particularly the COVID 19 crisis, has significantly impacted various manufacturing sectors, notably the electronics industry in Bangladesh. In the wake of the COVID 19 pandemic, industries faced unprecedented operational challenges, necessitating agile and innovative management strategies. This case study examines the role of Total Productive Maintenance (TPM) in bolstering the resilience of the domestic electronics industry during this global health crisis. As supply chains buckled and consumer demand fluctuated wildly, TPM principles provided a framework for maintaining equipment reliability and production efficiency when it was most needed. The study dissects how TPM's focus on proactive maintenance and employee empowerment helped organizations navigate the pandemic's complexities, ensuring minimal disruption in production despite stringent health regulations and workforce limitations. By analyzing real-world applications of TPM during this period, the research reveals the methodology's direct impact on mitigating downtime, preserving quality, and enhancing operational adaptability. The insights presented underscore the significance of TPM in fortifying the electronics sector against such systemic shocks, ultimately contributing to sustained industrial performance and economic stability in tumultuous times.
Contemporary technologies afford us the opportunity to augment and substitute traditional in-pers... more Contemporary technologies afford us the opportunity to augment and substitute traditional in-person classes with computer-based resources commonly known as virtual labs. Before the global pandemic, physical classrooms offered a hands-on learning environment for students. However, the pandemic has rendered in-person labs impractical, making it challenging for students to engage with faculty members directly. In the present scenario, the preference for remote education has surged due to safety concerns. With proper internet access, students now have the flexibility to conduct experiments from the comfort of their homes. Despite the inherent challenges associated with online learning, virtual labs serve as a valuable solution, addressing some of these issues. The utilization of simulation-based learning, especially in subjects like physics, chemistry, and biology, contributes significantly to students' comprehension of key concepts. Virtual labs play a pivotal role in fostering problem-solving skills, critical thinking, creativity, imagination, understanding of scientific processes, laboratory skills, as well as heightened interest and perception. Consequently, it becomes imperative for educators to integrate virtual labs into their teaching methodologies to enhance the quality of instruction and improve students' learning outcomes.
JOURNAL OF Firewall Software and Networking (e-ISSN: 2584-1750), 2024
The increasing deployment of satellites for essential applications necessitates robust anomaly de... more The increasing deployment of satellites for essential applications necessitates robust anomaly detection to maintain their operational integrity. Traditional methods, which depend on manual monitoring and predefined thresholds, often prove inadequate in the complex space environment. This paper investigates the application of Artificial Intelligence (AI) algorithms to improve the detection and analysis of anomalous behavior in on-orbit satellites. AI, especially through machine learning (ML) and deep learning (DL), provides advanced capabilities for processing extensive telemetry data and identifying intricate patterns. By leveraging historical data, AI systems can establish normal operational parameters and detect deviations indicating potential anomalies. Techniques such as supervised and unsupervised learning are employed to develop models with high predictive accuracy. Furthermore, AI facilitates root cause analysis by correlating anomalies with operational conditions or external factors, enabling effective corrective measures. The integration of AI also promotes autonomous satellite operations, which are crucial for deep-space missions. This advancement enhances satellite reliability and safety, supporting sustainable and progressive space exploration. In this research, machine learning algorithms were employed to develop the proposed anomaly detection system. The system aims to detect subtle failures in the spacecraft’s attitude dynamics system, particularly in the reaction wheel subsystem, by learning solely from the spacecraft's nominal behavioral data. The system was developed from a small satellite's attitude dynamics control system, which may exhibit bearing failures in the reaction wheels. Two types of anomaly detection systems were introduced: a two-sided learning anomaly detection system and a one-sided learning anomaly detection system. For this study, a two-sided learning anomaly detection system was developed using the Logistic Regression (LR) method. This provided a foundation for the training process using a machine learning approach. By learning from both nominal and failure behaviors of the satellite, the system was designed to detect small reaction wheel friction failures effectively.
European Journal of Advances in Engineering and Technology, 2023
This paper explores the critical role of systems engineers in the requirements engineering proces... more This paper explores the critical role of systems engineers in the requirements engineering process, emphasizing their contribution to the successful development of complex systems. Systems engineers act as the bridge between stakeholders and technical teams, ensuring that requirements are accurately captured, analyzed, and translated into actionable specifications. Their involvement is crucial in defining clear, feasible, and traceable requirements, which serve as the foundation for the entire project lifecycle. By integrating technical expertise with a deep understanding of stakeholder needs, systems engineers help mitigate risks, avoid costly rework, and ensure that the final system aligns with the intended goals. They play a key role in managing changes to requirements, balancing conflicting interests, and maintaining a focus on the system's overall objectives. The paper also discusses the methodologies and tools used by systems engineers to enhance the requirements engineering process, such as model-based systems engineering (MBSE) and requirements management software. Ultimately, the systems engineer's role is essential in driving the successful realization of complex projects, ensuring that they meet both technical and business objectives.
European Journal of Advances in Engineering and Technology, 2020
This paper presents a comprehensive exploration of advanced network systems and engineering, focu... more This paper presents a comprehensive exploration of advanced network systems and engineering, focusing on the integration of Layer-2 switching, Layer-3 routing, and the evolving architectures of digital networks. The study begins with a detailed examination of the fundamental principles underlying Layer-2 and Layer-3 technologies, highlighting their critical roles in network performance and reliability. It then delves into the architectural considerations and design strategies that optimize these technologies for current and future digital environments. The discussion extends to emerging trends in network access systems, emphasizing performance optimization, scalability, and security in increasingly complex and heterogeneous networks. By bridging foundational network engineering with cutting-edge developments, this paper provides a unified framework for understanding and advancing network systems in the digital era. Through case studies and theoretical analyses, the research offers insights into the practical applications of Layer-2/3 technologies within modern networks, proposing new directions for innovation and development. The findings aim to guide both practitioners and researchers in designing robust, future-proof network architectures that meet the demands of the ever-evolving digital landscape.
European Journal of Advances in Engineering and Technology, 2019
This research explores the multifaceted nature of software systems engineering by emphasizing the... more This research explores the multifaceted nature of software systems engineering by emphasizing the integration of qualitative factors throughout the software development lifecycle. This research provides a framework for analyzing, designing, and implementing complex software projects with a focus on maintainability, extensibility, reusability, and robustness. The study combines both top-down and bottom-up design approaches, enabling students to select and apply the most suitable methods based on technology, project duration, risk levels, and customer expectations. The research incorporates a Work Integrated Learning (WIL) experience, simulating industrial software engineering projects to bridge theoretical knowledge with practical application. Students gain hands-on experience in managing and executing software projects while receiving industry feedback to enhance their skills. The course covers essential IT concepts and project management principles, equipping students with the tools needed to succeed in the software engineering industry. This comprehensive approach ensures that graduates are well-prepared for real-world challenges in software development.
Journal of Scientific and Engineering Research, 2016
This Papers offers a comprehensive exploration of digital design, blending foundational concepts ... more This Papers offers a comprehensive exploration of digital design, blending foundational concepts with advanced techniques to address modern high-speed applications. This paper begins with an introduction to digital logic design, offering clear explanations of basic concepts such as Boolean algebra, combinational and sequential circuits, and timing analysis. It then delves into the Verilog hardware description language, guiding readers through essential coding practices and simulation strategies. As the content progresses, more complex topics are introduced, including design optimization, power management, and high-speed design techniques. The research also covers critical aspects of digital system design, such as timing closure, signal integrity, and verification methodologies, ensuring robust and reliable implementations. Numerous examples, practical exercises, and real-world case studies illustrate the application of Verilog in designing high-performance digital systems. This research is an invaluable resource for students, engineers, and professionals looking to enhance their knowledge and skills in digital system design, enabling them to tackle the challenges of modern electronic design with confidence.
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Papers by Iqtiar M Siddique
expands its scope to examine sustainability aspects within Bitcoin mining. It investigates the scope of using renewable energy sources such as wind, hydroelectric, solar, and geothermal. This in-depth exploration assesses the adaptability and potential synergies between these renewable energy
sources and Bitcoin mining operations. This study examines aspects such as energy efficiency, environmental effect, and economic viability to provide a comprehensive perspective on the possibility and challenges of merging wind, hydro, solar, and geothermal resources into sustainable
Bitcoin mining. The study's findings provide useful insights into Bitcoin's future and its potential impact on global financial systems and environmental sustainability. This thesis intends to use Bitcoin mining from Methane (CH4) flares to contribute to a more resilient and sustainable society while promoting positive environmental change.