JOURNAL OF INTEGRATED SCIENCES

EDITORIAL: SYNTHESIZING THE FUTURE AT THE 7TH ICIS

Prof. Dr. Afroza Bulbul — 2026-02-28

The successful conclusion of the 7th International Conference on Integrated Sciences (ICIS 2025), held on October 25th and 26th, marks a pivotal moment in the global academic calendar. Jointly organized by Eastern University (EU), Bangladesh, and the International Open University (IOU), The Gambia, this year’s conference transcended the traditional boundaries of research. By adopting the ambitious theme, "Integrating Artificial Intelligence in Science, Business, and Islamic Paradigms," the 7th ICIS has set a new benchmark for interdisciplinary dialog in the digital age.

A Hybrid Nexus for Global Thought

The dual-mode format of the conference—in-person at the Eastern University campus in Ashulia and virtually via a global digital platform—effectively democratized the exchange of ideas. With over 400 papers submitted, the event drew a vibrant tapestry of scholars, industry leaders, and students. This logistical feat, spearheaded by Chief Patron Professor Dr. Farid A. Sobhani (Vice-Chancellor, EU) and Conference Chair Professor Dr. Muhammad Ahsan (Deputy Vice-Chancellor, IOU), underscored a crucial reality in 2025: integration is not just a theoretical goal but an operational necessity.

AI Through an Ethical Lens

While the rapid advancement of Artificial Intelligence dominated many sessions, the discourse remained refreshingly grounded. Unlike many technology-centric forums, ICIS 2025 did not view AI in a vacuum. Keynote speaker Dr. Muhammad Imadur Rahman and other luminaries explored the synergy between algorithmic efficiency and human-centric values. A standout feature of the conference was the emphasis on Islamic Paradigms, examining how ethical frameworks from Islamic scholarship can guide the responsible deployment of GenAI and autonomous systems.

Breaking the Silos

The conference tracks were as diverse as they were rigorous, covering:

Sustainable Development & Climate Change: Addressing the "triple planetary crisis" through data-driven solutions.
Islamic Banking and Economics: Exploring how fintech and AI can foster ethical finance.
Life Sciences & Biotechnology: Utilizing bioinformatics and AI to revolutionize public health.

This "integrated" approach is the only viable path forward for solving what scholars call "wicked problems"—challenges like global inequality and ecological degradation that no single discipline can resolve alone.

Rewarding Excellence

The event also served as a launchpad for the next generation of researchers. Through the Research Excellence Awards, sponsored by Dreamers Academy Australia, the conference recognized ten outstanding contributors from Bangladesh and abroad. These awards, alongside the Best Paper Awards, highlight the commitment of EU and IOU to fostering a culture of high-impact research.

The Road Ahead

As the delegates return to their respective institutions, the legacy of the 7th ICIS will be measured by the collaborations it sparked. The papers published in the Journal of Integrated Sciences will serve as a roadmap for practitioners looking to bridge the gap between technological innovation and ethical responsibility.

In an era where technology often moves faster than our ability to govern it, the 7th ICIS has reminded us that the "Integrated" in "Integrated Sciences" must always include the human spirit.

SMART HOME CLINIC AND REMOTE HEALTH MONITORING SYSTEM

Md. Mohiuddin — 2026-02-28

With the introduction of the new coronavirus, modern nations have placed a premium on healthcare. So, in this sense, a health monitoring system based on the Internet of Things (IoT) has become a significant area of study in light of the recent global epidemic. Healthcare technology has become very popular and has been considered one of the priority fields of development in many countries in the world. The introduction of Internet of Things (IoT) technology has made it much easier to transition from traditional in-person consultations to more convenient online interactions between patients and their healthcare providers. Because of the proliferation of wearable sensors and smartphone technology, remote healthcare monitoring has progressed to the point where patients no longer have to physically present themselves at a physician's office. The purpose of this system is to utilize the sensors such as temperature sensors, heartbeat sensors, humidity sensors, and some other sensors. The patient's bodily conditions may be read by this project, which can subsequently send to the Firebase cloud database and the doctor's web server. The Internet of Things (IoT) health monitoring aids in recognizing medical conditions and obtaining an accurate diagnosis of the patient's health status, even when the doctor is located at a great distance. An easy-to-use physiological checking framework has been designed for this project. This framework can continuously monitor the patient's heartbeat, as well as the temperature, humidity, and other fundamental characteristics of the room. This system has been presented for a nonstop checking procedure to monitor the patient's health status and save the patient's information in a server employing Wi-Fi Module. Additionally, this system monitors the patient's vital signs in real-time. According to the values obtained from the sensors in a remote health monitoring system based on the Internet of Things (IoT), illnesses may be diagnosed by doctors from a distance based on the data recorded using the Firebase cloud database. This technique will cut down on the amount of time spent going to the medical facility as well as standing in line to consult with the chamber physician.

APPLY OPTIMIZATION TECHNIQUES IN MACHINE LEARNING MODEL FOR BREAST CANCER DETECTION

Ramen Kumar Das — 2026-02-28

Breast cancer is a serious issue for humans and one of the most common disorders for women. In the era of technology, researchers try to address any complication with ML, AI, Optimization and DL. This study aims to address the breast cancer problem with optimization and ML algorithm. The goal of this work is to identify the best suite ML models and optimization techniques for breast cancer detection. This study employs six optimization techniques and three ML models for breast cancer analysis. Random search, Grid search, Bayesian, Ant Bee Colony, Blue Whale, particle swarm optimization with SVM, XG-Boost, and ANN architecture impose this study to investigate breast cancer. This study utilizes a quantitative correlational approach based on the Mammographic Mass dataset. In SVM and XG Boost, Grid search shows the highest accuracy of 86.46% and 85.93%. However, ANN outperforms the accuracy of 83.13% for the Random search approach. The findings of this work are that Random search and Grid search contain maximum accuracy, but concerning time, Bayesian optimization is superior.

TECHNO-ECONOMIC AND ENVIRONMENTAL ASSESSMENT OF RENEWABLE ENERGY SYSTEMS FOR ENERGY-PLUS BUILDINGS IN POLAND

Masud Parves — 2026-02-28

The European Union’s decarbonization targets necessitate a rapid transformation of the building sector toward net-zero emissions. This study presents a comprehensive techno-economic and environmental assessment of photovoltaic (PV), wind, and hybrid PV–wind systems for energy-plus buildings in six representative Polish cities: Białystok, Katowice, Lublin, Poznań, Szczecin, and Gdańsk. Using HOMER Pro simulation software, systems were optimized to minimize Levelized Cost of Energy (LCOE) while maximizing renewable fraction. Results indicate that inland cities achieve optimal performance with PV-only systems (LCOE: 0.50–0.60 USD/kWh), whereas coastal cities favor wind-only systems (LCOE: 0.65 USD/kWh) due to higher wind resource availability. Annual greenhouse gas (GHG) reductions range from 89% to 94%, equivalent to 6.5-7.4 tons CO₂ per building. Sensitivity analysis confirms the significance of capital cost reductions and financing conditions for economic viability. Policy recommendations include targeted incentives, PV-ready building codes, and grid modernization. The findings provide actionable insights for policymakers, investors, and engineers aiming to accelerate renewable integration in Poland’s residential sector.

EMPLOYER RESISTANCE TO THE FOUR-DAY WORK WEEK IN BANGLADESH: AN EXPLORATORY STUDY OF ORGANIZATIONAL PERSPECTIVE

A H M Rafsan Uddin Khondokar — 2026-02-28

The idea of working four days a week instead of five sounds exciting less stress, more balance, and maybe even better productivity. Around the world, trials in places like Iceland and New Zealand have shown exactly that. But in Bangladesh, things aren’t so straightforward. We wanted to find out why many employers are still hesitant, so we talked to 18 organizations from industries such as garments, banking, IT, healthcare, and education. What we found was a mix of curiosity and concern. Employers worry about how clients would react if services were unavailable for an extra day, whether deadlines could still be met, and if productivity might drop. Cultural habits also play a role here, long hours are often seen as proof of commitment and the law doesn’t clearly support flexible work models yet. Still, most leaders agreed the idea has promise, especially for keeping employees happy and reducing stress. The challenge is that there’s no local evidence yet. Our study suggests Bangladesh doesn’t need a big leap all at once small, well-planned pilot programs in sectors like IT and education could show what’s possible and help build confidence. With the right support, a four-day work week could gradually become a reality that works for both businesses and people.

EFFICIENT RANSOMWARE DETECTION USING ENSEMBLE CLASSIFIERS

Sharmin Akter — 2026-02-28

Ransomware is one of the most widespread and damaging threats in cybersecurity. These malicious applications encrypt valuable user data using powerful cryptographic algorithms, rendering it unobtainable until payment of a ransom is made, typically by way of cryptocurrencies such as Bitcoin. Signature-based detection methods are slow in terms of reaction time, and they cannot accurately deal with the new or zero-day ransomware strains. To address these problems, this paper introduces and evaluates an ensemble machine learning classifier trained on static features collected from Windows Portable Executable (PE) files. The model was trained and tested on a dataset consisting of around 20,000 instances, each with 134 static attributes. The experimental results prove that the model proposed in this work achieves a high detection accuracy of 98.80%, and is characterized by an extremely low false positive rate (0.26%) together with a very high true positive rate (97.08%). These observations also demonstrate that static-based ensemble classifiers are an effective approach to establish a proactive, lightweight, and scalable defense against modern ransomware attacks.

INTENTION TO ADOPT CHATGPT BY ACADEMICIANS UTILIZING DIFFUSION INNOVATION MODEL

Ayeasatul Kobra — 2026-02-28

In this era of fourth industrial evaluation, users’ acceptance and adoption of Artificial Intelligence (AI) by its users complements a large literature and practical dimension shift, especially after the 2020 pandemic in the research area. The purpose of this study is to explore and investigate the central constructs and factors influencing academicians' intention to adopt ChatGPT in Bangladesh by academicians in their traditional workflows. This paper adopts Diffusion Innovation Theory (DIT). This paper analyzed a total of 148 academicians from both public and private universities, to find the intensity and willingness of their intention of using AI in their work purpose. The convenience sampling method was used in this study's quantitative approach to gather data. The study's findings revealed that complexity and compatibility are the most contributing factors in the adoption of ChatGPT by the academicians in Bangladesh, while conversely the variables relative advantage and observability don’t affect the adoption intention on a large scale. An external variable social influence is integrated as a moderator in this study. This study's practical consequences are relevant for universities in Bangladesh. The study also suggests universities may develop training programs to adopt new technologies and give facilities to make their education system more effective and smoother. The findings and implications of this study not only affect the workflow of the academicians on a large scale but also offer meaningful insights for educational leaders to build a technology-advanced & AI-based academic environment to standardize the learning sessions and environment.

ARTIFICIAL INTELLIGENCE IN PRE-DIABETES: CGM-DERIVED PHENOTYPES AND NON-INVASIVE BIOMARKERS FOR EARLY DETECTION AND RISK STRATIFICATION

Sajib Paul — 2026-02-28

Prediabetes represents a critical window for preventing progression to type 2 diabetes mellitus (T2DM). Artificial intelligence (AI) offers novel opportunities for precision prevention through early detection, risk stratification, and individualized lifestyle interventions. We reviewed twenty-seven recent studies that explored applications of AI in prediabetes, including machine learning models leveraging electronic health records, deep learning approaches using wearable sensors, and non-invasive digital biomarkers. Emerging evidence strengthens the case for AI-enabled prediabetes management, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.79 to 0.91 and root mean squared error (RMSE) below 15 mg/dL, surpassing conventional methods. AI-driven clustering reveals distinct metabolic phenotypes, highlighting the potential for tailored preventive strategies. Despite promising performances, challenges such as external validation, model generalizability, fairness, and clinical implementation remain. Current literature is also limited by homogeneous study designs and poor external validation, restricting opportunities for clinical translation. Our findings underscore the transformative potential of AI in prediabetes management by uniquely integrating available evidence, while emphasizing the need for rigorous translational studies to achieve scalable, data-driven, and personalized prevention at the population level.

DIAGNOSIS OF MATERIALS COMPOSITION USING NUMERICAL METHODS

Zoubir Laallam — 2026-02-28

In today's world of computer vision, different methods for image processing are heavily used. In this paper, we have processed with MATLAB images of an aluminum alloy after TIG welding and other images of TiO₂ nanoparticles obtained by SEM that we synthesized by the green synthesis process. Also, we processed two other samples of Bubble dissolution during the in-situ growth of copper dendrites experiment and Liquid phase growth of FFA hexagonal crystals after radiolysis obtained by a TEM. Finally, we used the Newton and Chebyshev numerical methods in order to detect the present elements in these samples.

THE CHALLENGES OF LEARNING ARABIC LANGUAGE USING INSTRUCTIONAL VIDEOS FOR NON-NATIVE SPEAKERS OF ARABIC: A CASE STUDY OF THE NEW HOPE COLLEGE

Faruki Kasisi — 2026-02-28

This research investigates the difficulties of learning Arabic via instructional videos among non-native students in New Hope College. Located at the intersection of second language learning, educational technology, and learner experience, the research investigates pedagogical, linguistic, cognitive and technological challenges that learners encounter while interacting with video-based instruction. Utilizing the qualitative case study design with inner mixed--subcategories, 30 to 40 learners and 3 to 5 instructors were surveyed, interviewed and observed within a classroom context on the paradigm, complemented by content-analysis of instructional videos. Thematic and statistical analysis identified three main limitations, namely lack of interaction, excessive reliance on passive learning and videos' inability to meet individual learner requirements. Linguistic difficulties, such as Arabic diglossia, orthographic intricacy and pronunciation obstacles were compounded by little feedback and cultural embedding. Other cognitive obstacles, for example, overload caused by poorly designed videos, act as impediments on learning outcomes. A technological and institutional environment of varying Internet availability, inadequate teacher training, and poor integration of videos into blended designs exacerbated these challenges. The research findings highlight the need for pedagogically informed, culturally sensitive, and cognitively optimized video design, supported by teacher scaffolding and blended learning strategies. The study concludes that instructional videos are valuable supplementary tools but insufficient as stand-alone resources for effective Arabic acquisition.

LEVERAGING HUMAN-ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING (ML): COLLABORATIVE APPROACH FOR ENHANCING WASTE MANAGEMENT PRACTICES IN BANGLADESH TO ACHIEVE ZERO-WASTE

Mehereen Khan Mila — 2026-02-28

Bangladesh struggles in managing the underdeveloped and poorly enforced waste management system, which has far-reaching consequences for the environment, public health, urban resilience, and long-term sustainability. In recent years, the transformation of waste management through AI and ML techniques into waste management enables innovative solutions for forecasting, classification and optimization, leading to more efficient, user-friendly, and cost-effective recycling systems which represents a significant step forward in enabling more efficient and zero-waste practices to reduce environmental challenges. This study aims to provide insights into the current waste management challenges and identify the effectiveness of the interventions of Artificial Intelligence (AI) and Machine Learning (ML) with human collaboration by adapting strategies from developed countries in improving waste management processes and mitigating environmental pollution to achieve Zero-Waste goals by fostering more sustainable and efficient urban waste management practices. This study utilized a systematic literature review, analyzing peer-reviewed articles, previous research, case study and different strategies from developed countries to better identify best practices and technological approach and investigate how the application of artificial intelligence and machine learning can be applied in the waste management system of Bangladesh. The rate of waste generation in Bangladesh is increasing. According to Bangladesh Waste Database 2021, per capita waste is projected to rise from 0.52 kg/day to 0.74 kg/day by 2041 and pushing national waste to nearly 51.2 million tons. According to the "Food Waste Index Report 2024", 14.10 million tons of food is wasted every year by households in Bangladesh. Human and AI collaboration and machine learning combined with policy integration, community participation, and the adoption of strategies from developed countries along with inclusive solutions may provide a scalable pathway for Bangladesh to reduce waste, enhance recycling and achieve Zero-Waste goals in future.

TRANSFORMATIONAL LEADERSHIP AND ORGANIZATIONAL CHANGE IN BANGLADESHI HIGHER EDUCATION INSTITUTIONS

Protiti Sahajee Prachi — 2026-02-28

Effective transformational leadership and strategic organizational change are key drivers of Bangladeshi Higher Education (BHE), as understanding their role can address gaps in innovation, effectiveness and sustainability. The influence of leadership reveals ongoing challenges and offers insights to enhance institutional adaptability and the overall quality of higher education delivery. While previous studies focused on short-term outcomes, the long-term effects of transformational leadership in BHE are not well understood, especially regarding achieving lasting reforms across higher education. This study uses longitudinal data from 1994-2023 to assess whether meaningful changes have occurred in BHE and how they relate to transformational leadership practices. Key indicators such as Impact Score, Policy Effectiveness, Innovation Index and Adoption/Sustainability Ratings are evaluated to determine the durability and effectiveness of institutional reforms. Findings indicate that sustainability lags behind other measures, which limits the long-term effects of transformational leadership. Even though policy effectiveness and innovation remain moderately strong and adoption rates fluctuate, it is important to strengthen sustainability integration and support mechanisms that encourage change in order to make sure that innovations and policies lead to long-lasting, meaningful changes in Bangladeshi higher education institutions. Overall, the analysis points out the significance of prioritizing sustainability in Bangladeshi higher education and stimulating transformation through training, incentives and strong leadership. By combining longitudinal evidence with actionable strategies, this study provides practical approaches to enhance institutional performance and integrate sustainability across higher education.

PRODUCT INNOVATION IN ISLAMIC COOPERATIVES THROUGH THE LENS OF MAQĀSID SHARIA: EVIDENCE FROM INDONESIA

Gayuh Erlis Suminar — 2026-02-28

The objectives of Islamic law are articulated in the concept of maqāsid shariah, which encompasses the preservation of religion, life, wealth, intellect, and lineage. The implementation of maqāsid shariah extends across various domains, including Islamic cooperatives. In Indonesia, Islamic cooperatives have developed not only in terms of quantity but also in the variety of products offered to the public. The core products of Islamic cooperatives—covering financing, funding, business, and services—have been further diversified to meet the needs of cooperative members and society at large. This paper discusses the implementation of maqāsid shariah in the context of product innovation within Islamic cooperatives. Employing literature study and observation methods, it explores the nature of product innovations in Islamic cooperatives and their alignment with maqāsid shariah. The findings indicate that product innovations within Islamic cooperatives are consistent with and contribute to the realization of maqāsid shariah.

FROM USE TO AWARENESS: EMBEDDING CRITICAL AI LITERACY IN TERTIARY EFL CLASSROOMS IN BANGLADESH

Tabassum Mahmud Ornab — 2026-02-28

The rapid integration of Artificial Intelligence (AI) into education has reshaped the practices of language teaching and learning worldwide. In the context of Bangladesh, the emergence of AI demands critical scrutiny. Because, tertiary-level English as a Foreign Language (EFL) education faces constant challenges such as limited resources, uneven digital literacy, and traditional pedagogy. This study investigates the role of critical AI literacy, the ability to use AI tools effectively as well as to evaluate their implications, biases, and ethical concerns in enhancing EFL teaching and learning. Theoretical frameworks of digital and critical literacies are utilized in this research. These explore how AI-powered applications such as, chatbots, automated writing evaluators, and translation tools are perceived and utilized in the Bangladeshi tertiary context. Quantitative data were collected through surveys of 160 students and 30 faculty members from multiple universities. Semi-structured interviews from 10 faculty members across selected institutions are also taken. The findings reveal a dual narrative. AI tools help to improve learner autonomy, feedback practices, and communicative competence. But the lack of critical awareness often leads to unreflective reliance and plagiarism risks. It may diminish critical thinking faculty. The study emphasizes on embedding critical AI literacy into EFL curricula. So that students can develop both functional proficiency and ethical responsibility in their academic and professional engagements. This study argues that implanting critical AI literacy into tertiary-level EFL education in Bangladesh is essential to balance the benefits of AI-assisted learning with ethical awareness, thereby fostering learner autonomy, digital proficiency, academic integrity, and future-ready global competence.

IMPERATIVES FOR SUSTAINABILITY OF WAQF THROUGH PARTICIPATION OF NON-MUSLIMS IN THE MODERN WORLD

Abdullahi Saliu Ishola — 2026-02-28

There is a general belief in many quarters that Islamic schemes are only for Muslims. While this is true strictly in religious rites, it is not so in social and commercial affairs. Within the scope of Islamic law, waqf can be situated within social/contractual matters popularly called (Muʿāmalah). This, on the surface, makes its rules flexible unlike religious rites whose rules are not so flexible in its fundamentals. Again, waqf is also a religious undertaking by which the pleasure of Allāh is sought. It is confusing if non-Muslims can get involved in waqf. As an instrument of poverty alleviation, Waqf is not just concerned about the poor conditions of Muslims but also provides succor to non-Muslims as well. Accordingly, this paper exposes participation of non-Muslims in Waqf as an imperative for waqf sustainability. Through this study, waqf stakeholders across jurisdictions would be better informed and get guided on the way and manner non-Muslims can be involved in Waqf affairs and therefore assist in the sustainability drives for waqf across jurisdictions. The paper adopts the doctrinal research methodology. The study found that participation of non-Muslims in Waqf enjoys no uniform rulings among the Schools of Sharīʿah. This study is unable to undertake a case study of specific jurisdictions with regards to participation of non-Muslims in their waqf activities and this is its limitation. Following the exposition made in this study, each jurisdiction would now be better concerned about the impacts which its position would have on the sustainability of waqf with regards to the involvement of non-Muslims in the sector.