Author (s) : DOI : 10.32692/IJDI-ERET/15.1.2026.2601

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1). Dr. Manvi Breja, The NorthCap University, Gurugram, Haryana, India

Abstract :

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Answering non-factoid questions such as why and how type questions require exhaustive knowledge of contextualization and common-sense reasoning. Traditional neural techniques help in finding semantic similarity but ignore the causal context, which is handled by incorporating symbolic techniques for interpreting the concepts at reasoning level. The paper proposes context-aware neural-symbolic reasoning system which integrates the transformer-based context representation with several traditional causal, syntactic and semantic features to improve the performance of answer re-ranking module. The datasets with the evaluation metrics that are utilized for each module of framework are also discussed in detail. Further the framework is evaluated against standard baseline models such as BM25, DPR, Cross-Encoder and HGN on benchmark datasets HotPotQA, ELI5, BioASQ and Consumer Health QA. Integrating symbolic causal features with neural encoders helps in increasing the performance of ranking metrics as compared to basic models.

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Author (s) : DOI : 10.32692/IJDI-ERET/15.1.2026.2602

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1). Meet K. Vaghela, Darshan Institute of Engineering & Technology, Rajkot, Gujarat, India
2). Ketan Abhani, Darshan Institute of Engineering & Technology, Darshan University, Rajkot, Gujarat, India

Abstract :

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This study investigates the influence of Nano-Silica (NS) and Graphene Oxide (GO) on the physical and mechanical properties of M50 grade concrete. Sixteen different concrete mixes were prepared, including a control mix, individual NS and GO mixes, and hybrid mixes using both materials. Workability tests (slump and compaction factor), strength tests (compressive, split tensile, and flexural), and non-destructive tests (rebound hammer and ultrasonic pulse velocity) were conducted. Results indicate that the addition of NS and GO reduces workability due to increased fineness, but significantly improves mechanical strength and surface hardness. The hybrid mix containing 1.0% Nano-Silica and 0.06% Graphene Oxide exhibited the highest strength improvement, showing approximately 30% enhancement compared to the control mix. The ultrasonic pulse velocity results classified this mix under excellent-quality concrete. The study concludes that the combined use of Nano-Silica and Graphene Oxide significantly improves the strength, durability, and quality of concrete, making it suitable for high-performance structural applications.

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Author (s) : DOI : 10.32692/IJDI-ERET/15.1.2026.2603

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1). M. N. Nwoga , Enugu State University of Science and Technology, Enugu, Enugu State, Nigeria
2). Charles Chinwuba Ike, Enugu State University of Science and Technology, Enugu, Enugu State, Nigeria
3). Ugwu Juliet Nneka, Enugu State University of Science and Technology, Enugu, Enugu State, Nigeria

Abstract :

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In an effort to utilize agro waste products such like rice husk ash (RHA) and palm kernel shell ash (PKSA) to improve the geotechnical behaviour of soil. While also, reduce cost and minimized the lengthy procedures common in geotechnical laboratory tests. This study focused on developing a predictive model for optimizing the influence of RHA and PKSA on ESUT Agbani soil California Bearing Ratio (CBR). Tests revealed that the natural soil exhibited a CBR value of 6%. Scheffe’s model was employed to achieve optimization. Optimal proportions were determined to be 5.30: 0.26: 0.18: 0.28 for soil, RHA, PKSA and water correspondingly, produced an unsoaked CBR of 27%, equivalent to the output Y12. Formulated model estimated unsoaked CBR at 5 mm penetration for soil treated with RHA and PKSA was expressed as: ?CBR at 5mm?_ ((unsoaked)) =?17e?_1+?21e?_2+?13e?_3+?10e?_4+?32e?_1 e_2+?24e?_1 e_3+?38e?_1 e_4+?4e?_2 e_3+?10e?_2 e_4+?22e?_3 e_4. Model performance was verified by F-test and t-test. The computed F-statistic of 1.8845 was below the critical F-value of 3.1789, and the t-statistic of –0.5849 was lower than the critical t-value of 2.1199. These outcomes confirm that the model is valid at the 95% confidence level. It demonstrated no meaningful difference between the predicted and experimental CBR values. Accordingly, the model was considered adequacy and the null hypothesis was retained. Model validation was carried out using Microsoft Excel 2016.

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Author (s) : DOI : 10.32692/IJDI-ERET/15.1.2026.2604

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1). Krish Satasiya, Silver Oak College of Engineering & Technology, Ahmedabad, Gujarat, India

Abstract :

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The integration of Artificial Intelligence (AI), the Internet of Things (IoT), and wearable technology is catalyzing a fundamental shift in healthcare, moving the paradigm from reactive, episodic treatment to a proactive, preventive, and personalized model. This paper explores how AI-driven Remote Patient Monitoring (RPM) systems, powered by data from wearables and IoT devices, facilitate early diagnosis, hyper-personalized treatment plans, enhanced patient engagement, and significant cost efficiencies. The benefits are manifold, with robust evidence demonstrating improved clinical outcomes and substantial reductions in healthcare expenditures. This paper provides a comprehensive analysis of the key benefits and real-world applications of AI-driven RPM, including its impact on chronic disease management, post-operative care, and mental health monitoring. It also critically examines the primary implementation challenges, including navigating complex regulatory landscapes like HIPAA and GDPR, achieving technical interoperability through standards such as FHIR, addressing the ethical imperative of algorithmic bias, and overcoming barriers to provider adoption. Finally, the paper looks to the future, exploring the convergence of RPM with next-generation technologies like predictive genomics, digital twins, and 6G networks, which promise to usher in an era of truly predictive, pre-symptomatic, and hyper-personalized healthcare.

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Author (s) : DOI : 10.32692/IJDI-ERET/15.1.2026.2605

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1). Dr. Manvi Breja, The NorthCap University, Gurugram, Haryana, India
2). Gaurvi Rana, The NorthCap University, Gurugram, Haryana, India

Abstract :

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Women’s safety is one of the major concerns across the globe, particularly in urban areas where incidents of harassment, assault, domestic violence and crime continues to increase. As the urbanization has evolved, there is a need for some technology that can guard and ensure the women’s safety. The paper aims to address the safety challenges by reviewing the current landscape of mobile applications dedicated to women safety and by proposing a mobile application, NYX as a next-generation women safety application that incorporates intelligent emergency response tools with integrating techniques of Artificial Intelligence (AI) and sentiment analysis. Although existing mobile applications include features like emergency contacts or panic buttons, but lack the proactive response mechanisms. For this, the paper introduces NYX, a mobile application which bridges these gaps by integrating advanced techniques of AI, ML and sentiment analysis to provide real-time assistance and informed decision making. The application provides real-time location tracking, detecting high risk prone areas through sentiment analysis of live news, fake calling to escape threatening situations and video voice memo recording for capturing evidences. Real-time location tracking, AI-based sentiment analysis of the surrounding news to identify high-risk areas, fake call triggers to get out of a problematic situation, and instant video/voice memo recording for proof. It also supports SOS alerts, automated group calls to predefined contacts, and a locator of a nearby police station to act as fast as possible in emergency situations.

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Author (s) : DOI : 10.32692/IJDI-ERET/15.1.2026.2606

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1). Cyril Nwachukwu Okwueze, Chukwuemeka Odumegwu Ojukwu University, Uli, Anambra State, Nigeria
2). A.M. Udefi, Grundtvig Polytechnic, Oba, Anambra State, Nigeria
3). John Paul Iloh, Chukwuemeka Odumegwu Ojukwu University, Uli, Anambra State, Nigeria
4). Chidiebere Nnaedozie Muoghalu, Chukwuemeka Odumegwu Ojukwu University, Uli, Anambra State, Nigeria

Abstract :

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This paper presents a performance analysis of digital modulation schemes over different wireless channel models. A transceiver system was developed in MATLAB/Simulink to evaluate the Bit Error Rate (BER) of BPSK, 8-PSK, 16-QAM, and 64-QAM under Additive White Gaussian Noise (AWGN), Rayleigh fading, and Rician fading conditions. Simulations were conducted across signal-to-noise ratio (SNR) values ranging from 0 to 15 dB, with diversity orders varying from 1 to 5. The results show that BER performance improves with increasing SNR and diversity order for all modulation schemes. Rician fading consistently outperforms Rayleigh fading due to the presence of a line-of-sight component, while AWGN provides the best baseline performance. Among the modulation schemes, BPSK achieves the lowest BER and is most resilient to fading, though it sacrifices spectral efficiency. Higher-order schemes such as 16-QAM and 64-QAM provide higher data rates but suffer from increased error rates, particularly in Rayleigh channels. Overall, the study highlights the fundamental trade-off between reliability and spectral efficiency in wireless communication and underscores the importance of selecting modulation schemes based on channel conditions and system requirements.

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Author (s) : DOI : 10.32692/IJDI-ERET/15.1.2026.2607

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1). Nelson Uzochukwu Ifegbunam , Enugu State University of Science & Technology, Agbani, Enugu State, Nigeria
2). Charles Chinwuba Ike, Enugu State University of Science and Technology, Enugu, Enugu State, Nigeria

Abstract :

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In this study, the hydraulic conductivity values obtained from the falling head permeability tests for the ten soil samples vary between 1.5 × 10?5 cm/s and 7.9 × 10?5 cm/s, indicating a relatively narrow range of permeability across the study area. All samples fall within the order of 10?5 cm/s, which is characteristic of fine-grained soils, such as silty clays or clayey sands. Sample 7 exhibited the highest hydraulic conductivity (7.9 × 10?5 cm/s), implying a comparatively soil structure that may be associated with a higher proportion of coarse particles or reduced plasticity. Conversely, Sample 4 recorded the lowest hydraulic conductivity (1.5 × 10?5 cm/s), suggesting a soil with increased fines content which restricts water flow. From an engineering perspective, the low hydraulic conductivity values indicate that the soils possess adequate resistance to fluid flow, making them potentially suitable for use as subgrade materials, embankment fills, or barrier layers, if other geotechnical requirements such as strength and compressibility are satisfied. Furthermore, the results highlight the sensitivity of hydraulic conductivity to minor changes in soil structure, underscoring the importance of proper compaction control during construction to maintain desirable permeability characteristics. Overall, the permeability behavior observed in this study aligns with expectations for fine-grained soils and provides a reliable basis for evaluating the suitability of the soils for geotechnical applications.

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Author (s) : DOI : 10.32692/IJDI-ERET/15.1.2026.2608

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1). S. I. Sarsam, Sarsam and Associates Consult Bureau (SACB), Baghdad, Baghdad, IRAQ

Abstract :

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Active Acoustic Emission (AE) can be defined as a non-destructive test (NDT) monitoring technique that can be used to capture elastic waves generated by the release of a stored strain energy within AC structure, it can enable the crack initiation detection and the progress of the internal damage at the microscopic scale. In this assessment, specimens of AC have been prepared with optimum binder requirements using Marshall method. Specimens were subjected to test for fatigue using dynamic indirect tensile stresses with the aid of PRLS (pneumatic repeated load system) at intermediate environment of 20°C and monitored through AE technology. Dynamic indirect tensile stress (DITS) was applied to the diametral specimen, and the vertical strain is recorded after each load repetition. A constant load frequency of 60 cycles per minute and loading sequence for each cycle is 0.9 sec of rest period and 0.1 sec for load duration. The pulse velocity before and after 600 and 1200 load repetitions application of the DITS was recorded. Testing was terminated after the specified load repetitions, and the specimens were removed from the PRLS testing chamber and seated in an oven for 120 minutes at 60 °C to allow microcrack healing. Specimens were returned to the testing chamber and practiced a second round of load repetitions. Deformation of the mixtures after the first loading repetition declined by (91, and 97.7) % for (wearing and binder) courses respectively after practicing the microcrack healing process. Dynamic modulus of asphalt concrete (DMAC) before microcrack healing declined by (10.4, and 10.1) % for binder and base courses respectively as compared with the modulus of the wearing course. After microcrack healing it declined by (13.6, and 18) % for binder and base courses respectively as compared with the wearing course condition. Deterioration of the mixtures was monitored through AE technology and modeled. AE technology is recommended for prediction of the damage performance of AC through its fatigue life.

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