Institute of Wood Science and Technology

Diabetic retinopathy (DR) is a leading preventable cause of blindness worldwide. It often has advanced progression prior to exhibiting significant symptoms. Early detection and accurate grading of DR are important for treatment and also for the prevention of visual loss. The severity levels of No DR, Mild, Moderate, Severe, and Proliferative DR have been categorised for retinal fundus images for this analysis using a Convolutional Neural Network (CNN) model. This chapter investigates the application of CNN model to detect Diabetic retinopathy. Grad-CAM or gradient-weighted Class Activation Mapping will be applied thereafter to improve interpretability by highlighting clinically relevant features like haemorrhages, microaneurysms and exudates. Using preprocessing techniques such as scaling, normalisation, and augmentation improved the model's ability to generalise on both the EyePACS and Messidor datasets. The CNN produced a macro-averaged recall of 92%, accuracy of 93%, precision of 91%, and ROC-AUC of 0.95. In other words, dividing the remaining probabilities by No DR (23.9%), Mild (7.5%), Severe (6.5%) and Moderate (3.8%) respectively, while there was a single case of Proliferative DR that had a 58.3% confidence. To deploy it in real time, a simple web application with Flask was developed to get Grad-CAM overlays and predictions in seconds. The system is a viable option for early DR screening due to its accuracy, interpretability, and usability, particularly in low-resource settings and clinical settings. Previous studies have initially demonstrated the effectiveness of deep learning-based systems for the detection and grading of diabetic retinopathy [1,6,11,15], which further encourages the adoption of CNN techniques in clinical screenings.

Background: Phishing contributes to over one-third of security incidents globally, highlighting the urgent need for robust detection systems. Aims: The aim of this study is to design and validate a phishing detection system that ensures accuracy, adaptability, and real-time deployment suitability. The system targets institutional and enterprise-level use, focusing on overcoming the shortcomings of traditional rule-based and blacklist approaches. Study Design: An experimental research study was conducted to evaluate multiple machine learning algorithms for phishing detection. The study adopted a comparative design to identify the most stable and efficient model. Place and Duration of Study: The work was carried out at the Department of MCA, Acharya Institute of Technology, Bangalore, India, between July 2025 and September 2025. Methodology: A dataset of SMS messages, consisting of 5,559 messages labelled (phishing and legitimate), was pre-processed using tokenisation, stop-word removal, and vectorisation (TF-IDF and BoW). Lexical, structural, statistical, and semantic features were engineered. Six classifiers—Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbours (kNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—were trained and evaluated using Accuracy, Precision, Recall, and F1-score. Cross-validation was applied for stability testing. A Django-based web interface was implemented for real-time predictions. Results: The proposed method uses many algorithms of machine learning with feature engineering to find phishing sites. Support Vector Machine achieved the best stability with 99.99% Accuracy, 98.99% Precision, 99.12% Recall, and 99.05% F1-score. MNB, kNN, and LSTM achieved near-perfect results, while CNN performed relatively lower (Accuracy 91.02%). Real-time system testing showed an average response time of 0.05 seconds per message. Conclusion: The proposed phishing detection system demonstrates strong accuracy, efficiency, and adaptability. Its lightweight design and real-time performance make it suitable for deployment in institutional servers, email systems, and organisational networks, providing an effective defence against evolving phishing attacks.

Personalised learning, which refers to an educational paradigm that allows individual students to dictate content and schedule of their learning, has begun to assume dominance as a new level of education transformation, and has been shown to promote better retention (by about 20%) when plans reflect the learner's states. This study delineates the implementation of a machine learning-based approach toward personalised study plan generation and personalised scheduling that takes into account individual lifestyle factors and attention profiles. This experimental study was carried out in the Department of MCA, Acharya Institute of Technology, Bengaluru, India (January 2024 - August 2024) and applied Random Forest regression (RF) and KMeans cluster analysis (k-means) to a dataset of student-specific schedules based upon eight lifestyle variables: sleep/hours, screen/hours, stress level, mental health rating, frequency of exercise, motivation level, time management score and daily study hours. The experimental study underwent a preprocessing stage; Random Forest exhibited strong performance (MAE of 3.46 and R-squared of 0.97) and identified importance ranking predictors of attention; participants' sleep and stress have been identified as the highest ranked predictors of attention in Random Forest. K-Means managed to classify learners into Low, Medium, and High focus profiles (k=3 and a silhouette score of 0.53 Which is moderately acceptable), which led to scheduled Pomodoro sessions being allocated on study plans. The Pomodoro sessions were allocated during study hours as the Largest Remainder Method. The system development utilised Flask and SQLite and was able to create a study plan in less than two seconds; system adaptations positively impacted feedback, enabling learners to complete 15% more of their study plans. The results presented here indicate that combining variables related to lifestyle will permit more personalised and effective study plans versus traditional methods. The main objective of the system presented was to have a scalable, responsive, and privacy-respecting solution to increase student focus, decrease procrastination behaviours, and improve learning outcomes, while also providing institutions with systems that can assist in adaptive, data-informed educational practices in the future.

Explainable Artificial Intelligence (XAI) is indeed causing an uproar in the sphere of medical image analysis. It is simply a matter of developing trust, creating transparency, and promoting clinical utilization of AI systems. This systematic review gathers the knowledge of 25 latest studies to provide us with a clear image of the current state of XAI, the issues in it, and of the possible future directions. The results demonstrate that there is a growing reliance on deep learning models that are enhanced with interpretability techniques, such as saliency maps, self-explainable architectures and ensemble techniques. The papers emphasize the need to consider clinical relevance, usability and compliance with regulatory standards, particularly in such crucial fields as breast cancer diagnosis, brain tumor classification and analysis of Alzheimer disease. Despite all this progress, there remain some large issues to address, such as the lack of standardized platforms of evaluation, the difficulties of connecting technical descriptions to clinical reasoning, and the necessity to uphold equitability and confidentiality in federated learning settings. Besides that, new solutions such as compression that protects privacy and multi-modal explanations are leading to more effective and reliable solutions. In this review, interdisciplinary teamwork, emphasis on human-centered design and standardization of benchmarks are highlighted as the necessary conditions to promote responsible and effective XAI in medical image analysis.

For many applications, Indoor navigation is crucial. Especially in environments such as large buildings, underground structures, and complex indoor spaces where GPS signals are weak or unavailable. In this work, a LiDAR-based navigation system that employs Simultaneous Localization and Mapping (SLAM) methods for accurate 2D mapping and localization in indoor environments is introduced. Because it always estimates its location relative to the environment, SLAM allows the system to build good maps of the environment. Optimal path planning is achieved by the use of the A* algorithm, providing a powerful method for finding the best paths to take and avoid obstacles. In addition, the project employs PointNet++ for advanced point cloud processing, which significantly enhances the ability of the system to analyze and classify indoor objects and structures and gain a deeper understanding of the environment. Integrating SLAM, A*, and PointNet++ creates a robust indoor localization and navigation system that is perfect for application in augmented reality, smart environments, and assistive technology for individuals with disabilities. Due to the high mapping and path planning accuracy of the system, it operates reliably in a wide range of real-world indoor environments and greatly enhances navigation efficiency and spatial awareness.

The growing demand for advanced security systems has highlighted the need for innovative surveillance solutions that are costeffective, efficient, and reliable. Traditional security systems often face limited coverage, high power consumption, and susceptibility to environmental factors. The research presents the design and implementation of an advanced security system, Invisible Eye, utilizing the STM32 Nucleo-F103RB microcontroller, PIR sensor, OV7670 camera, and a buzzer for realtime threat detection and alerting. The system leverages motion detection via the PIR sensor, image acquisition through the OV7670 camera, and immediate alerting mechanisms to enhance security monitoring. The methodology involves integrating hardware components with firmware developed in Arduino IDE to process sensor data efficiently. Key findings indicate that the proposed system provides accurate motion detection with minimal false alarms, consumes low power, and is adaptable to various security applications. This work significantly contributes to intelligent security solutions by offering a cost-effective and automated surveillance system.

In the proposed work a novel configuration of a printed 2-port MIMO antenna has been analyzed and investigated for mm wave applications. The proposed design is having dual band characteristics with frequency span of 27.70 $29.90 \mathrm{GHz}(2.2 \mathrm{GHz})$ and $32.50-35.20(2.7 \mathrm{GHz})$ which make the structure suitable for 5G mm wave communication. The designed antenna is printed on high frequency laminates of RT duroid $5880\left(\varepsilon_{\mathrm{r}}=2.2\right.$ and $\left.\tan \delta=0.0002\right)$ with thickness of 0.8 mm. The proposed design consist of a T shape feed line and rectangular slit loaded rectangular ground plane. The parametric analysis has been executed to get the final optimized structure in terms of geometrical parameters. The 2-Port MIMO configuration is achieved by replicating the single unit in orthogonal manner. In order to enhance the isolation between the 2-port further rectangular slot on the ground plane is etched out. All the important antenna findings for single port such as return loss, gain, radiation efficiency and for MIMO antenna diversity performance parameters such as ECC and DG has been simulated and calculated. The design offers a good isolation, high radiation efficiency making it suitable for high data-rate $\mathbf{5G}$ communications.

Wood’s flammability, low dimensional stability, and poor fungal resistance limit its application. In this study, Scots pine sapwood ( Pinus sylvestris L .) was treated with a low-molecular-weight phenol–formaldehyde (PF) resin and a monoguanidine phosphate flame retardant (FR) to achieve multifunctional enhancement. Differential scanning calorimetry confirmed no interference of FR with PF curing, while SEM and cell wall bulking analysis demonstrated effective penetration. The treatment improved the modulus of elasticity and rupture, though impact bending strength slightly decreased. Fungal decay resistance reached durability class 1 (EN 350, 2016) based on white- and brown-rot tests (EN 113–2, 2021). Dynamic vapor sorption showed no increase in equilibrium moisture content below 70 % RH. Artificial and natural weathering tests confirmed good FR retention and weathering resistance. Thermogravimetric analysis showed enhanced thermal stability, with higher char residue than FR-only treatments. Cone calorimetry verified sustained flame retardancy even after weathering. This combined treatment offers a durable, multifunctional wood modification approach suitable for structural and outdoor applications. • PF resin and monoguanidine phosphate salt effectively penetrate wood cell walls. • Enhanced mechanical properties, fungal resistance (DC 1), and dimensional stability. • Maintained durability and strong flame retardancy under artificial and natural weathering.