10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
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Authors - Erick Verdugo, Andy Abad, Remigio Hurtado Abstract - Software defect prediction is crucial for reducing costs and improving quality. According to a Cutter Consortium report, software defects cause an estimated annual loss of $1.56 trillion in global productivity. Additionally, Tricentis reported that over 30% of software development projects failed due to undetected defects. Undetected defects can increase maintenance costs, delay deliveries, and compromise security, particularly in critical applications such as financial or medical systems. A significant challenge is dealing with imbalanced data, where there are more defect-free modules than defective ones, making detection difficult. This study proposes a four-phase approach: loading and transforming data, using balancing techniques, applying machine learning models, and explaining predictions. Techniques such as SMOTE, ADASYN, and RandomUnderSampling were used to balance the data, applied to models like Random Forest, Gradient Boosting, and SVM. The JM1 dataset, containing software quality metrics and 80% defect-free modules, was used for analysis. Data preprocessing involved imputation, encoding, and normalization. Results show that Random Forest and Gradient Boosting, combined with balancing techniques, achieved the best performance in defect identification. In the future, advanced algorithms such as XGBoost and LightGBM will be explored, and parameter optimization will be conducted to further enhance results. This approach aims to improve defect detection in software and to be applied in other fields.
Authors - Salma Mosaad Mohamed Elfeky, Mennaallah Nafady Ahmed Yehia, Ali Hamdi Abstract - This paper introduces a novel drone-based plant disease detection system optimized for efficient and scalable deployment using MLOps. Utilizing the CADI AI dataset for cashew crop disease classification, it includes automated workflows for iterative training, testing, and deployment across YOLO architectures (YOLOv5, YOLOv8, YOLOv9, and YOLOv10). Advanced data augmentation and incremental dataset expansion, growing from 757 training images to the full dataset, ensure fair evaluations and model optimization. YOLOv5 achieved a peak mAP@50 of 59.4%, followed by YOLOv8 with 50.1%. Iterative finetuning revealed YOLOv9’s superior insect detection performance (mAP@50: 70.9 %) and YOLOv10’s excellence in abiotic stress detection (mAP@50: 77.3%). This study highlights MLOps’ role in real-time model deployment and benchmarking, showcasing robust object detection capabilities and emphasizing iterative optimization and auto-deployment strategies to address dataset imbalance and enhance precision agriculture.
Authors - Ananya Deshpande, Akshay Angadi, Amulya H S, Adhokshaja R B, Nirmala Devi M Abstract - The increasing complexity of ICs and the reliance on external suppliers increase the risk of hardware Trojans, posing significant security threats. Traditional detection methods often fail due to limitations in addressing all potential vulnerabilities. This paper proposes a node compaction technique combined with an XGBoost classifier using features like Vulnerability Factor, Transition Probability, and SCOAP metrics to classify circuit nodes as Trojan-infected or Trojan-free. The compaction reduces execution time and improves real-time monitoring. The checker logic further validates the detection of Trojans by comparing the expected and observed functionality. Validation in TrustHub benchmark circuits demonstrates significant improvements in detection accuracy.
Authors - Kevin Lajpop Abstract - The Fast Fourier Transform (FFT) is a fundamental algorithm used in a wide range of applications, from signal processing to cryptography. With the increasing use of embedded and mobile devices, the need to optimize FFT performance has become crucial. This study focuses on the implementation of FFT on an ARM Cortex-A72 processor, leveraging NEON instructions, which are part of the SIMD (Single Instruction, Multiple Data) set. NEON instructions enable parallel operations, resulting in a significant improvement in execution times. Through a comparative analysis between implementations with and without NEON, a 99.99% reduction in execution time was demonstrated when using NEON, highlighting its effectiveness in applications that require high-speed processing, such as post-quantum cryptography.
Authors - Diego Loja, David Alvarado, Remigio Hurtado Abstract - Lung cancer, one of the leading causes of death worldwide, accounts for more than 2.2 million cases and nearly 1.8 million deaths. This type of cancer is classified into non-small cell lung carcinoma (NSCLC), the most common and slow-progressing type, and small cell lung carcinoma (SCLC), which is less common but highly aggressive [1]. In response to the urgency for rapid and accurate diagnosis, this work presents an innovative method for classifying PET images using the EfficientV2S model, combined with advanced data augmentation and normalization techniques. Unlike traditional methods, this approach incorporates visual explanations based on integrated gradients, enabling the justification of model predictions. The proposed method consists of three phases: data preprocessing, experimentation, and prediction explanation. The LUNGPETCT- DX dataset is utilized, comprising 133 patients distributed across three main classes: adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. The models are evaluated using quality metrics such as accuracy (78%), precision (82%), recall (78%), and F1-score (76%), highlighting the superior performance of EfficientV2S compared to other approaches. Additionally, integrated gradients are employed to visually justify predictions, providing critical interpretability in the medical context. For future work, the integration of CT images is proposed to enhance predictions, along with validation on larger datasets and optimization through fine-tuning, aiming to improve the model’s generalization and robustness
Authors - Ebin V Francis, T. Nirmala, Jiby Jose E. Abstract - This study investigates how media affected the career aspirations of College students in Kerala and finds a tendency associated with social media adjusting over conventional work. Using content analysis of digital media platforms, the research investigates how media content, trends, and narratives influence students’ perceptions of social media as a viable and desirable career path. This study seeks to determine what has changed over the last two decades, including whether peer effects, economic opportunities, or social acceptance are behind this shift. This study offers insights into how the career interests and preferences of the young generation in Kerala influenced by the media landscape can potentially impact the labour market and employment patterns among the youth in the region and aids in understanding the implications of media-influenced occupational aspirations and media patterns among the students in Kerala.
