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 - Shahd Tarek, Ali Hamdi Abstract - Brain tumors represent one of the most critical health challenges due to their complexity and high mortality rates, necessitating early and precise diagnosis to improve patient outcomes. Traditional MRI interpretation methods rely heavily on manual analysis, which is timeconsuming, error-prone, and inconsistent. To address these limitations, this study introduces a novel deep attentional framework that integrates multiple Convolutional Neural Network (CNN) base models—EfficientNet- B0, ResNet50, and VGG16—within a Multi-Head Attention (MHA) mechanism for robust brain tumor classification. Convolutional features extracted from these CNNs are fed into the MHA as Query (Q), Key (K), and Value (V) inputs, enabling the model to focus on the most distinguishing features within MRI images. By leveraging complementary feature maps from diverse CNN architectures, the MHA mechanism generates more refined, attentive representations, significantly improving classification accuracy. The proposed approach classifies MRI images into four categories: pituitary tumor, meningioma, glioma, and no tumor. A dataset of 7,023 labeled MRI images was curated from public repositories, including Figshare, SARTAJ, and Br35H, with preprocessing steps to standardize dimensions and remove margins. Experimental results demonstrate the superior performance of individual CNNs—VGG16 achieving 97.25% accuracy, ResNet50 98.02%, and EfficientNet-B0 93.21%. Moreover, the ensemble model integrating VGG16, EfficientNet-B0, and ResNet50 achieves the highest accuracy of 98.70%, surpassing other ensemble configurations such as ResNet50 + VGG16 + EfficientNet-B0 (96.95%) and VGG16 + ResNet50 + EfficientNet-B0 (95.96%). These findings underscore the effectiveness of multi-level attention in refining predictions and provide a reliable, automated tool to assist radiologists. The proposed framework highlights the transformative potential of deep learning in medical imaging, streamlining clinical workflows, and enhancing healthcare outcomes.
Authors - Luis Puebla Rives, Connie Cofre-Morales, Miguel Rodriguez, Eduardo Puraivan, Marisela Vera Medinelli, Abigail Gonzalez, Ignacio Reyes, Karina Huencho-Iturra, Macarena Astudillo-Vasquez Abstract - This study analyzes the perception of both practicing and future English teachers regarding an activity designed under a didactic conceptual framework that uses SCRATCH as a tool to promote English language teaching to 4th grade primary school students. A survey was designed, validated by experts, and then applied to 28 participants. The reliability of the scale was analyzed, showing internal consistency of 0.96 and 0.99 using Cronbach’s alpha and G6, respectively. Implicative statistical analysis was used to explore the relationships between questions across different dimensions. The similarity tree identified two significant clusters with values of 0.6 and 0.54. The implicative graph and cohesive tree displayed implications with values exceeding 0.7. The findings highlight a high appreciation for the activity using SCRATCH, which is perceived as both viable and an effective facilitator of contextualized and meaningful learning.
Authors - La-or Kovavisaruch, Kriangkri Maneerat, Taweesak Sanpechuda, Krisada Chinda, Sodsai Wisadsud, Thitipong Wongsatho, Sambat Lim, Kamol Kaemarungsi, Tiwat Pongthavornkamol Abstract - The industrial sector in Thailand remains primarily characterized by traditional practices of Industry 2.0, which face significant challenges in transitioning to Industry 4.0. This research proposes a decentralized real-time location and status reporting system to address these issues. By utilizing Ultra-Wideband (UWB) technology combined with the Internet of Things (IoT), the newly developed "UWB Tag Plus" device eliminates the reliance on costly UWB gateways, instead transmitting data directly to cloud servers via 4G/5G networks. Implementing this system at an automotive parts assembly factory in Thailand reduced system costs by over 30%. The communications protocol between the tag and cloud server changed from IEEE 802.15.4 to TCP/IP, which enhanced operational flexibility. The proposed system makes advanced modernization more accessible for small and medium-sized enterprises. Furthermore, the "UNAI Data Analytic" tool provides real-time performance analytics for automated guided vehicles, empowering warehouse operators to optimize operations and improve efficiency.
Authors - Junichiro Ando, Satoshi Okada, Takuho Mitsunaga Abstract - Large Language Models (LLMs) like ChatGPT and Claude have demonstrated exceptional capabilities in content generation but remain vulnerable to adversarial jailbreak attacks that bypass safety mechanisms to output harmful content. This study introduces a novel jailbreak method targeting Autodefense, a multi-agent defense framework designed to detect and mitigate such attacks. By combining obfuscation techniques with the injection of harmless plaintext, our proposed method achieved a high jailbreak attack success rate (maximum value is 95.3%) across different obfuscation methods, which marks a significant increase compared to the ASR of 7.95% without our proposed method. Our experiments prove the effectiveness of our proposed method to bypass Autodefense system.
Authors - Hana Ulinnuha, Mukhlish Rasyidi, Yanti Tjong, Husna Putri Pertiwi, Wendy Purnama Tarigan, Michael Tegar Wicaksono Abstract - Recently, tourism villages are central to Indonesia’s tourism development strategy, contributing significantly to local, regional, and even national economic growth. With the increasing number of tourism villages, understanding tourists’ perspectives is essential for ensuring their sustainability. Tourist reviews on platforms provide valuable insights into their experiences and expectations. Sentiment analysis, widely used in tourism research, enables the extraction and identification of opinions from these unstructured data sources, offering a deeper understanding of visitor sentiments. This study employs Large Language Models (LLM) to analyze tourist reviews of Indonesian tourism villages. Unlike common methods, LLMs provide advanced capabilities for both sentiment analysis and the evaluation of the 4A tourism components—Attraction, Accessibility, Amenities, and Ancillary services. By examining positive, neutral, and negative reviews, the research identifies key factors that shape tourist experiences. The findings offer practical recommendations for tourism village managers, not only to enhances visitor satisfaction but also supports the government’s goal of fostering economic growth in tourism and rural areas. The study demonstrates the potential of LLM-based sentiment analysis as a valuable tool for advancing Indonesia's tourism industry.
Authors - Mark Bhunu, Timothy T Adeliyi Abstract - The proliferation of social media (SM) platforms has made them an integral part of our daily lives, significantly shaping how we interact and engage with the world. While SM offers benefits such as social connectedness and sup-port, its impact on the psychological health and well-being of young individuals has both positive and negative dimensions. Understanding these effects is essen-tial to developing strategies for mitigating the adverse outcomes associated with its use. A systematic literature review was conducted to explore the influence of SM usage on the mental health and well-being of young adults aged 18 to 35. Drawing insights from 25 publications across three databases, the study identified common themes related to SM's effects on this demographic. The findings reveal a correlation between SM use and mental health outcomes, with benefits includ-ing enhanced social support but also risks such as depression, anxiety, low self-esteem, and increased vulnerability to cyberbullying. These results highlight the urgent need for targeted interventions to address the negative consequences of SM on the mental health and overall well-being of young adults.
