本文提出了一種階層式兼投票式的心律疾病智慧醫療檢測模型，以MIT-BIH心律資料庫為基礎，建立了兩種判別模型。模型一針對正常心跳N及較常發生的V、L、D、R、A(見表3)五種心律疾病進行單一疾病分類；模型二針對疾病較多的N、SVEB、VEB、F、Q(見表4)五類進行分類。採用階層式模型使各層獨立訓練、二分法使資料量均勻；在階層式模型上增加投票式模型，使各層以多種機器學習共同判斷，並按各機器學習訓練之準確程度調整比重。研究結果顯示，模型一最終準確率達99.01%，五種分類類別中有四種召回率達97%以上；模型二整體準確率98.74%，N、VEB、SVEB、F、Q五類召回率分別達99.5%、97.1%、83.6%、77.8%、85.7%。兩模型對於心律疾病判別準確率均較近年論文有所突破。

在這次的研究中，我們在書上看到了一個問題，是一道有關於在棋盤上，貓和老鼠不能看到對方的問題。我們先研究這個題目中棋盤大小、貓和老鼠數量的規律，我們從1×1一路研究到了8×8，並且試著找出在不同棋盤大小的遊戲中，要有幾隻貓才能讓老鼠的平均數量接近2隻，之後我們將 題目設計成對戰的遊戲。 我們首先設計了一個棋盤大小是6×6的桌上型遊戲，並且修改過幾次規則。後來學習了程式設計，把遊戲改到電腦裡遊玩，我們使用scratch寫程式來製作遊戲，並且把原本6×6的棋盤擴大改成了8×8的棋盤。我們在試玩的過程中，又再次把一些不公平的遊戲規則修改了一下，最後我們和同學一起試玩遊戲，製作出了屬於我們的「貓鼠終極戰」。

此研究探討在正角柱及正角錐上一刀斬後分割成二部份而形成截面時，觀察其所形成的截面變化，並利用Geogebra、Desmos等電腦軟體模擬繪製，藉此來計算正角柱及正角錐分割成的截面周長與面積，進而推導出其公式及觀察截面大小之變化，以及其與側稜線長的關係。

培訓一位醫生的時間非常的久，除了在醫學院花上7~8年的時間學習之外，畢業取得住院醫師的身份後，還需要在醫院中選擇特定專業科目受訓，依照科別不同，受訓的時間從3~7年不等，其中神經外科醫生就需要花上7~8年的時間受訓，受訓後再通過專業醫師執照考試，才能正式成為醫師。 醫生的訓練過程費時耗力，在醫療訓練資源有限的情況下，採用虛擬實境(Virtual Reality,VR)科技，可以把手術訓練帶到虛擬世界中，不僅可以提供手術前開刀策略判斷，更可以提高醫療成功率。 本研究主要針對腦部腫瘤手術，藉由運用3D Slicer重建腦部腫瘤3D模型，並轉入到Unity製作手術模擬過程，再轉入到VR，讓醫師可以透過VR虛擬實境，進行手術練習與模擬。

隨著人工智慧和自然語言處理技術的發展，客戶端小型語言模型在各種應用中扮演著日益重要的角色，例如智能手機、物聯網設備和邊緣計算裝置等。這些小型 語言模型需要在有限的計算資源和存儲空間下實現高效的自然語言處理能力。在這種情況下，訓練資料量的大小對於客戶端小型語言模型的性能至關重要。 過去的研究已經表明，大規模的訓練資料對於建立高性能的語言模型至關重要， 但對於客戶端小型語言模型而言，資源的限制使得無法直接應用這些方法。因此，我們需要探討訓練資料量對於客戶端小型語言模型的影響，以找到最佳的平衡點。本研究的結果將有助於指導客戶端小型語言模型的設計和訓練，從而更好地滿足現實世界中的應用需求，同時充分利用有限的計算資源和存儲空間。

In Brazil, approximately sixty million people suffer from or acquire some type of disease daily. However, the average time for blood count diagnoses, used to identify many of these diseases, remains very lengthy. This can lead to the worsening of conditions and delays in care, as well as a decrease in the patients’ quality of life. Moreover, in some cases, the waiting period can result in irreversible situations and even the death of the affected individuals. In this landscape, technological tools such as artificial intelligence software can help reduce the time taken for diagnostic reporting. In light of this, the project involves developing software to assist in the analysis of blood counts and optimize medical diagnoses. For this purpose, the methodology was divided into three stages. In the first, titled ”Medical Standardization”, a survey of the standard variables related to diseases that can be identified with the help of blood counts was conducted. Among the findings, diabetes, anemia, leukemia, dengue, polycythemia, tuberculosis, leprosy, meningitis, chlamydia, schistosomiasis, spotted fever, and malaria were the main diseases detected. Furthermore, hemoglobin, leukocytes, platelets, glucose, cholesterol, ions, and hormones were the key findings concerning the primary blood indicative factors for the mentioned diseases. In the second phase, the theoretical and practical foundations of the software were developed, based on artificial neural networks. In Python, regression models were also crafted to check the feasibility of the analyses. Finally, the last stage consisted of testing with real datasets, based on 1,227 anonymized blood counts. Among the artificial intelligence algorithm models tested, Support Vector (0.02) and Multiple Linear (0.61) had the lowest performances, while Polynomial (0.97), Random Forest (1.0), and Decision Tree (1.0) showed the best results. Given that the Random Forest and Decision Tree regression models achieved an accuracy of 1.0, while the Polynomial model scored 0.97, Support Vector 0.02, and Multiple Linear Regression 0.61, it is concluded that the blood count analysis system, with Python tools like regression, proved to be highly efficient. The closer the R² value is to 1.0, the better the programming fits the model, ensuring accurate analyses. Aside from that, in order to expand the number of analysis possible to do be done we decided to use a second tool called ”classification”, with which we made a bigger dataset to be used as a model to identify blood related diseases and the behavior of complex and diverse diseases. With that in mind, we performed a second evaluation of the models by doing an accuracy test, scored 87 percentage points and with a confusion matrix. With those results, we verified that the high performance of the tests indicates that Artificial Intelligence can be avaunt-guard to the elaboration of more efficient medical diagnosis, improving people’s lives quality and, overall, lowering the number of deaths in our country.

Wibrazz is a wearable communication tool that allows the teacher, the therapist, the parent to communicate information to the child remotely using the device. Haptic (vibrationbased) feedback is becoming increasingly important in everyday life. A vibrating device that transmits information through clothing can help people with disabilities who have no or limited sensory use to live an integrated life in society without barriers.

Road safety remains a global concern, with driver-related factors like distraction, drowsiness, and medical conditions such as stroke being leading causes of accidents. In this paper, we propose a real-time ensemble learning framework that leverages transfer learning for the detection of stroke, drowsiness, and distracted driving. Our model integrates multiple Convolutional Neural Networks (CNNs) fine-tuned for each specific task, and employs a stacking method to combine the predictions of these models using a meta-classifier. Notably, the model is optimized to enhance stroke detection, minimizing false negatives— an essential aspect for timely medical intervention. Experimental evaluations on diverse datasets demonstrate the efficacy of our approach, achieving an overall accuracy of 92.5%. The results emphasize the model’s potential for real-time driver monitoring, offering critical safety features that could reduce accidents and save lives.

現今醫療中，黃疸的早期偵測對肝臟疾病的預防與治療至關重要，但多數人難以在症狀輕微時察覺。我們希望藉由智慧手機影像結合機器學習進行黃疸檢測，提升民眾自我監測的能力。Su 等人（2021）曾使用深度學習和機器學習進行黃疸預測，但其方法依賴專業色卡進行色彩校正，成本高且限制應用範圍。本研究提出以白平衡演算法中的白色補丁法與灰界演算法，搭配深度學習模型 DCCNM1和2 取代色卡，提升黃疸檢測的普及性與便利性。經黃疸偵測效果評估顯示，DCCNM2 在無色卡模型中表現最佳，雖然各指標略低於色卡校正，但其展現出優異的穩定性和準確性，證明其作為無色卡黃疸篩檢方案的可行性。本方法將能提供便捷的居家黃疸檢測途徑，尤其對偏鄉地區居民而言，不僅提升早期發現的機會，還能有效減輕醫護人員的負擔，推動大眾健康管理。

This report presents a novel AI-driven tool for aiding musical composition through the generation of chord progressions. Data acquisition and analysis are discussed, uncovering intriguing patterns in chord progressions across diverse musical genres and periods. We developed a range of deep learning models, from basic recurrent networks to sophisticated Transformer architectures, including conditional and style-based Transformers for improved controllability. Human evaluation indicates that, within the context of our specific data processing methods, the chord sequences generated by the more advanced models are practically indistinguishable from real sequences. The models are then integrated into a userfriendly open-source web application, making advanced music composition tools accessible to a broader audience.

In this fast-paced digital economy, customers' judgment is based on their experience with a company’s products and services. Customer reviews become a vital source of information for companies because this information can be used to enhance their products, understand customer wants and needs, improve brand reputation, and provide a competitor’s advantage. A company can understand customer needs and wants by going through reviews. Customers are encouraged to leave not only their opinion but also their ideas for the development of the product or service. By understanding these reviews, a company can actively respond and engage with a reviewer or problem. Failure of companies who don't answer customer queries may negatively impact customer loyalty. Customers will feel neglected by these companies and will choose competing companies to handle their needs. Additionally, customers may speak negatively about a company that does not respond to reviews. The AI-based customer sentiment dashboard can help gain a company's competitive advantage by identifying weaknesses in themselves and others. Companies will be enabled to understand where they succeed and where improvement is needed compared to their competitors, leveraging businesses to address strengths and weaknesses before competitors do. Through AI-based customer sentiment dashboards, a company can analyze its competitor’s reviews and use that information as leverage to make improvements to its products and services. Customers are increasingly leaving reviews on popular apps like Google Play, Stamped.io, Yapto, and Judge.me, Loox, Qualaroo, and Yelp. The reviews are rich in customer sentiments offering valuable insights into user satisfaction and pointing out the areas for improvement that are crucial to every company no matter how big or small. Despite their value, manually processing these reviews is a challenging task due to the large volume of unstructured data. Manual processing is also vulnerable to bias and human error, leading to inaccurate information. Traditional methods such as surveys have been proven to be ineffective if the main focus is targeted feedback and have low responses compared to reviews. The advances in artificial intelligence like Natural Language Processing (NLP) help us interpret and analyze human language and generate outputs like predicting what type of sentiments are in reviews. This project proposes developing an AI-based sentiment analysis model to evaluate customer feedback on two widely used taxi applications. Natural Language Processing libraries, such as the Valence Aware Dictionary and Sentiment Reasoner (. The model aims to categorize customer reviews into positive, negative, and neutral sentiments.

本研究研發出最適植物萃取液配方：洛神花3滴、黑豆1滴、紫莢長豆2滴、紫色高麗菜2滴與蝶豆花7滴。建構出具穩定性、連續性與高辨識度的天然酸鹼色階圖卡，於pH1～14範圍內產生自然清晰的協同變色反應，顯色漸層分明，辨識度高，成功取代市售指示劑。 探究初期以水萃法提取植物色素，觀察其顏色與稀釋後的色彩變化；接續將各萃取液滴於 pH2～13標準溶液中，運用電腦影像軟體分析RGB色彩參數，量化其變色趨勢。 探究發現，市售指示劑於全pH範圍內的顯色反應缺乏連續性與辨識度。因此更進一步評估植物色素間的協同效應，調整配方比例，提升色階表現整體性。最終優化配方具清晰的變色反應，整體效能優於市售指示劑，展現教學與綠色化學之應用潛力。