Authors - Meixin Hu, Chuanchen BI Abstract - Speech synthesis is an important tool for improving human-computer interac tion, accessibility, and other multimedia applications. Traditional Text-to-Speech (TTS) systems have issues related to robotic tone, slow inference and lack of expressiveness. This current study presented a realization of the effectiveness of the neural TTS system using Fast Speech 2 as the underlying neural TTS sys tem. The system used in the current study was a combination of Fast Speech 2 as the underlying neural system in generating high-quality utterances and HiFi-GAN as the underlying neural vocoder. The process involves reconstructing natural-sounding text utterances in terms of mel-spectrograms by Fast Speech 2 that incorporate the use of variance adaptation in terms of pitch, duration, and energy. The implementation of natural-sounding utterances in terms of mel spectrograms is done in real-time using HiFi-GAN. The implementation of the available studies provided insights into Fast Speech 2’s effectiveness in generating mel-spectrograms in real-time and faster. The use of HiFi-GAN provided insights in generating natural-sounding utterances in real-time. The effectiveness of Fast Speech 2 in generating high-quality utterances has further stretched the poten tial use of Fast Speech 2 in virtual assistant applications, audiobooks, accessible text services, further highlighting its significance in advanced human–computer interaction systems.
Authors - Cheng Cheng, Chuanchen BI Abstract - In recent years, there has been an increase in AI - generated images. This poses a major challenge in distinguishing fabricated images from real ones. This distinction is valuable for discovering misinformation and preserving digital trust. Some deep learning models, particularly large Convolutional Neu ral Networks (CNNs), have demonstrated high accuracy on benchmark datasets like CIFAKE, but their computational requirements often in clude specialised hardware like powerful Graphics Processing Units (GPUs), which ultimately limit practical deployment. This paper explores an alternative approach that focuses on efficiency and interpretability. The CIFAKE dataset is used, but a significantly lighter CNN architecture, ResNet18 is deployed which does not require high end local GPU hardware. Furthermore, the paper applied Gradient - weighted Class Activation Mapping (Grad - CAM) not just for visu alization, but also to validate that the model learns meaningful visual features that are relevant to the classification task. This work highlights a practical method to interpret AI - generated images.
Authors - Jiayan Peng, Chuanchen Bi Abstract - With the continued growth of digital education (and multiple platforms providing education/courses), students have many things to deal with in terms of finding useful content (e.g., Lecture videos; audio files; PDF's; slides, etc) and as a result, it may be difficult to efficiently scan and gather all of this information. AutoNoteX is a tool that automatically creates notes from your spoken word using speech-to-text technology (e.g. Whisper), Natural Language Processing, and various AI agents. AutoNoteX will provide accurate transcriptions, along with structured summaries that highlight key points and provide diagrams when appropriate in order to create good, clear notes for students. AutoNoteX can support collaborative and independent learning by allowing the user to merge their notes with Google Docs or download them as PDF's. AutoNoteX also includes interactive knowledge checks that have multiple levels of difficulty (easy, medium, difficult) when answering questions and also provide a means for the student to receive instant feedback on their progress. AutoNoteX was developed using React.js for the front end and Python Flask for the backend, and is cloud-enabled (scalable; accessible via many devices; and easy to integrate into a variety of subjects) giving students the tools they need to create better notes. Overall, AutoNoteX provides a new avenue for multi-modal, AI-assisted, and personalized digital note-taking, while reducing the amount of time needed to make notes and improving student comprehension by encouraging students to participate in their learning process actively.
Authors - Qixuan Geng, Chuanchen BI Abstract - Efficient nutrient management is vital in a sugarcane cultivation to sustain the crop yields. But, the conventional practices are still reactive and imprecise often leading to improper nutrient management and yield loss. To overcome this issue, the study utilizes a multimodal AI driven framework by integrating drone-based canopy imaging and in-field soil sensors to aid in real-time nutrient deficiency detection and precise recommendation of fertilizers. UAV images are analysed using a transfer learning based Convolutional Neural Network (CNN) to locate visible deficiency symptoms and determine its severity. In order to forecast impending nutrient deficiencies, significant soil parameters (NPK, moisture, pH, electrical conductivity and temperature) are monitored continuously and processed using GRU/ LSTM- based models. The data and information from sensor networks, images and environmental context are then integrated through a fusion architecture to produce a nutrient deficiency label, severity score, and confidence measure. To ensure interpretability and agronomic safety, predictions are incorporated with crop growth stage- specific nutrient gap model that convert deficiencies into dosages of fertilizers, with alerts given on high-risk conditions and optionally permissioned fertigation control. The proposed system allows proactive, data-driven nutrient management, mitigates the risk of over fertilization, and supports scalable precision agriculture.
Authors - Md. Riaz Mahmud, Kazi Asif Ahmed, Md. Rafiqul Islam, Kabya Guha Abstract - Modeling multi-scale spatial dependencies is essential in histopathology image analysis, where diagnostically relevant patterns span cellular textures and tissue-level structures. While convolutional neural networks effectively capture local features, they struggle to model long-range interactions, and transformer-based approaches address this limitation at the cost of quadratic computational complexity with respect to spatial resolution. In this work, we propose HiSS-Fuse, a linear-time hierarchical state-space fusion framework that integrates multi-scale fea ture representations using Mamba-based selective state-space modules. The proposed architecture progressively fuses local and global contex tual information across network depths while maintaining O(L) com putational complexity, where L denotes the number of spatial tokens. Experimental evaluation on the PathMNIST benchmark demonstrates that HiSS-Fuse achieves 97.0% classification accuracy with an AUC of 0.997 while maintaining strong computational efficiency. Ablation stud ies further confirm that hierarchical fusion systematically enhances rep resentation learning. Overall, HiSS-Fuse provides a scalable and compu tationally efficient alternative to quadratic attention-based architectures for multi-scale histopathology image analysis.
Authors - Cheng Cheng, Chuanchen BI Abstract - The increasing reliance on Information and Communication Technology (ICT)-driven intelligent systems has transformed organizational decision-making processes, enabling more efficient, data-driven, and adaptive strategies. These systems, which encompass artificial intelligence, machine learning, and decision support tools, have revolutionized how businesses process and analyze vast amounts of data to inform strategic decisions (Cheng et al., 2017; Yoo & Lee, 2020). This paper presents a strategic framework for integrating ICT-driven intelligent systems into organizational decision-making, addressing key challenges such as technological compatibility, organizational resistance, and alignment with strategic goals (Patel & Sharma, 2019; López et al., 2019). The main objective of this study is to develop a comprehensive and practical framework that organizations can adopt for successfully integrating intelligent systems into their decision-making processes. The research aims to bridge the gap between existing theoretical models and practical applications by proposing a step-by-step process that involves assessing organizational readiness, selecting appropriate systems, ensuring seamless integration, and fostering continuous improvement (Ahmad et al., 2021; Pereira et al., 2021). The methodology employed includes qualitative case studies from diverse industries, supplemented with a review of relevant literature and theoretical models such as the Technology-Organization-Environment (TOE) framework (Tor-natzky & Fleischer, 1990) and the Resource-Based View (Barney, 1991). The findings suggest that successful ICT integration is contingent upon a well-planned, strategic approach that aligns technological capabilities with organizational goals and promotes an adaptive organizational culture (Brinkman & Möller, 2018). The implications of this study are far-reaching, offering valuable insights for managers and policymakers to overcome integration barriers and optimize decision-making using intelligent systems (Hossain & Kaur, 2021). This research contributes to the growing body of knowledge on ICT integration in decision-making, offering both theoretical advancements and practical guidelines for successful implementation.
Authors - Tajamul Islam, Ruby Chanda Abstract - The present study explores the online privacy concerns of young Indian consumers. Using the segmentation approach popularized by Dr Alan Wes-tin in the U.S., this study identifies the segments within Indian youth. This study is based on a survey conducted on a sample of Indian university students. Hierarchical and non-hierarchical cluster analysis techniques were applied to identify segments within young Indian consumers based on their privacy concerns. The study identified three consumer segments: highly concerned, moderately concerned, and less concerned based on online privacy concerns. The findings also reveal important differences among the three segments in terms of out-come variables such as perceived effectiveness of legal/regulatory policy, fabricating personal information, and software usage for protection. The results indicate an overall increased level of concern for online privacy among young Indian consumers. The results suggest similarities and dissimilarities with Westin’s approach. While previous research on online privacy has been chiefly based on the Western context, this study offers a window to look at the Eastern context by examining the privacy concerns of young Indian consumers, who have not been studied, and hence provides an important contribution to the existing literature.
Authors - Meixin Hu, Chuanchen BI Abstract - Secret-sharing schemes are fundamental cryptographic primitives en- abling secure distribution of sensitive information among multiple parties. Orig- inally introduced to protect cryptographic keys, they have evolved into power- ful tools underpinning modern secure multiparty computation, distributed stor- age, blockchain systems, and privacy-preserving machine learning. This review presents a systematic overview of threshold secret-sharing schemes, ramp con- structions, and secret-sharing schemes for arbitrary access structures. We discuss information-theoretic foundations, lower bounds, structural generalizations, and recent advances. Furthermore, we highlight emerging applications in distributed computing, post-quantum cryptography, and secure AI systems.
Authors - Ying Tang, Chuanchen BI Abstract - This article presents a comprehensive analysis of methods and recent research in the sentiment analysis of Uzbek-language social media posts. A balanced corpus of 100,000 posts from Telegram, Instagram, Twitter, and Facebook was constructed as the object of study, in which positive, neutral, and negative classes are equally represented. The data were subjected to thorough preprocessing steps including cleaning, normalization, tokenization, removal of stop words, stemming, and lemmatization. The evaluated models include Naive Bayes, Support Vector Machines (SVM), Conditional Random Fields (CRF), Long Short-Term Memory networks (LSTM), and transformer-based architectures such as BERT and RoBERTa. The accuracy, F1-score, and runtime performance of each model were compared. Experimental results indicate that transformer-based models achieved the highest accuracy (~92%), followed by LSTM (~90%) and SVM (~88%). Despite being a simple method, Naive Bayes served as a baseline (~78% accuracy). The literature review highlights prior research conducted in Uzbek sentiment analysis, emphasizing the importance of corpus creation and accounting for language-specific features. The results indicate that transformer models provide the highest accuracy, whereas classical methods remain competitive even in low-resource settings. The article concludes with a discussion of promising directions and potential practical applications in the field of Uzbek-language sentiment analysis.
Authors - Lankalapalli Vamsi Krishna, Santanu Mandal Abstract - The rapid advancement of generative and agentic artificial intelligence (AI) is significantly transforming research in operations management and supply chain systems. Despite the substantial increase in scholarly output in recent years, the structural evolution and thematic consolidation of this interdisciplinary field remain insufficiently mapped. This study presents a bibliometric analysis of 116 Scopus-indexed articles published between 2015 and 2025 to examine publication trends, knowledge concentration, intellectual structure, and longitudinal thematic transitions. Utilizing the Bibliometrix R package, the analysis employs performance metrics, Bradford’s Law, keyword co-occurrence mapping, thematic centrality–density analysis, and temporal evolution modeling. The results indicate accelerating research growth and increasing consolidation within core engineering-oriented journals. Intellectual clustering reveals strong integration between computational modeling, reinforcement learning, and supply chain decision systems. Thematic mapping identifies computational methods and autonomous agents as central themes, while generative AI emerges as a developing yet increasingly interconnected trajectory. Longitudinal analysis reveals a clear shift from agent-based simulation frameworks toward adaptive, autonomous, and AI-integrated operational ecosystems. The findings suggest that generative and agentic AI are becoming foundational elements of next-generation operational intelligence systems. This study provides structured insights into the maturation of AI-enabled operational research and offers guidance for future interdisciplinary investigations in autonomous supply chain intelligence.
