With the advancement of the technological field, day by day, people from around the world are having easier access to internet abled devices, and as a result, video data is growing rapidly. The increase of portable devices such as various action cameras, mobile cameras, motion cameras, etc., can also be considered for the faster growth of video data. Data from these multiple sources need more maintenance to process for various usages according to the needs. By considering these enormous amounts of video data, it cannot be navigated fully by the end-users. Throughout recent times, many research works have been done to generate descriptions from the images or visual scene recordings to address the mentioned issue. This description generation, also known as video captioning, is more complex than single image captioning. Various advanced neural networks have been used in various studies to perform video captioning. In this paper, we propose an attention-based Bi-LSTM and sequential LSTM (Att-BiL-SL) encoder-decoder model for describing the video in textual format. The model consists of two-layer attention-based bi-LSTM and one-layer sequential LSTM for video captioning. The model also extracts the universal and native temporal features from the video frames for smooth sentence generation from optical frames. This paper includes the word embedding with a soft attention mechanism and a beam search optimization algorithm to generate qualitative results. It is found that the architecture proposed in this paper performs better than various existing state of the art models.

This chapter examines the critical challenge of balancing usability and security in digital system design. As organizations increasingly rely on digital platforms, the tension between creating user-friendly interfaces and ensuring robust security measures has become more pronounced. By exploring human-centered design principles, behavioral insights, and adaptive technologies, the chapter highlights strategies for integrating security seamlessly into user workflows without sacrificing ease of use. Drawing on interdisciplinary research, case studies, and best practices, the chapter emphasizes the importance of transparency, user trust, and iterative design. It concludes by proposing a framework for developing systems that not only protect privacy but also enhance user experience, offering a roadmap for sustainable and secure digital transformations.

This chapter explores the amalgamation of artificial intelligence (AI) and semiconductor technologies in smart buildings to achieve urban energy sustainability. Utilizing AI for intelligent energy management, adaptive automation, and seamless integration with renewable energy sources, smart buildings markedly improve energy efficiency and diminish carbon emissions. The significance of improved semiconductor components in facilitating these advancements is highlighted. The chapter discusses the problems and future perspectives for the implementation of smart building technologies, emphasizing the need for multidisciplinary cooperation across stakeholders to attain net-zero carbon cities.

Teacher performance evaluation is an essential task in the field of education. In recent years, aspect-based sentiment analysis (ABSA) has emerged as a promising technique for evaluating teaching performance by providing a more nuanced analysis of student evaluations. This article presents a novel approach for creating a large-scale dataset for ABSA of teacher performance evaluation. The dataset was constructed by collecting student feedback from American International University-Bangladesh and then labeled by undergraduate-level students into three sentiment classes: positive, negative, and neutral. The dataset was carefully cleaned and preprocessed to ensure data quality and consistency. The final dataset contains over 2,000,000 student feedback instances related to teacher performance, making it one of the largest datasets for ABSA of teacher performance evaluation. This dataset can be used to develop and evaluate ABSA models for teacher performance evaluation, ultimately leading to better feedback and improvement for educators. The results of this study demonstrate the usefulness and effectiveness of ABSA in evaluating teacher performance and highlight the importance of creating high-quality datasets for this task.

This chapter explores the critical role of advanced cryptographic technologies in securing smart city applications across domains such as e-health, e-commerce, and e-banking. As urban environments become increasingly digitized, safeguarding sensitive data, ensuring transaction integrity, and protecting user privacy emerge as paramount challenges. The chapter explores a spectrum of cryptographic solutions, including blockchain, homomorphic encryption, zero-knowledge proofs, and quantum cryptography, that address these complexities. Through detailed analysis and real-world case studies, it highlights practical implementations and emerging trends that reinforce trust and resilience in smart city infrastructures. Moreover, the chapter discusses the policy and ethical dimensions necessary to balance security with usability and transparency. Finally, it provides a comprehensive framework for integrating cryptographic safeguards into the evolving digital fabric of smart cities, ensuring secure, scalable, and citizen-centric urban services.