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TechComp Innovations: Journal of Computer Science and Technology
Published by Yayasan Mabadi Iqtishad Al Islami
ISSN : 30628903     EISSN : 3062889X     DOI : https://doi.org/10.70063
Core Subject : Science, Engineering,
Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management
TechComp Innovations: Journal of Computer Science and Technology is a premier scholarly publication dedicated to advancing knowledge and understanding in the rapidly evolving field of computer science and technology. The journal serves as a platform for researchers, academics, engineers, and practitioners to disseminate cutting-edge research findings, innovative technologies, and practical applications in various areas of computer science and technology. With a focus on fostering interdisciplinary collaboration and promoting excellence in research and development, TechComp Innovations covers a wide spectrum of topics, including but not limited to artificial intelligence, machine learning, computer vision, cybersecurity, data science, cloud computing, software engineering, and Internet of Things (IoT). Each issue of TechComp Innovations features high-quality, peer-reviewed articles that present original research contributions, theoretical insights, experimental studies, and practical implementations. The journal strives to publish research that pushes the boundaries of knowledge in computer science and technology, addresses significant challenges, and contributes to the advancement of the field. By providing a platform for scholarly discourse and knowledge exchange, TechComp Innovations aims to foster innovation, inspire collaboration, and drive technological progress in both academia and industry. TechComp Innovations welcomes submissions from researchers and practitioners worldwide, offering a forum for the dissemination of new ideas, methodologies, and technologies. Through its publication, the journal aims to facilitate dialogue, stimulate critical thinking, and promote the adoption of cutting-edge solutions to address real-world problems. Ultimately, TechComp Innovations endeavors to be a leading resource for researchers, professionals, and students interested in the latest developments and trends in computer science and technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Interaksi Komputer-Manusia
Articles 5 Documents
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Search results for , issue "Vol. 2 No. 2 (2025): TechComp Innovations: Journal of Computer Science and Technology" : 5 Documents clear
Reconceptualizing Artificial Intelligence Legal Personhood: A Jurisprudential and Comparative Analysis of Emerging Regulatory Frameworks Singhal , Shivani; Sharma, Meenu
TechComp Innovations: Journal of Computer Science and Technology Vol. 2 No. 2 (2025): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v2i2.105

Abstract

This article examines the evolving discourse on granting legal personhood to Artificial Intelligence (AI) by analyzing jurisprudential foundations, global regulatory frameworks, and emerging challenges in liability attribution. As AI systems acquire higher autonomy, opacity, and decision-making independence, traditional human-centered legal structures struggle to assign responsibility for AI-generated harms. Through a qualitative methodological approach, involving library research and content analysis, this study evaluates whether limited or functional legal personhood can serve as a viable solution to accountability gaps created by advanced AI systems. The discussion explores key themes including AI autonomy, black-box decision processes, digital identities in virtual environments, metaverse avatars, and the boundaries of existing tort and contract law. Comparative insights from the European Union, the United States, and India highlight significant divergences in regulatory approaches, particularly regarding “electronic personhood,” strict liability models, and AI-specific safeguards. Findings indicate that while full personhood is premature, a hybrid framework—combining functional personhood, risk-based regulation, and AI-focused accountability mechanisms—could enhance legal clarity, promote responsible innovation, and strengthen public trust. This study contributes to the ongoing global effort to conceptualize AI legal personhood within modern socio-digital ecosystems.
An Accelerated Dynamic Programming Framework Based on Quadrilateral Inequality and Convex Monotonicity Theory Kim, Kwon Jun; So, Ung Bom; Kim, Hyon Chol
TechComp Innovations: Journal of Computer Science and Technology Vol. 2 No. 2 (2025): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v2i2.109

Abstract

Dynamic programming (DP) is a foundational technique for solving multistage optimization problems; however, its classical formulation often incurs a cubic time complexity, rendering it impractical for large-scale real-world applications. This study proposes an accelerated dynamic programming framework that leverages quadrilateral inequality and convex monotonicity theory to significantly reduce computational overhead. By formally establishing the structural conditions under which DP decision boundaries exhibit monotone behavior, the proposed method eliminates redundant state transitions and maintains a sequence of admissible decisions through an efficient stack-based mechanism. The resulting algorithm achieves a worst-case complexity of O(n2) and can be further optimized to O(n) when convex monotonicity holds, representing a substantial improvement over traditional O (n3) techniques. Experimental validation using the classical stone-merging problem demonstrates that the accelerated method executes more than 60 times faster than the standard approach for large inputs, while maintaining identical optimal results. These findings confirm that embedding geometric regularities into DP formulations not only enhances performance but also broadens the applicability of dynamic programming to computation-intensive optimization tasks. This research contributes a mathematically grounded, scalability-oriented solution for accelerating dynamic programming, with implications for logistics, production planning, data segmentation, and other large-scale decision systems.
Mobile Robot Control using Deep Reinforcement Learning and Autoencoder in Dynamic Environment Choe, Ryong Bom; Pak, Mu Rim; Ro, Kang Song; Jo, Kwang Bin; Yun, Ji Yon
TechComp Innovations: Journal of Computer Science and Technology Vol. 2 No. 2 (2025): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v2i2.110

Abstract

In recent years, with the rapid development of artificial intelligence, many innovative changes have been made in the field of intelligent mobile robot development. In the field of control and navigation of mobile robots, learning-based methods have many advantages over traditional ones. The study of mobile robot control methods using deep reinforcement learning is a remarkable area in the development of mobile robots that must operate in dynamic environments. In the previous studies, the proposed robot control algorithms using deep reinforcement learning are mostly based on the given target point and obstacle information, the robot path planning is performed, and the corresponding control is based on the obtained path. The DDPG-based method is a typical example. However, in dynamic environments, DRL based robot path planning requires a state of target point and obstacles information, which leads to a large amount of computation, resulting in extremely long convergence time and even non-convergent cases. In this paper, we propose a new method for mobile robot control in dynamic environment that solves the dimensional problem by extracting the features of the configuration of obstacles using autoencoder and learning the DDPG algorithm based on the obtained features. Simulation results show that the proposed algorithm can effectively solve the mobile robot control problem in dynamic environment.
Federated Intelligence Architectures for Secure, Data-Driven Innovation Across AI, IoT, and Cloud Ecosystems Saidala, Ravi Kumar; Iftikhar, Umna; Hasanov, Tofig; Mammadli, Vüqar Ahmad
TechComp Innovations: Journal of Computer Science and Technology Vol. 2 No. 2 (2025): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v2i2.124

Abstract

This study examines the emerging paradigm of federated intelligence architectures as a secure, privacy-preserving, and scalable foundation for data-driven innovation across AI, IoT, and cloud ecosystems. With billions of interconnected devices generating massive heterogeneous data, traditional centralized machine-learning models face critical limitations, including privacy risks, regulatory constraints, latency, and single points of failure. Through a qualitative content-analysis approach, this paper synthesizes contemporary research on federated learning, blockchain integration, zero-trust governance, and edge intelligence to formulate a comprehensive understanding of distributed AI infrastructures. The findings highlight that federated learning enables collaborative model training without exposing raw data, significantly enhancing privacy, security, and compliance. Moreover, combining blockchain with federated learning strengthens auditability, model integrity, and trust, while zero-trust principles provide continuous verification and adaptive security enforcement across devices. Edge-AI integration further reduces latency and bandwidth consumption, enabling real-time analytics in resource-constrained IoT environments. Collectively, these elements contribute to the formation of cognitive ecosystems capable of autonomous, interoperable, and context-aware operations. The study underscores the transformative potential of federated intelligence while identifying critical gaps that inform future research trajectories.
A Self-Supervised Multi-Scale Fusion Framework for Accurate and Efficient Image Segmentation Yusifova, Elmira Haci; Osmanov, Fuad Fazil; Azizov, Elman; Azizli, Kamran
TechComp Innovations: Journal of Computer Science and Technology Vol. 2 No. 2 (2025): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v2i2.125

Abstract

This study conceptually examines a self-supervised multi-scale fusion framework designed to enhance accuracy and computational efficiency in medical image segmentation, a domain where data scarcity and annotation cost remain major challenges. Traditional supervised approaches are constrained by their reliance on extensive labeled datasets, limiting applicability in real-world clinical environments. Self-supervised learning (SSL) mitigates this issue by extracting supervisory signals directly from unlabeled data, enabling the model to learn rich feature representations without human annotation. Simultaneously, multi-scale fusion architectures integrate global contextual information with fine-grained local features, supporting robust segmentation across varying anatomical structures and image resolutions. Through a qualitative methodology involving library research and content analysis, this study synthesizes state-of-the-art SSL-driven segmentation techniques and highlights how adaptive multi-scale fusion mechanisms address limitations of existing convolutional and transformer-based architectures. The analysis indicates that combining SSL and multi-scale strategies leads to more generalizable, scalable, and computationally efficient segmentation pipelines suitable for diverse medical imaging modalities. The proposed framework represents a promising direction for developing next-generation diagnostic tools capable of handling sparse labels, complex textures, and real-time deployment constraints.

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