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42 results for “Data Encoding”
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Research ArticleOpen accessPhrase in the titleCoupling Routing Algorithm and Data Encoding for Low Power Networks on ChipMaurizio Palesi, Davide Patti, Giuseppe Ascia et al.The routing algorithm used in a Network-on-Chip (NoC) has a strong impact on both the functional and non functional indices of the overall system. Traditionally, routing algorithms have been designed considering performance and cost as the main objectives. In this study we focus on two important non functional metrics, namely, power dissipation and energy consumption. We propose a selection policy … …Journal of Computer Science, Vol. 11, Issue 3 · 4 May 2015 · 6,928 accessesView PDFCite
Research ArticleOpen accessIn the keywordsHuffman Based Code Generation Algorithms: Data Compression PerspectivesAhsan Habib, M. Jahirul Islam, M. Shahidur RahmanThis article proposes two dynamic Huffman based code generation algorithms, namely Octanary and Hexanary algorithm, for data compression. Faster encoding and decoding process is very important in data compression area. We propose tribit-based (Octanary) and quadbit-based (Hexanary) algorithm and compare the performance with the existing widely used single bit (Binary) and recently introduced dibit (Quaternary) algorithms. The decoding algorithms for … …Journal of Computer Science, Vol. 14, Issue 12 · 9 Dec 2018 · 7,701 accessesView PDFCite
Research ArticleOpen accessIn the abstractSmart and Secure Anomaly Detection in IoT Using Quantum Inspired Kernel Ensemble LearningAbrar M. AlajlanThe exponential growth of the Internet of Things networks has greatly increased the attack surface for cyber threats, necessitating the use of strong and perceptive anomaly detection systems. Traditional intrusion detection systems often struggle with high false alarm rates, limited generalization, and inefficiency in processing high-dimensional heterogeneous IoT data. To address this issue, a novel Hybrid Quantum-Enhanced Kernel Ensemble Learning … …Journal of Computer Science, Vol. 22, Issue 3 · 12 Mar 2026 · 1,762 accessesView PDFCite
Research ArticleOpen accessIn the abstractFedCarepod: Federated Quantum Transfer Learning Approach in Screening Process for Early Detection of Liver AbnormalitiesK MalaThe liver is the most frequently injured intra-abdominal organ that leads to complications. Visual interpretation depends on the expertise, and this justifies the need for automatic accelerated assistive prediction system. Classical and Quantum machine learning approaches are combined to leverage their benefits, and the feasibility of designing a novel system is explored. This approach successfully ends up with health care … …Journal of Computer Science, Vol. 21, Issue 10 · 16 Dec 2025 · 2,068 accessesView PDFCite
Research ArticleOpen accessIn the abstractIntegrated ARIMA and Multi-Scale GRU for Crop Recommendation and Yield Prediction in Precision AgricultureVani Vijay Kumar Ballapuram, Gurupraksh Chirathahalli DyamannaAgriculture is the fundamental source of food, income, and livelihood for rural communities in India. Numerous crops are affected due to the lack of technical decision-making support and variations in weather patterns, temperature, rainfall, and atmosphere factors, which play a critical role in defining crop yield. Hence, choosing the appropriate crop to maximize yield is key to enhancing real-time farming … …Journal of Computer Science, Vol. 21, Issue 10 · 26 Nov 2025 · 2,022 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Intrusion Detection System Using Optimized Deep Learning for IoT NetworksChempavathy B., Mouleeswaran S. K.In the rapidly evolving landscape of Internet of Things (IoT) networks, assuring security is paramount. Cyber-attacks on IoT networks are evolving in complexity and scale. Intruders employ diverse tactics, including malware, Denial of Service (DoS) attacks, along with unauthorized access. This research aims to propose an optimized Deep Learning (DL) based Intrusion Detection System (IDS) to bolster security within IoT … …Journal of Computer Science, Vol. 21, Issue 8 · 9 Oct 2025 · 2,993 accessesView PDFCite
Research ArticleOpen accessIn the abstractRandom Forest-Based Genome Analysis for Disease Association and SNP Marker IdentificationXiangdong LiuAt present, genomic data analysis has the problems of insufficient model interpretation and limited computational efficiency in disease association research. As a powerful machine learning algorithm, Random Forest has demonstrated excellent ability in processing complex and multi-dimensional data sets and has gradually become a popular tool in the field of bioinformatics. The purpose of this paper is to explore how … …American Journal of Biochemistry and Biotechnology, Vol. 21, Issue 2 · 25 Jul 2025 · 5,533 accessesView PDFCite
Research ArticleOpen accessIn the abstractImplementation of Single Candidate Loss Optimization Algorithm for Loss Optimization of Bhojpuri-English Machine Translation ModelRituraj Dixit, Sarabjeet Singh Bedi, Ibrahim Aljubayri, Mohammad Zubair KhanMachine translation of low-resource Indian languages is necessary as most of the regions still know and speak their specific dialects and are still not comfortable understanding the English language. Indian languages are morphologically rich, due to which there are two big challenges, Ambiguity and Domain adaption, which are faced by researchers during the translation. Lack of data also increases the … …Journal of Computer Science, Vol. 21, Issue 5 · 17 Apr 2025 · 5,151 accessesView PDFCite
Research ArticleOpen accessIn the abstractComputational Analysis of Genre Effects on Movie Ratings Using MLP AlgorithmsSubir Gupta, Upasana Adhikari, Shefali Varshney, Tanupriya ChoudhuryPredictive analytics are what the entertainment industry depends on so much in prognosticating movie ratings, which is why they inform filmmakers, distributors and stream platforms strategic moves. Traditional prediction models most times only succeed in missing out on the intricate dynamics that are genre-specific to movie ratings thereby leading to inaccuracies and suboptimal decision making. The paper proposes this research … …Journal of Computer Science, Vol. 21, Issue 4 · 19 Mar 2025 · 5,534 accessesView PDFCite
Research ArticleOpen accessIn the abstractDynamic Knowledge Driven Multi-Scale Spatiotemporal Fusion Framework for Reliable Multi-Horizon Traffic Flow ForecastingDeepika M S, P. Deepa Shenoy, Venugopal K RAccurate traffic flow forecasting remains a core challenge in intelligent transportation systems. Urban traffic patterns are characterised by complex spatiotemporal dependencies and inherent uncertainties, which complicate traffic forecasting. This study proposes a Dynamic Knowledge-Driven Spatiotemporal Fusion (DKDMSF) architecture to extract features of Multi-Scale traffic dynamics across various temporal traffic horizons. DKDMSF integrates Dynamic Spatial Attention (DSA) and Adaptive Graph Learning … …Journal of Computer Science, Vol. 22, Issue 9 · 30 Sep 2026 · 413 accessesView PDFCite