Abstract : Future 6G physical layers need to work together to improve peak power, spectrum confinement, reliability, energy, and latency when channels are not stationary and service slices are not the same. Traditional approaches for reducing PAPR and shaping spectra, such as PTS/SLM, μ-law companding, and fixed orthogonal precoding, work to some extent but are not very reliable across different types of waveforms (UFMC, OTFS) and power-domain multiplexing (NOMA). To suggest a single, AI-driven signal-shaping architecture that combines a hybrid orthogonal precoding bank (DFT/DST/SRC/ZC) with a probabilistic deep compander (PDC) and selective/clustered companding (SCC). A learning controller, such as Deep Reinforcement Learning (PPO) or a Cultural-History Optimization Algorithm (CHOA), changes the precoding choice, companding profile, cluster aggressiveness, NOMA split, besides delay–Doppler grid (for OTFS) to minimize a multi-objective loss over PAPR, out-of-band (OOB) emissions, EVM/BER, energy per bit, and latency. During exploration, safety shields make sure that power-amplifier headroom, spectrum masks, and latency budgets are followed. Synthetic but reasonable results show that improvements are consistent across waveforms. When CCDF is 10⁻³, PAPR goes down from 11.8 dB (OFDM) besides 10.6 dB (UFMC) to 6.9 dB (Proposed-UFMC) and 6.5 dB (Proposed-OTFS). The power outside of the band (OOB) goes up to −44.5 dBm. For 16-QAM, BER at 10 dB drops to 9.8×10⁻³ (Proposed-UFMC) and 7.5×10⁻³ (Proposed-OTFS). The framework has good energy-latency trade-offs: 0.62 mJ/bit at 1.3 ms (URLLC slice) and 0.45 mJ/bit at 3.8 ms (Green slice). Ablations show how each module works: taking off AI controller lowers PAPR to 8.3 dB and OOB to −39.1 dBm. These results indicate that suggested stack is a feasible 6G-level solution that integrates waveform diversity adaptive, safety-conscious optimization..
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Abstract : The fast growth in the scale of large-scale data systems in sectors like cybersecurity, finance, healthcare, and industrial surveillance has enhanced the necessity of powerful anomaly detecting methods that can operate without indicated data. This paper explores the use of unsupervised machine learning to detect anomalies in high-dimensional and large-scale unhomogeneous data. Four exemplary algorithms, namely: Isolation Forest, One-Class Support Vector Machine (OC-SVM), Local Outlier Factor (LOF) and Autoencoder neural networks were evaluated systematically on several large-scale datasets with network traffic, transactional and sensor records. The experimental findings proved that deep learning-based Autoencoders had a top overall detection performance with an average precision of 93.6, recall of 90.8, F1-score of 92.2, and AUC of 0.96, which showed they were most effective in identifying non-linear patterns involving complex physiological behavior. The Isolation Forest was also particularly well performing obtaining an F1-score of 89.8% with much lower training time (71 seconds on 500k records) and much lower detection latency (1.8 ms per instance), which was similar to real time applications. Conversely, LOF displayed lower scalability and performance breakdown in a high-dimensional environment as well as OC-SVM. The proposed framework is competitive or better with other related work of recent interest and, therefore, can be compared with other comparable studies. On the whole, this study can contribute to practical understanding of the choice of unsupervised techniques of anomaly detection regarding the scale of a system, the nature of collected data, and the limitations of its operation..
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Abstract : We construct the triple difference sequence spaces Γ^3 (p∇^3 q)=(Γ^3 )_(p∇^3 q), where p=(θ_uvw^mnk ) is an infinite three-dimensional matrix of Padovan numbers θ_mnk defined by (θ_uvw^mnk )={■(θ_mnk/(θ_(u+5,v+5,w+5)-2),&m≤u,n≤v,k≤w,@0,&m>u,n>v,k>w)┤ and ∇_q^3 is a q-difference operator of third order. We obtain some inclusion relations, topological properties, Schauder basis and α-,β- and γ- duals of the newly defined space. We examine some geometric properties. We introduce and study some basic properties of rough I_λ-statistical convergent of weight g(A),where g:N^3→[0,∞) is a function satisfying g(m,n,k)→∞ and g(m,n,k)↛0 as m,n,k→∞ of triple sequence of Padovan q-difference matrix, where A represent the RH-regular matrix and also prove the Korovkin approximation theorem by using the notion of weighted A-statistical convergence of weight g(A) limits of a triple sequence of Padovan q-difference matrix and also have proved Korovkin approximation theorem by using the notion of weight g(A) limits of a triple sequence space of Padovan q-difference matrix. All the results will certainly motivate the young researchers..
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