Authors - Hasan Ahmed, Ram Singh Abstract - The growth of digital media platforms has resulted in more disseminated falsehoods which now include elaborate AI-generated syn thetic text instead of manually created false information. The develop ments create major obstacles which disrupt both information trustwor thiness and public confidence. The research presents a High-Accuracy Misinformation Detection Hybrid Transformer Framework which uses BERT and RoBERTa models within an ensemble learning system. The system undergoes initial training on WELFake dataset which serves as a standard benchmark collection that contains equal proportions of au thentic and fraudulent news articles derived from both verified and un verified sources. The framework achieves adaptability through its in cremental updating process which incorporates contemporary headlines and machine-generated content. The weighted fusion mechanism merges probability results from both transformer models to decrease model spe cific bias while strengthening the system’s classification ability. The sys tem shows better results than single transformer setups and operates through a web-based system which provides immediate misinformation assessment. The study results show that using ensemble modeling to gether with scheduled model updates creates an efficient method for tackling the ongoing emergence of synthetic misinformation.
Authors - Gagani Kulathilaka, Inuka Gajanayake, Guhanathan Poravi, Saadh Jawwadh Abstract - In modern digital environments, organizations require intelligent sys tems to manage complex workflows and decision-making. Unlike most of the task management systems that are manual and give no feedback and even lack competence; this leads to poor prioritization, deadline been missed and poor com munication between teams. Thus, IntelliTask is an intelligent system of dealing with tasks, which is AI-powered and, consequently, is context-aware, giving it an edge to enhance the quality of the working processes of the people using the system (both individuals and businesses), enhancing the prioritization, and im proving the productivity. The IntelliTask platform is machine-learning models, predictive analytics, and dynamic scheduling based on identifying key tasks to balance the workloads and the cognitive load on users without the user having to engage in the task. The solution will enhance the rate at which the tasks are ac complished, making informed decisions and will bring flexibility on what task management systems will be established in the future in enterprises.
Authors - Umar Ali R, Payas Khan H, Nouriensha N, Nithish Kumar S, Nisha M Abstract - An effort to calculate the infinite value of circumference ratio is made in this paper. Instead of being made of countless infinitesimals, a given circle is parts of an new defined infinity that is single magnitude continuum derived from the change in direction that indicates that there is a jumping from finiteness to infinity .This single magnitude continuum is the accumulations of infinitely many finite magnitudes and can never be achieved by forever extending continuously finite magnitudes.The change in direction implies that infinite length (i.e. infinite distance) can be defined as two parallel lines that never intersect ,which denotes that only the terminal end of the first straight line is meaningful when extending towards infinite distance, and this terminal end is defined as infinite length, which is a magnitude that cannot be discussed any magnitudes outside of it. When the first straight line extends to infinite distance, its one-dimensional feature will be lost and become an infinite dimensional magnitude, which is determined by the change in direction.The infinite value of circumference ratio is this new defined infinity.
Authors - Sarah Rahim, Guhanathan Poravi Abstract - In mobile networks without fixed base stations (MANETs), finding the best path for data is difficult when devices are constantly moving. Traditional methods often lead to dropped data and wasted battery. This study introduces a smarter approach by combining the standard routing protocol with a "Dolphin Partner Optimization" (DPO) algorithm. Much like how dolphins coordinate, this system picks the best path by looking at battery life, connection stability, and speed all at once. Testing shows this new method keeps the network running longer and sends data much more reliably than older systems.
Authors - Md. Mehedi Rahman Rana, Md. Anisur Rahman, Kamrul Hasan Talukder, Syed Md. Galib Abstract - The adoption of AI in the law sphere on a larger scale has left new opportunities of case analysis and verdict prediction as well as legal texts interpretation with the help of the robot. However, the existing Legal Judgment Prediction (LJP) systems are submissible to implicit data bias, which contains adult information on such delicate aspects as gender, caste, occupation, and socio-economic status. These biases may result in ethically unsound and unreliable forecasting, which is a vital issue in high stakes judicial settings. This work provides a Bias-Aware Legal Case Classification and Judgment Interpretation architecture that enables improved levels of fairness, interpretability and contextual reliability in legal decision support systems. The bias-sensitive preprocessing pipeline proposed combines the Named Entity Recognition and zero-shot and legal-specific bias-tagging. These two types of vocabularies are used with a dual-encoder framework based on LegalBERT on bias-masked data and BERT on unmasked data in order to trade-off legal reasoning with controlled demographic awareness. Representations in a gating-based fusion mechanism are combined in advance to make final classification. The system is set to work on the real case documents of the Indian laws based on the publicly available repositories. Instead of substituting the jurisdictional powers, the framework is intended to deliver ethical, transparent, and contextually sensitive support to the legal practitioners. The research is relevant in the history of responsible AI, as it focuses on the issues of fairness and interpretability in the field of automated legal analytics.
Authors - Leonardo Juan Ramirez Lopez, Cristian Santiago Cruz Jimenez, Johan Sebastian Ayala Gaitan Abstract - Ongoing technological progress has significantly increased global energy demand, particularly in rapidly developing economies, a trend further intensified by continuous population growth. Although improving energy efficiency is a universal objective, it remains an unresolved challenge. Advances in science and engineering have enabled the creation of diverse energy-harvesting technologies that utilize established non-conventional sources— such as solar, wind, thermal, hydro, piezoelectric, electromagnetic, and bio-battery systems—as well as emerging concepts like rectenna-based collection. This study aims to present a comprehensive evaluation and comparison of these technologies by examining their energy sources, availability, conversion principles, infrastructure needs, production costs, performance outputs, application domains, overall efficiency, harvesting capacity, constraints, resource characteristics, and commercial feasibility. By offering a systematic comparison, the authors seek to clarify the strengths of each approach while also highlighting the practical challenges involved in applying them to meet present and future global energy demands through both existing and prospective alternative energy solutions. The main objective of this paper is to systematically evaluate and compare a wide range of energy harvesting technologies—spanning established non-conventional sources and emerging concepts—by analyzing their operating principles, resource availability, infrastructure requirements, cost, efficiency, performance, limitations, and practical applicability, with the aim of identifying their strengths, challenges, and potential contributions toward meeting current and future global energy demands through sustainable alternative solutions.
Authors - Asmit U. Patil, Sneha Jadhav Mane, Swati Suryawanshi, Prerana Mahajan, Priya Sharma, Smita Shedbale, Dhanaraj S. Jadhav, Supriya Mane Abstract - Inference latency remains a critical bottleneck in deploying large language models, for real-time and resource-constrained environments. Prior work has proposed latency formulations that express latency as a function of key parameters. However, they often assume a linear dependence on sequence length, which fails to generalize to tasks involving significantly longer sequences, such as document-level language modeling, long-context retrieval, or time-series forecasting, where latency scales nonlinearly and unpredictably. This paper addresses the limitations of existing latency formulations by proposing three complementary enhancements to improve generalization across varying sequence lengths. First, we introduce a nonlinear term for sequence length, capturing the superlinear growth in latency observed in transformer-based architectures due to quadratic attention mechanisms and memory overhead. Second, we propose a sequence-length-dependent scaling factor for the sequence length parameter itself, allowing the model to adaptively adjust its sensitivity based on empirical latency profiles across different tasks and hardware configurations. Third, we incorporate an empirical correction term enabling calibration of the latency model to account for hardware-specific and implementation-level nuances. By explicitly modeling the nonlinear and context-sensitive behavior of sequence length, our approach offers a more faithful representation of latency dynamics. This work lays the foundation for more adaptive and hardware-aware latency estimation frameworks, with implications for model deployment, scheduling, and cost optimization in production systems. We conclude by discussing future directions for integrating dynamic profiling and reinforcement learning to further refine latency predictions in evolving runtime environments.
Authors - Felipe M. Coelho, Margarida N. P. dos Santos, Jeziel M. Pessoa, William A. P. de Melo, Joel C. do Nascimento, Carlos A. O. de Freitas , Debora R. Raimundo, Vandermi J. da Silva Abstract - The transition from 4G to 5G networks, particularly in Non Standalone (NSA) deployments, introduces new challenges for the energy effi ciency of mobile devices, as they must maintain simultaneous connectivity with LTE for signaling while using 5G NR for high-speed data transmission. To ad dress this issue, this work proposes a hybrid artificial intelligence approach for predicting current consumption that combines conventional deep learning with neuromorphic computing principles. Real-world telemetry data are first pro cessed using convolutional layers and bidirectional LSTM units to capture spa tial and temporal patterns, and the resulting representations are then converted through rate coding and provided to a Spiking Neural Network (SNN). The model is trained using a hybrid strategy that integrates Spike-Timing Dependent Plasticity (STDP) with surrogate gradients, together with a custom loss function designed to emphasize prediction accuracy during high-demand periods. Experimental results show that the proposed model achieves an RMSE of 0.1164 mA, representing a 6.3% improvement compared to standard Recur rent Spiking Neural Network (RSNN) approaches, indicating its ability to cap ture abrupt variations in power consumption typical of 5G NSA environments.
Authors - Udayamoorthy Venkateshkumar Abstract - This paper focus on dual axis solar panel tracking system using Brushless Direct Current motor (BLDC), in which rotor position estimation along azimuthal angle and elevation angle is predicted using incremental en coder. The physical kinematics and dynamics parameters which are non-linear in nature is converted to linear form and processed in conventional estimated kalman filter (EKF) algorithm. The physical process noise covariance value Qk and measured noise covariance value Rk is estimated from conventional EKF predicted value, using sliding window method. Smoothing factor λ is used for quick convergence and tuning factor to estimate the process noise covariance. The simulation is performed using Python and results shows rotor position es timation along azimuthal angle is improved by 50% and 55% along elevation angle. Dual axis estimation error convergence during dynamic tracking along azimuthal angle is reduced by 66% and along elevation angle is reduced by 70% when compared to conventional EKF algorithm.
Authors - Ananya Kale, Aditi Jaikar, Shravika Hamjade, Neeta Maitre, Rashmi Apte, Mangesh Bedekar Abstract - Singer identification is a challenging task because of pitch and me lodic variations, tempo, vibrato, and adaptive singing styles. This paper propos es a novel approach towards singer identification and classification by adapting a model originally meant for speaker recognition. Specifically, this work utiliz es vector representations extracted from a pretrained Speech Brain Emphasized Channel Attention, Propagation and Aggregation in Time Delay Neural Net work (ECAPA-TDNN) model. The research pipeline processes a custom curated dataset of four prominent Indian playback singers into fixed, 8 second audio clips, with mono channel sampled at 16 kHz and exported as wav files. The Speech Brain Emphasized Channel Attention, Propagation and Aggrega tion (ECAPA) encoder transforms these labelled clips into fixed embeddings which are unique vector representations of voice characteristics of each audio clips. A suite of classical machine learning classifiers is trained on these em beddings. The study evaluates four of them namely, Logistic Regression, Sup port Vector Machines, Random Forests, and a Multi-Layer Perceptron (MLP). The MLP achieved the highest accuracy of 99.38% on held-out test data. Sup porting this result, both confusion matrix analysis and t-SNE projection clearly demonstrate clear cluster separation based on individual singer identities. These findings thus collectively validate that ECAPA embeddings contain sufficient identity-bearing structure on a singing voice. This analysis thus concludes that adaptation of speaker recognition models with appropriate classifiers is a great ly effective and efficient approach for singer identification.
