Adroid Conference Series: Engineering and Technology
Adroid Conference Series: Engineering and Technology is a conference series focused on sustainable engineering, aiming to share the latest insights and expertise in this diverse field. It is open-access and undergoes peer review, targeting researchers, academic professionals, and postgraduate students. The conference imposes a strict double-blind peer-review process and upholds the ethical standards set by the Committee on Publication Ethics (COPE) to maintain publication quality. For quote submission -> Request For Quotation Year: 2025 Frequency of the publication: Annual Publication format: Online Subject: Engineering Language/s: English Highlights of the Adroid Conference Series: - Faster Publication - ⁠Conference Series Indexed (Indexing) - ⁠DOI Assignment to each paper - ⁠Peer-Reviewed Open Access Publication - ⁠Post Publication, the Proceedings will be Submitted to Scopus, WoS for possible Indexing - ⁠Opportunity to publish TOP Quality papers in Adroid Journals
Adroid Connectz Private Limited (Adroid Publishing)
The development of quantum technology has advanced rapidly to become a critical transformative device for use in medical diagnosis and treatment, surpassing the boundaries of normal methods. By applying principles such as superposition, complexity, and quantum tunnelling in QDs, it is now possible to produce highly sensitive equipment that can diagnose diseases at their earliest stages. Quantum sensors enable tracking physical characteristics with nuclear accuracy, helping detect molecular alterations before symptoms appear. Quantum computing increases diagnostic capabilities by rapidly processing large therapeutic datasets, enabling the recognition of various patterns and gene sequences and the immediate suggestion of personalised treatments. Combining QD with IoT and AI-based healthcare systems creates an ecosystem that enables continuous monitoring, future-state analysis, and instant decision-making. This kind of cooperation is expected to yield good results in the case of neurodegenerative and infectious diseases. However, implementing QD in medical diagnostics faces several significant obstacles, including high growth costs, scalability issues, technical immaturity, and regulatory gaps. Nevertheless, further work is going on despite these limitations. The current chapter will focus on the technical basis, present uses, limitations, and future of quantum technology in diagnosis.
Copyright (c) 2026 Adroid Conference Series: Engineering and Technology
Human Perception and Virtual Reality: What Is the Connection?
Perception was how the brain and nervous system functioned to process sensory information from the environment. One observation that has been verified is required for a well-constructed interaction. As immersive technology advances from research-driven prototypes to mature consumer and business platforms, the ability to design VR experiences that can integrate with the human perceptual system (including designing for, evolving with, and potentially augmenting those systems) is emerging as a key area of study. Machine learning (ML) is emerging as a key connecting point between these fields and has the potential to impact real-time adaptive rendering, improved visual fidelity, biometric-based customisation, and the prediction of perceptual issues such as cybersickness. This paper contributes to a growing multi-disciplinary discussion on the relationship between human perception and VR, specifically, and how ML approaches - including DL, RL, CV, and AC (Affective Computing) - are changing immersive experience design. Based on the principles of the latest neuroscience and cognitive psychology and computer science and engineering literature, we succinctly describe how human beings perceive, what the expectations of an ideal VR system are, the perceptual challenges, dichotomies, and paradoxes associated with immersive environments and discuss the machine learning revolution in futuristic solving of such quandaries.
Behavioral Biases in Investment Decision-Making
Investment decisions are not always logical or rational. When making financial decisions in the real world, people are guided by their emotions and personal history, and they take mental shortcuts that don’t always lead to the right decisions. These are influences known as behavioural biases. This review article considers the most relevant biases impacting investor decision-making. It discusses ideas from behavioural finance, a discipline that integrates psychology and economics to explain why people sometimes make bad decisions in financial markets. The paper also describes how these biases affect stock markets, individual investors, and portfolio managers. It draws on theories such as the Prospect Theory of Kahneman and Tversky, as well as several empirical studies from both advanced and emerging markets. The paper concludes by suggesting possible mitigating actions for the biases.
Machine Learning in Gas and Air Quality Sensors: A Comprehensive Review
Air pollution is now considered one of the greatest public health threats. Detecting pollution is unfortunately often too slow, too costly or not precise enough, but millions of people suffer every year due to bad air quality. This paper reviews the application of machine learning (ML) techniques in the enhancement of gas sensors and air quality monitoring systems. We describe which types of sensors are most frequently employed, what the key challenges they are facing are, and how these challenges are addressed with different ML methods. We also review real-world case studies, present comparisons of different ML approaches and discuss the future of this field. The paper is simple enough for anyone to understand the interplay between machine learning and sensors — and how that interaction makes a bigger difference when it comes to breathing in cleaner air.
AI-Driven Optimization in Supply Chain Operations: A Machine Learning Framework for Demand Forecasting and Inventory Management
Supply chain operations are among the most intricate sectors of modern business: their characteristics are high-dimensional data, time-variant demand dynamics, and cascading failures. In this work, a novel Hybrid Machine Learning framework comprising Long Short-Term Memory (LSTM) networks and an XGBoost ensemble is proposed to accurately forecast demand and optimise inventory replenishment. The framework is applied to a real retail supply chain dataset spanning 36 months and 500+ SKUs. The framework outperforms the benchmark by 34% (MAPE from 9.6% to 6.3%), with 28% fewer stockouts and 19% less excess inventory. The LSTM module consumes a 28-day sliding window of historical demand, using a 2-layer stacked architecture (128 and 64 units) to output a 64-dimensional temporal embedding that captures the long-range dependencies of the demand sequence. The embedding is concatenated with 42 engineered features that capture temporal indicators, lag and rolling features, promotion, supply-side data, and external features, producing a 106-dimensional feature vector for the XGBoost ensemble predictor. A downstream inventory replenishment module dynamically calculates safety stock, reorder point, and order-up-to level based on forecast error and substitutes static classical inventory policies with adaptive, data-driven strategies. The method is evaluated on an actual FMCG retail supply chain dataset, including 536 SKUs over 36 months and roughly 7.2M transaction records. HybridSCF reports a 6.32% MAPE for a 7-day forecast horizon, a 34% increase over ARIMA, reduces stockout occurrences by 27.4%, decreases the excess inventory ratio by 18.4%, and reduces the total annual inventory holding cost from $42.8M to $35.6M (16.8% reduction). Ablation studies demonstrate the effectiveness and individual contributions of the LSTM encoder and the dynamic safety stock module. SHAP-based interpretability is implemented to enable easy, accountable deployment of the automated replenishment solution in the real world.
Impact of AI on Employment and Workforce Transformation
The rapid deployment of AI technologies into the global economy is one of the most dramatic changes in the workforce in modern times. This paper provides an extensive examination of this phenomenon, drawing on data on its influence on labour markets across industries, including the decline in traditional employment and the emergence of new occupations driven by technological innovations. Based on empirical data and case studies from the most developed countries in the world, we evaluate the process's rate, its effects on workers with different skill levels, and the measures that should be taken to manage it. According to our estimates, by 2025, AI may displace approximately 8 million people worldwide while creating 9.5 million jobs. Whether the process has a negative effect depends entirely on the responses of education, businesses, and labour policies to the changes AI brings.
Technological Unemployment, & Job Displacement
Opportunities and Challenges of AI in Education
The emergence of AI is one of the recent trends that has affected several spheres of education. The use of artificial intelligence can change the way teaching and learning take place, as well as the way assessments and educational processes are managed. It looks like personalised systems and tutorships are among those that tend to be talked about. Besides, automated evaluation and predictive analytics are among the possible applications of AI. It may increase efficiency, but it is likely to become less accessible to all students. In my view, there are many opportunities available, but it is not clear whether they depend on a particular implementation. Some regard it as revolutionary, while others pay less attention to it. However, there are challenges to using AI in education. The first obvious issue is ethical considerations. Privacy concerns should be addressed due to the vast amounts of data required. Bias in algorithm design may lead to discrimination against certain social groups. There are inequalities in access among students. The use of AI in learning may decrease face-to-face interaction. I am not sure if these issues are directly connected, but they are definitely worth mentioning. The reviewed paper explores both opportunities and challenges in the application of AI in education. This paper reviews the recent literature, considers applications, examines the advantages and disadvantages, and provides recommendations for responsible implementation. It appears that artificial intelligence can be of great assistance in enhancing efficiency and effectiveness, but its proper use requires consideration of ethical, technological, and sociological issues, as well as learning methods. This section gets pretty complicated when you consider all these factors together. Some concepts appear to be more related to each other than shown here.
Quantum AI for Drug Discovery and Material Science
Materials science has historically depended on a blend of experimental methods and theoretical modelling to identify and create new materials with specific properties. Nevertheless, these processes may require considerable time and resources, and are frequently constrained by the intricacy of material systems. The rise of artificial intelligence (AI), particularly machine learning, has revolutionised materials science by offering powerful tools that accelerate the discovery, design, and characterisation of novel materials. This chapter emphasises the latest developments in AI applications in materials science for drug discovery. Artificial Intelligence is proficient at analysing intricate data, enhancing processes, and developing drug candidates, whereas quantum systems enable unparalleled molecular simulations, highly sensitive sensing, and accurate physical control. Applications in drug discovery are emphasised, encompassing molecular property prediction and molecular generation. This chapter focuses on technologies such as Nanomaterials, Biomaterials, Polymers, Metal-Organic Frameworks (MOFs), Hydrogels, and Smart Materials. This chapter emphasises the advantages of quantum technology in drug discovery: enhanced accuracy in molecular simulations, Accelerated drug screening, better comprehension of reactions, and Tailored medicine. Additionally, the challenges include: hardware limitations, the high cost of error correction, the maturity of algorithms, integration with classical methods, and issues related to cost and accessibility.
AI and the Treatment of Severe Mental Illnesses
In addition, Artificial Intelligence (AI) has revolutionised approaches used in the management of mental diseases that put one's life at risk, such as schizophrenia, bipolar disorder, and major depressive disorder. Challenges associated with conventional techniques include a lack of symptom identification, subjectivity in assessing patient status, and limited access to specialised healthcare services. In that regard, AI has developed advanced predictive analytics, machine learning (ML), and natural language processing (NLP) to facilitate the deployment of SMIs by leveraging pattern recognition of behaviour, speech, and physiological parameters. AI Chatbots and virtual psychologists can monitor a person's mental wellness throughout the day without necessarily involving healthcare specialists. In addition, wearable devices worn by patients fitted with AI produce real-time signals, including heart rate variations and sleep patterns, which help pace the respondent's mental state. Recommendation engines based on artificial intelligence provide personalised plans for pharmaceutical and therapeutic interventions. With the above innovations in the hood, however, ethical concerns related to AI, such as data privacy, biases, and AI over-extrapolation, remain a concern. It is crucial to ensure transparency and regulation, as well as a human-AI partnership, for AI to thrive in mental healthcare services. This chapter explores how AI talks, listens, and helps people living with SMIs, examining the nature and limitations of AI in transforming the diagnosis and treatment of SMIs.
AI-DRIVEN FAULT DETECTION AND DIAGNOSTICS
Artificial intelligence (AI) has changed the game when it comes to fault detection and diagnosis (FDD) because with AI, identifying and solving faults in machines, systems, and networks becomes easy and effective. Manual techniques have traditionally been used to perform these tasks; however, they are tedious and costly. Utilizing the concept of FDD using AI involves a lot of machine learning (ML), deep learning (DL), and data analysis techniques in order to detect and predict possible faults. The sensors continuously, in real time, parse enormous amounts of data, and automated AI readily identifies equipment errors, especially in manufacturing, healthcare, energy, automotive, cybersecurity, and other industries, where error prevention averts failure, enhances safety, and reduces expenses. AI-based systems help organisations optimise machine performance while reducing downtime. AI has the potential to reduce faults through the use of smart algorithms and Internet of Things (IoT) devices. It is important to note that concerns remain regarding data quality, high costs, and the interpretability of AI decisions. With the advancement in AI. The technology, reliability, and usability of AI are improving over time, and fault detection and diagnostics are becoming faster and more effective. This chapter provides a comprehensive review of the applications of artificial intelligence for fault detection and diagnosis, with a critical analysis of the advantages, challenges, and future scope of AI in this domain for improved system performance.
The Future of Artificial Intelligence in Healthcare
In this article, we describe the potential future impact of AI-augmented healthcare systems, present new developments in harnessing AI's power in healthcare, and outline a roadmap for building safe and reliable AI systems that are likely to succeed. AI stands for artificial intelligence, and it is the simulation of human cognitive processes by a computer or machine. This revolution is being driven by the increasing availability of healthcare data and improvements in analytical methods. AI's entry into the healthcare sector has opened a new era and radically transformed the sector as never before. This work explores the deep impacts that AI is having on healthcare systems and what the future will look like. AI is changing patient care, increasing operational effectiveness, and encouraging creative solutions, ranging from tailored treatment plans to sophisticated diagnostics. From personalised treatment plans to advanced diagnostics, AI is transforming patient care, improving operational efficiency, and driving innovation. This study offers a holistic view of the new shape-shifter AI and the future of medicine through the prism of our understanding of technology and human well-being.
Foundation of Self-Learning AI for Drones
Full autonomy and the end of manual drone operation require a powerful self-training AI spine. Drones piloted with Static Commands would break in unpredictable environments (e.g., windy weather, moving obstacles). Classical self-learning systems establish these principles with strict rules; instead, a drone acquires its abilities through on-policy experience. The drone is a digital pupil that learns from experience. It’s mathematically rewarded for getting somewhere and penalised for mistakes, like moving too close to a wall. With thousands of rounds of training under its belt, the AI learns an intricate picture of how to handle flight physics that could not have been hand-coded by a person. Rather than relaying the information to a remote server, the drone performs image processing on the cameras and sensors themselves in real time, with the help of powerful internal chips. These offline computations are complemented by online “Sim-to-Real” training, where the AI is first taught to fly in a safe virtual environment before transfer to physical hardware. That means the drone is already an ace at flying before it ever took off. Also, the foundation must enable lifelong learning. The drone can now adapt to changing environments or mechanical ageing over time, without forgetting what it was taught initially. Developing drones on such intelligent platforms makes them reliable partners for challenging missions, including emergency response and autonomous delivery. This innovation ensures the sky’s future will be safe, fluid, and self-determining.
Comparing Supervised and Unsupervised Learning Approaches for Specific Tasks
Nearly all prior knowledge in machine learning can be categorised as either supervised learning (SL), which utilises labelled datasets for training, or unsupervised learning (UL), which infers latent structure in unlabelled data. Although both paradigms are well studied in isolation, they are rarely systematically compared across task domains. In this paper, we provide a differentiable comparison of SL and UL across six prototypical tasks—image classification, fraud detection, natural language sentiment analysis, customer segmentation, anomaly detection, and product recommendations—using twelve benchmark datasets. We consider accuracy, F1-score, silhouette coefficient, training time, memory usage, and label dependency. Our experiments show that SL outperforms UL on precision–critical tasks with sufficient labels (mean accuracy increase: +14.3%), while UL outperforms SL on exploratory and scalability–critical tasks by up to 38% according to cluster quality metrics. Additionally, we develop a decision framework to guide practitioners in choosing the right paradigm for given task constraints. All artefacts from the experiments—code, hyperparameter logs, and datasets—are publicly available.
The Challenges of AI Implementation in Business
US dominance in AI was simply assumed for the US to have a place in the 21st-century economy, and thus, it must spearhead this transformative technology. Yet, for all the hype, the road to using AI is a bumpy one. In this article, we cover the science, data stewardship, business, ethical duty, regulatory compliance, change management, and financial obligations surrounding the complex challenge for organisations keen to bring AI-based solutions to market. Based on a systematic review of best-practice literature, emerging industry case studies, and empirical research, the results provide a conceptual framework to overcome barriers and strategically guide the uptake of sustainable, responsible, and value-based AI in business.
Future Directions in Digital Social Linguistics
The chapter examined the future direction of digital social linguistics, a new branch that has emerged within sociolinguistics, communication studies and digital media studies. With the continued evolution of digital technologies, such as platforms, artificial intelligence, and immersive technologies, that transform the communicative practices of human beings in all their complexity and variety, communication research is presented with new opportunities and methodological challenges. Six topical issues are reviewed in this chapter: (1) artificial intelligence and computational approaches to language; (2) performative identity construction in new media; (3) applied sociolinguistics and virtual/augmented reality; (4) global and multi-lingual phenomena; (5) linguistic norms and algorithmic mediation; and (6) ethics and digital linguistic research. For digital social linguistics, this chapter explores contemporary theoretical frameworks and empirical research to argue for interdisciplinary collaborative work, ethical reflexivity, and imaginative methodology to capture the scale, speed, and variety of digitally mediated communication, as it can productively function in the 21st century.
Supply Network Disruption Prediction using Heterogeneous Graph Neural Networks and Temporal Embeddings
Recent post-pandemic disruption has revealed shortcomings in current supply chain paradigms, which are built on segregated, compartmentalised information systems that are neither real-time nor support real-time decisions. The typical Supply Chain Management (SCM) paradigms have several weaknesses, such as inaccurate demand forecasts, lack of visibility into the multi-tier supply networks, lack of inventory synchronisation, and high vulnerability to geopolitical/natural disasters/cyber-attacks, resulting in annual losses to the world economy at an estimated $184 billion per year. This, compounded by a number of other factors, leads to a fundamental failure of current paradigms and gives rise to an immediate, industry-wide requirement for an intelligent, tech-enabled SCM paradigm that can make decisions intelligently, self-optimise, and adapt at scale. This paper introduces a comprehensive end to end computer science driven CS-SCOF (Cognitive Supply Chain Optimization Framework) which integrates the four modern computational techniques as follows; (i) Transformer based sequence models along with Graph Neural Networks (GNNs) for multi-horizon demand forecasting across inter-dependent product categories, (ii) Reinforcement learning agents for dynamic safety stock calibration and inventory optimization for stochastic demand variability, (iii) Graph Neural Networks (GNNs) for multi modal logistics route optimization using real time traffic, weather conditions and live fuel price information and (iv) A Carded Hyperledger Fabric to enable immutable, tamper-proof and end to end supply chain visibility and traceability across multi tiers of suppliers. CS-SCOF has been rigorously tested on five real-world datasets, namely the M5 Walmart Forecasting dataset (42.8M records), the UCI Supply Chain dataset, and the GS1 Trade Transactions dataset. The experimental results have clearly demonstrated that CS-SCOF can reduce the demand forecast MAPE by 14.3% to 5.6%, stock out rate by 38% and the average delivery latency by 22%, framework accuracy of 94.7% outperforming the best baseline by 8.4 percentage points and 1250 TPS on the sharded blockchain against 440TPS on standard Ethereum implementation at maximum security settings.
Survey on Internet of Things (IOT) Forensics: Challenges, Approaches, Privacy-Aware Frameworks and Open Issues
The pervasive growth of IoT devices in vital fields such as health, finance, transit, and smart cities has transformed the domain of digital forensics. Although IoT devices generate rich streams of evidence data, forensic investigators encounter new challenges due to the heterogeneity of devices, the volatility of data, cross-border legal issues, and end-to-end encryption. This paper presents a structured and holistic review of the state-of-the art in IoT Forensics by describing the taxonomy of IoT devices, a careful study of forensic issues in device layer, network layer, and cloud layer, and a comparative analysis of six investigation methods such as AI/ML based forensics, blockchain based evidence management, privacy preserving framework, Forensics-as-a-Service (FaaS) [1][,[2],[3]. The performance of these six methods, based on detection accuracy and analysis time, is evaluated through experiments, showing that AI/ML-based forensics can achieve a detection rate of 87.4% with a minimum analysis time of 95 minutes. The privacy-aware methods achieve the best formal assurance with the differential privacy approach [4],[5],[6]. The privacy-utility trade-off in the health, smart home, and generic IoT domains is investigated, and best practices for implementation in these domains are recommended. Finally, open research questions such as standardisation issues, Byzantine reliable aggregation, and proactive forensics are discussed.
Federated Learning with Differential Privacy: A Comprehensive Framework for Privacy-Preserving Distributed Machine Learning
Overview: Federated Learning (FL) is a type of machine learning in which multiple clients use their own data to train a model. This does not require sending the raw data from each client to a central server. However, FL is currently very vulnerable to several types of attacks, including inference, model inversion, and membership inference. In this study, we have implemented a comprehensive experimental framework for analysing FL performance using standard FL aggregation protocols FedAvg, FedProx, and SCAFFOLD in conjunction with Differential Privacy (DP) mechanisms; specifically, the Gaussian noise mechanism with Rényi Differential Privacy (RDP) accountants. Our experiments all used K = 100 simulated clients, with heterogeneous data distribution across the entire federation (non-IID), and the respective clean datasets used were MNIST and CIFAR-10. For example, in our IID scenarios, our DP-FedAvg protocol achieved an accuracy of 84.9% on MNIST with ε = 1.0 and δ = 10^-5 (i.e., no privacy guarantees and 92.6% reuse of the same parameters). This corresponds to a 7.7-percentage-point trade-off in accuracy for provable privacy. In our non-IID scenario, we achieved an accuracy of 72.8% for the exact same privacy parameters and an LDA αᴸᴸᴸ = 0.5. We systematically analyse how various combinations of clipping norms on gradients, noise multipliers, and client participation rates converged. All results have been reported as-is, along with the associated standard deviation across each of the five independent sample runs that we performed using different random seed values. Finally, our framework will assist practitioners deploying privacy-friendly FL within either healthcare, banking, or Internet of Things environments.
Comparative Analysis of Machine Learning and Large Language Model Architectures for Intelligent Medical Diagnosis and Clinical Decision Support Systems
Artificial Intelligence (AI) and Machine Learning (ML) have dramatically changed the methods involved with medical diagnostics and decision-making in the clinic, as well as the use of informatics within healthcare. In this paper, we present a rigorous comparison of six widely used and popular machine learning and artificial intelligence architectures in the creation of a clinical decision support/predictive analytic model: Random Forest, Support Vector Machine (SVM), Multilayer Perceptron (MLP), Gradient Boosting, BERT-based Transformer and GPT-4 fine-tuned model [1],[2],[3]. Evaluation of each model was conducted using a large-scale clinical dataset of 500,000 patients from multiple tertiary care hospitals throughout India, and was based on accuracy, precision, recall, F1-score, AUC-ROC, computational scalability and clinical interpretability. Our results indicate that the GPT-4 fine-tuned model achieved state-of-the-art classification accuracy of 97.2%, surpassing all other models tested. Additionally, we demonstrate a novel hybrid ensemble architecture (MediConnect-AI) that uses gradient-boosting classifiers with transformer-based contextual embeddings to achieve 98.1% classification accuracy with significantly reduced inference latency [4],[5],[6]. Using ablation studies, feature significance analysis, and extensive statistical significance testing, we demonstrate that our new framework is superior to all tested models. Collectively, these results will have a significant impact on the future deployment of AI systems in real-world clinical settings.
Impact of Digital Financial Platforms on ESG Investment Decisions
The convergence between digital financial platforms and environmental, social, and governance (ESG) investing has reshaped capital allocation worldwide. This review draws on empirical research, market data and early case evidence to assess the impact of FinTech platforms - the mobile investment app, AI-driven robo-advisor, blockchain-based reporting infrastructure and big-data analytics ecosystem - on investor behaviour, firm ESG performance and market transparency. Major results show that digital finance has a positive and significant effect on corporate ESG performance, that the combination of robo-advisor adoption and ESG-portfolio customisation has the largest positive effect on investor engagement, and that blockchain-based disclosure systems can mitigate greenwashing on a mass scale. The worldwide ESG investing market is expected to reach more than USD 127 trillion by 2034, up from around USD 29.77 trillion in 2024, growing at a CAGR of 17.26% over the forecast period. Policy, data quality, and research gaps are identified and discussed.
Effect of the Moral Trading Model on the Permanent Business Development of the Tata Group with AI integration
The research thesis examines the ties within the Tata group through an intensive case study of moral trade practices and sustainable corporate development, which is one of the oldest and most respected groups in India. The study shows how the Tata group integrates the moral trade model, including the adoption of Artificial Intelligence (AI) to achieve long-term success. By analysing the group's historical development, corporate values, governance structures, and commercial results from 2020 to 2024, this study identifies the major mechanisms through which moral practices, including AI-powered innovations, translate into sustainable development. The conclusions suggest that moral trade models enhanced by AI create value for stakeholders by reducing operational risk, enhancing talent attraction and retention, driving innovation, and generating social impact. The TATA case indicates that morality, profitability and AI-operated stability are not competing, but interestingly strengthening strategic elements. Major metrics include consistent revenue growth (7.3% CAGR), improved ESG score (88/100 in 2024), reduced carbon emissions (-30% after 2020), and AI-SAC capacity in operations and supply chains. The study provides a roadmap for companies prioritising stability by exposing the synergy between competitive advantage, moral governance, and AI integration, exemplified by AI-operated solutions such as Tata's code of conduct, CSR initiative, and smart manufacturing and permanent mobility.
Linguistic barriers remain a major challenge in communication, education, and cultural preservation, particularly for ancient and low-resource languages. While modern neural machine translation systems perform efficiently for widely used languages, they lack sufficient support for historical scripts such as Brahmi and classical languages due to limited datasets. This paper presents HiEn: Speak the Switch, a multimodal multilingual conversion system that integrates text, image, and voice-based translation within a unified platform. The system combines Optical Character Recognition, speech recognition, text-to-speech synthesis, and machine translation APIs within a Python-based graphical interface. Additionally, a custom character-mapping module enables deterministic conversion of the Brahmi script into modern equivalents. The system demonstrates reliable multilingual translation and satisfactory OCR performance. It enhances accessibility, simplifies translation workflows, and contributes to the digital preservation of ancient scripts. The architecture is scalable and can be extended using advanced transformer-based models in future implementations.
A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR LEGAL TEXT CLASSIFICATION
This paper provides a comparative analysis of machine learning algorithms for text classification in law. The swelling of legal documents and the growing case backlog have led to a demand for automated systems to aid efficient legal analysis. In this article, text data is first preprocessed and converted to numerical features using TF-IDF. Naive Bayes, Support Vector Machine, and Logistic Regression are three machine learning algorithms that are applied and tested on a real-life legal dataset. Evaluation metrics for such models include accuracy, precision, recall, and F1-score. The experimental results indicate that the Support Vector Machine model achieves the highest accuracy among the considered approaches. The results not only confirm that machine learning methods can be applied to large-scale legal text classification but also serve as a reference point for improving the methods in the future through more advanced Naive models.
AI-Based Intelligent Document Translator And Audio Reader For Telugu Language
Accurate machine translation for low-resource languages remains a significant challenge, particularly for technical documents where consistent terminology is essential. This work presents a dictionary-guided post-editing framework for English-to-Telugu document translation. The proposed system operates as a lightweight post-processing layer applied after translation and does not require modifications to the underlying translation model or additional training data. Terminology consistency is achieved through semantic matching with a curated Telugu glossary, ensuring uniform translation of domain-specific terms across the entire document. The system is evaluated using standard translation quality metrics and demonstrates improved performance compared to existing large-scale translation systems. The proposed method achieves a score of 9.23 on a widely used evaluation metric and 42.00 on a character-based evaluation measure, showing consistent improvement in both lexical and morphological accuracy. In addition, a speech synthesis module is integrated to convert translated text into audio form, enhancing accessibility for users. The results indicate that the proposed framework provides a practical and scalable solution for improving translation quality in low-resource language settings, particularly for technical and domain-specific content.
Ecogeoguard: A Dual Approach to Environmental Safety and Crop Yield
A community-focused AI-IoT technology, EcoGeoGuard combines livestock safety tracking, precision agriculture analytics, and multi-sensor geophysical monitoring into a single real-time intelligence ecosystem. In addition to GPS-enabled livestock trackers, the system uses inexpensive environmental sensors, such as MEMS accelerometers, geophones, tilt sensors, soil-moisture probes, and BME280 weather modules, all connected via a hybrid LoRa/NB-IoT/Wi-Fi communication architecture. Before being fed into a cloud-based analytics pipeline via AWS IoT Core, data is processed at the edge using FFT-based vibration analysis and threshold filtering. Machine learning algorithms, such as Random Forests, Gradient Boosting, Linear Regression, and K-Means clustering, are used to analyse time-series streams to identify early warning signs of slope instability, optimise the use of irrigation and fertilisers, and detect cow movements outside designated safety zones. The anomaly management layer calculates risk ratings and activates multi-channel alerts via dashboards, SMS, mobile applications, and local siren units. EcoGeoGuard is a holistic early-warning and decision-support platform to safeguard livestock and population in disaster-prone regions, enhance productive rural agriculture, and enhance climate resilience. It includes network resilience and energy-efficient, solar-powered nodes.
An Applied Security Model For Controlled Key-Release In Blowfish-Based Data Retrieval
From a cybersecurity standpoint, the security of the data stored in these systems has become a crucial concern as cloud storage continues to proliferate in digital infrastructure. Credential compromise, brute-force attacks, and sophisticated, persistent infiltration tactics have demonstrated the vulnerability of traditional security techniques, especially those that rely on static encryption keys or password-based authentication. This study offers a multi-layered security approach that combines multi-stage authentication with a dynamic key-release method for Blowfish-encrypted file storage, in response to constantly evolving cyber threats. Before allowing the controlled recovery of the Blowfish decryption key, the model described here used sequential verification with user credentials, a time-based one-time password, and a registered device token validation procedure. By keeping encryption keys unreadable until identity is fully confirmed, the system architecture reduces the exposure risks associated with conventional key storage methods. FastAPI, Streamlit, PostgreSQL, and MinIO were used in the implementation; the configuration was simple but deployable in both academic and business environments. According to experimental evaluation, the framework significantly increases resistance to unauthorised access attempts while maintaining low latency for both authentication and decryption. The findings make it clear that the suggested architecture provides a safe and effective way to protect files in cloud environments, balancing computational efficiency and user accessibility while ensuring robust cryptographic security.
Lunar Sentinel: Computational Analysis of Lunar Surface Instabilities from Orbital Remote Sensing Data
Safe lunar landing requires quantitative assessment of the terrain of hazards such as steep slopes, shadowed regions and high crater density. This study presents an integrated multicriteria hazard modelling framework for lunar landing site evaluation using Chandrayaan-2 Terrain Mapping Camera-2 (TMC-2) imagery and corresponding Digital Elevation Model (DEM) data, combined with deep learning-based crater detection from LROC Narrow Angle Camera imagery. The terrain slope was derived from DEM gradients and was normalised using a min– max approach, while hillshade modelling was employed to identify persistent low-illumination zones. Crater instances were detected using a custom-trained YOLOv8 model trained and validated on 8521 annotated image samples, achieving a mean average precision (mAP@0.75) of 0.89 and an optimal F1 score of 0.87 at a confidence threshold of 0.45. The extracted hazard layers (slope, illumination mask and crater density surface) were integrated through a weighted multi-parameter fusion scheme to generate a continuous Landing Safety Index (LSI). The resulting LSI index classifies terrain into safe, moderate and high-risk zones, highlighting the steep craters and shadowed depressions as hazardous to rover landing. The given system shows the results of combining DEM-based terrain with a deep learning technique. This is used to identify the lunar hazard surface and also provides a structured methodology for future autonomous missions. The existing solution analysed crater detection, slope analysis and Illumination conditions separately. Whereas our approach combines all three components within a unified index, fulfilling the gaps in lunar hazard analysis.
Adaptive Travel Itinerary Planning with AI Destinations and Weather Aware Adjustments
The proposed system is implemented as a Python-based Streamlit application that integrates multiple functional modules, including user authentication, weather data retrieval, destination selection, and itinerary generation. The system uses JSON-based local storage to manage user credentials, feedback records, and country metadata. Real-time weather data is fetched using an external weather API module, which processes and returns temperature and condition details for selected locations. The application dynamically generates travel itineraries based on user inputs, including destination, duration, and preferences, while incorporating weather conditions into its planning logic. The architecture follows a modular design consisting of input handling, API interaction, data processing, and output rendering components. The implementation demonstrates effective integration of API-driven data retrieval, session-based state management, and structured data handling using Python. The system highlights practical considerations, such as plaintext credential storage, dependency management, and basic feedback logging, while providing a functional prototype for adaptive travel planning.
AI-Powered Predictive Analytics for Market Trend Forecasting: Integrating Deep Learning, NLP, and Ensemble Models for Enhanced Financial Insights
Market forecasting has undergone a significant transformation with the rise of Artificial Intelligence (AI), leading to greater precision and faster insights than traditional statistical techniques. This study examines how AI technologies—such as deep learning, natural language processing, and ensemble modelling can be integrated to more effectively predict market dynamics. It highlights the critical role of varied data sources, including financial metrics, news sentiment, and social media trends, in building robust forecasting frameworks. By evaluating AI-driven prediction models alongside conventional methods and analysing key performance indicators, the research offers an in-depth understanding of the opportunities and challenges of AI in financial forecasting. The findings also emphasise the expanding role of AI-powered decision-support systems across sectors such as finance, supply chain management, and marketing.
AyuRAG: A Retrieval-Augmented Generation Framework for Ayurvedic Knowledge Access
Ayurveda, India’s traditional system of medicine, preserves centuries of clinical knowledge in Sanskrit texts such as Charaka Samhita, Sushruta Samhita, and Ashtanga Hridaya. Although parts of this literature are now accessible thanks to recent digitization efforts, current systems primarily rely on keyword-based search, which restricts the contextual accuracy and semantic depth of clinical queries. This gap could be filled by advances in retrieval-augmented generation (RAG) and large language models (LLMs), enabling multilingual, interpretable, and context-aware access to Ayurvedic knowledge. This paper proposes AyuRAG, a lightweight, domain-adapted LLM framework that integrates RAG with carefully selected Ayurvedic corpora and modern biomedical knowledge bases. AyurRAG guarantees accuracy in retrieval and reasoning, addresses the unique linguistic challenges of Sanskrit, and aligns Ayurvedic disease concepts with contemporary medical ontologies. By using parameter-efficient fine-tuning to achieve scalability for deployment in resource-constrained settings, AyurRAG supports the evidence-based integration of Ayurveda into modern healthcare practices.