Volume 13 | Issue 6 |

 Abstract

Emergency dispatch systems are important to provide timely and effective response to serious situations. Conventional call evaluation techniques are based on human assessment, which is subjective and unreliable. Voice stress analysis (VSA) has been found to be a potential technique for analyzing the stress level of callers and enhancing decision-making in emergency dispatch centers. This study delves into the incorporation of AI-based voice stress analysis in emergency call systems using machine learning and speech processing algorithms to identify stress patterns in real time. Through examination of fluctuations in pitch, tone, and speech rhythm, the system is able to evaluate the level of urgency and emotional state of the caller and assist dispatchers in prioritizing responses more effectively. Experimental testing with real and simulated emergency call datasets proves VSA's success in enhancing response times, decreasing misclassification of emergency severity, and increasing situational awareness. The results prove the potential for AI-driven voice stress analysis in maximizing emergency response strategies while responding to accuracy, privacy, and ethical concerns challenges.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Voice Stress Analysis, Emergency Dispatch, AI-Driven Speech Processing, Machine Learning, Stress Detection, Real-Time Decision-Making, Emergency Call Classification, Speech Emotion Recognition, Deep Learning, Public Safety

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Bio medical waste (BMW) causes many harmful effects to human being and environment if not disposed properly for this Government of India introduced the BMW rules in 1998 which was amended in 2011. It was introduced for safe handling of BMW and to protect public health and Environment. This paper will throw light on growth of BMW in India during 2013- 2023.The study is based on secondary sources of data which is already available on internet, government websites, and previous published journals. The study reveals that the highest production of BMW is between 2020 to 2021 during Covid-19 pandemic. As hospitals were full of patients so it is natural that the BMW will be generated more during the year 2020 to 2021. The number of healthcare facilities (HCF) increased more than two times in the period of 2013-2023 due to increase in labs and clinics in our country.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Biomedical Waste, Waste Management, COVID-19 Pandemic, Healthcare Facilities, Environmental Impact, Public Health.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rise of autonomous and semi-autonomous machinery in industrial settings necessitates advanced safety mechanisms to ensure smooth operation while preventing collisions and protecting nearby workers [1]. This project proposes an AI-based Extreme-Edge Collision-Avoidance System utilizing a Temporal Convolutional Network (TCN) deployed on a Raspberry Pi microcontroller. The system integrates multiple sensing technologies, including LIDAR, Camera Modules, Ultrasonic Sensors, and Infrared Sensors, to comprehensively monitor the machine's surroundings [2]. At the system's core, the STM32 microcontroller processes real-time data from the sensors via a driver circuit, ensuring ultra-low-latency response. AI-based monitoring runs on a Raspberry Pi, analyzing time-series sensor data using the TCN model to detect potential hazards in real-time [3]. The AI algorithm predicts collision risks and enhances situational awareness, ensuring timely interventions critical for industrial safety [4]. Upon detecting an obstacle, the Raspberry Pi triggers immediate corrective actions, controlling machinery operations while engaging an alarm and alert system to notify nearby workers. The integration of Raspberry Pi extends computational flexibility, supporting data logging, visualization, and remote monitoring, while enabling machine learning model updates [4]. The system ensures robust performance in noisy and dynamic industrial environments by leveraging sensor fusion and AI. Extreme-edge processing minimizes latency, optimizes energy consumption, and maintains a compact memory footprint. Designed for seamless integration into industrial applications, this real-time collision avoidance system significantly enhances workplace safety, reduces accidents, and improves operational efficiency [5].

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Collision avoidance, Industrial safety, Edge computing, Ultrasound sensors, Temporal Convolutional Network (TCN), Machine learning, Low-power MCU, Raspberry Pi, Real-time processing, Sensor fusion, Acoustic noise robustness, Embedded systems, Proximity sensing, Smart manufacturing, Industrial automation.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper explores how machine learning enhances epidemic forecasting by leveraging multimodal data for accurate, real-time outbreak prediction. It also addresses implementation challenges and proposes a modular, ethical AI-based surveillance framework

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine learning, epidemic forecasting, infectious disease surveillance, deep learning, outbreak prediction, real-time analytics, federated learning, explainable AI, public health, IoT health monitoring.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The evolution of wireless communication has seen revolutionary leaps from 1G to 5G, each generation pushing the boundaries of speed, latency, and connectivity. As 5G begins its global deployment, researchers and technologists have already turned their focus toward the next frontier: 6G. This paper explores the key features, enabling technologies, applications, and challenges associated with 6G wireless communication. With projected data rates of up to 1 Tbps, sub-millisecond latency, and AI integration, 6G aims to connect the physical, digital, and biological worlds. We propose a framework for understanding 6G's architecture, spectrum usage, and network intelligence, with original diagrams to support theoretical constructs.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

6G, Wireless Communication, Terahertz, AI, Latency, Massive Connectivity, Smart Networks

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Comparative advertising refers to promotion of a product by making comparisons with another product. Sometimes the companies while making these comparisons make exaggerated claims about their own product to establish superiority over the competing product. Comparative advertising encourages healthy competition and helps consumers make informed choices but sometimes the advertisers make false claims about the competing product and mislead the consumers. If these false claims damages the reputation and goodwill of the competitor's product, it can result in product disparagement and trademark infringement. The objective of this research paper is to explore the concept of comparative advertising and its intersection with trademark infringement and product disparagement, focusing on how they are understood and regulated under Indian law. The research paper adopts a doctrinal methodology relying on analysis of the legal provisions related to trademarks and advertising, particularly in relation to the Trade Marks Act, 1999, and the guidelines of Advertising Council of India, and also focusing on important court cases that have influenced the law. By examining statutes and various cases, this paper will analyse how Indian courts have balanced the marketing rights along with intellectual property rights. The study concludes that comparative advertising should be allowed to promote transparency in the market but it should be done by staying within ethical and legal boundaries to avoid consumer deception and brand disparagement.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Comparative Advertising, Product Disparagement, Trademark Infringement, ASCI, Trade Marks Act.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study explores the impact of Artificial Intelligence (AI) on Micro, small, and Medium Enterprises (MSMEs) in Coimbatore district. It prove AI adoption patterns, benefit, and challenge face up by theseEnterprises .

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

MSME , ARTIFICIAL INTELLIGENCE , TECHNICAL EXPERTNESS , CHAR BOTS

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research examines the key drivers of export competitiveness in the machinery industry, with emphasis on improving performance in overseas markets. Employing primary and secondary data, it establishes production costs, technological innovation, trade p olicy, and international demand as key drivers of competitiveness. The study comprises survey -based analysis of 122 respondents from the machinery sector and uses statistical means such as ANOVA, chi-square and regression to decipher the critical findings. Insights indicate that although digitalization and automation are at the forefront of priorities, cost efficiency is equally a high priority, while innovation especially in product customization and customer service remains untapped. Yet, strong statistical associations between digital strategies and competitiveness measures were relatively non-existent, which suggests a gap in implementation effectiveness. The research ends with practical recommendations for policymakers and companies to use technology, digital tools, and R&D to enhance their global positioning and maintain growth in an increasingly competitive world of exports

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Export Competitiveness, Machinery Exports, Export Strategy, Cost efficiency, Innovation and Technology

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The global agricultural sector faces mounting pressure to meet the food demands of a growing population while minimizing environmental harm. Conventional farming practices, heavily reliant on chemical fertilizers, have led to soil degradation, reduced biodiversity, and greenhouse gas emissions. Against this backdrop, the exploration of microbiome-based interventions in agriculture has emerged as a promising solution. This review explores the synergistic integration of microbial-assisted breeding (MAB) and the development of microbiome-centric biofertilizers to enhance plant health, stress resilience, and productivity in a sustainable manner. Microbial-assisted breeding is a relatively novel approach that combines classical plant breeding techniques with insights from microbial ecology and molecular biology. It focuses on selecting or engineering plant genotypes that can actively shape and maintain beneficial microbial communities--termed the phytomicrobiome--within their rhizosphere, endosphere, or phyllosphere. These microbial partners, including plant growth-promoting rhizobacteria (PGPR), mycorrhizal fungi, and nitrogen-fixing bacteria, play a crucial role in nutrient cycling, hormone regulation, pathogen suppression, and abiotic stress tolerance. Unlike traditional biofertilizers that are often applied without considering plant genotype compatibility, MAB allows for a more targeted and stable establishment of beneficial microbes through host-mediated selection. The review further delves into the role of cutting-edge technologies such as metagenomics, amplicon sequencing, and microbial trait mapping in identifying key microbial taxa associated with high-yielding or stress-tolerant crop varieties. By integrating these insights, breeders can now select plant varieties not just for yield and disease resistance but also for their ability to recruit and sustain a beneficial microbiome. Parallelly, advances in synthetic microbial communities (SynComs), genome editing tools like CRISPR-Cas9, and AI-based modeling have enabled the design of next-generation microbial inoculants that are both crop- and environment-specific. Biofertilizers derived from such research are no longer limited to simple mono-strain applications. Instead, microbial consortia are being developed with a focus on functionality, synergy, and persistence in the soil-plant system. Moreover, the formulation and delivery mechanisms of these biofertilizers have improved dramatically, with innovations in encapsulation technologies, biofilm-based inoculants, and precision delivery systems. These advancements enhance microbial shelf-life, stress tolerance, and colonization efficiency. Despite the promise, several challenges hinder the widespread adoption of MAB and microbiome-driven biofertilizers. These include inconsistencies in field performance, variability in soil microbiomes, lack of standardized protocols, and regulatory barriers. The success of this integrated approach depends on interdisciplinary collaboration among plant breeders, microbiologists, soil scientists, and agronomists. Furthermore, policy frameworks and farmer awareness programs are essential to facilitate field-level adoption. In conclusion, this review advocates for a holistic paradigm in crop improvement--one that views plants and their microbiomes as co-evolving partners. By bridging plant genetics with microbial biotechnology, microbial-assisted breeding coupled with customized biofertilizer development offers a scalable and sustainable strategy to ensure food security in an era of climate uncertainty. This approach not only enriches our understanding of plant-microbe interactions but also paves the way for ecologically responsible farming practices that can reduce dependency on chemical inputs and regenerate soil health.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Phytomicrobiome, Microbial-assisted breeding, PGPR, Synthetic microbial communities, Microbiome engineering, Host-mediated selection, Biofertilizers, Plant-microbe interaction, Metagenomics, Sustainable agriculture

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Political ecology is one of the important concepts in studying and contextualising environmental issues. The key references used in the paper explains the spatial relationships of a national and international web of drug production and trafficking. The paper is an attempt to trace the human and environmental interface in a context which leads to conflicts between two mutually co-existing ethnic groups, say the Meiteis and Kukis in the recent past. The paper also tries to understand the cause of the outbreak of the May 3rd 2023 violence between the two communities while raising several issues like what fuelled the outbreak of the violence; are there invisible factors and hands that gains from the conflict? The conflict needs to be examined from a wider lens as it apparently involves forces which are extraneous. For instance, the political instability in Myanmar, long infatuated insurgency movement, large-scale drug trafficking, difficult terrain, unman long porous border, FRM and its spill over to Manipur have played significant role in escalating Manipur conundrum. The moot question, however, is whether the conflict has significantly affected the political economic profile of the region, and Manipur state. Hill-Valley as hyphenated space seems to be becoming a ground for further division and identity politics.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

ASTUM, FMR, Golden Pentagon, Political Ecology and Drugs

  License

Creative Commons Attribution 4.0 and The Open Definition