Water Pollution Management and detection techniques: a Review

Now a day’s water pollution became a national and global issue not only in India but over the whole world. Like India other countries are also facing the same problem of water pollution due to agricultural waste, industrial waste, sewage waste and so on. In this paper it has been explained clearly with the help of considerable number of references. It gives the information about the pollutants which pollutes the water. Sensors have a ability to Control and monitor the quality of water as well as able to detect the contaminants added due to human generating activities. Sensors can be used for the selection and identification of the techniques which will be suitable for performing the given task that is identification of pollutants present in water. Various pollutants found in water are pesticides, harmful chemicals, heavy metals, nutrients, etc. We will also going to discuss about the different techniques used for water pollution detections. Tethered

Aquatic Robot Design for Water Pollutants Monitoring

This paper focuses on the design and development of an Aquatic Robot for water pollutants monitoring. An aquatic robot is integrated with the smartphone for data acquisition. The implemented design contains CV algorithm for image processing on openCV platform. Regularly monitoring aquatic pollutants is needed for pure aquatic environment and safe aquatic life. The human health and water transport are also main consideration towards this robot design. The proposed Aquatic robot consists of sensors and camera for sensing hazardous pollutants and capturing images of surrounding environment respectively. The aquatic robot can accurately detect pollutants and display results on smartphone in the presence of various conditions. DOI: 10.17762/ijritcc2321-8169.15064

AI-Driven High-Precision Model for Blockage Detection in Urban Wastewater Systems

In artificial intelligence (AI), computer vision consists of intelligent models to interpret and recognize the visual world, similar to human vision. This technology relies on a synergy of extensive data and human expertise, meticulously structured to yield accurate results. Tackling the intricate task of locating and resolving blockages within sewer systems is a significant challenge due to their diverse nature and lack of robust technique. This research utilizes the previously introduced “S-BIRD” dataset, a collection of frames depicting sewer blockages, as the foundational training data for a deep neural network model. To enhance the model’s performance and attain optimal results, transfer learning and fine-tuning techniques are strategically implemented on the YOLOv5 architecture, using the corresponding dataset. The outcomes of the trained model exhibit a remarkable accuracy rate in sewer blockage detection, thereby boosting the reliability and efficacy of the associated robotic framework for proficient removal of various blockages. Particularly noteworthy is the achieved mean average precision (mAP) score of 96.30% at a confidence threshold of 0.5, maintaining a consistently high-performance level of 79.20% across Intersection over Union (IoU) thresholds ranging from 0.5 to 0.95. It is expected that this work contributes to advancing the applications of AI-driven solutions for modern urban sanitation systems

ANOMALY BASED DETECTION AND PREVENTION TO PROVIDE SECURE MANET USING DUAL HEAD CLUSTER IN HIERARCHICAL COOPERATIVE IDS

ABSTRACT A purely wireless network wherein each device itself acts as a node and also performs the task of router is called as Mobile Ad-hoc network. A MANET has become a need of today's fastest developing era. A measure issue in MANET is security as it is an autonomous system of nodes which has no fixed infrastructure and also, due to continuous movement of mobile nodes it has dynamic topology so it is difficult to maintain security. In our proposed system a cluster with dual head will be used in cooperative IDS for anomaly detection system .Two head nodes will be protecting each other from intrusion along with detecting intrusion for cluster member. This intrusion can be detected by signature analysis or anomaly based detection. Anomaly based detection will detect intrusion by monitoring the whole system activities. Our proposed system will also find attacks which are new and which were not possible to detect by using signature analysis. Proposed system will be able to detect the anomaly behaviour of the attacks like black hole, Dos and flood anomaly. As a result of our research work a stable, secure network will get formed

EVALUATION OF NEUROPHARMACOLOGICAL ACTIVITY OF MEDICINAL PLANT

Objective: The objective of the study is to evaluate the antiparkinsonian activity of hydroalcoholic extract of the seeds of Canavalia gladiata (HECG) in zebrafish and Swiss albino mice.Materials and Methods: Catalepsy was induced in zebrafish by exposing them to haloperidol solution. Treatment groups were exposed to bromocriptine and HECG, 30 min before haloperidol exposure at the dose of 2, 5, and 10 μg/mL. Latency to travel from one fixed point to another, time spent near the bottom of the tank, and complete cataleptic time were evaluated to assess behavioral changes. In mice, catalepsy was induced using haloperidol (1 mg/kg i.p.). Treatment groups received bromocriptine (2.5 mg/kg) and HECG at the dose of (100, 200, and 300 mg/kg) orally. Bar test for catalepsy, motor coordination test by rotarod, and locomotor activity by actophotometer were carried out to assess behavioral changes.Results: Bromocriptine and HECG-treated groups showed significant difference in behavioral parameters as compared to haloperidol control group in both the experimental models.Conclusion: Canavalia gladiata seeds exhibited significant antiparkinsonian activity in haloperidol mouse model and zebrafish. Zebrafish can be used with ease and effectiveness for initial screening of drugs before subjecting them to rodent testing

S-BIRD: A Novel Critical Multi-Class Imagery Dataset for Sewer Monitoring and Maintenance Systems

Computer vision in consideration of automated and robotic systems has come up as a steady and robust platform in sewer maintenance and cleaning tasks. The AI revolution has enhanced the ability of computer vision and is being used to detect problems with underground sewer pipes, such as blockages and damages. A large amount of appropriate, validated, and labeled imagery data is always a key requirement for learning AI-based detection models to generate the desired outcomes. In this paper, a new imagery dataset S-BIRD (Sewer-Blockages Imagery Recognition Dataset) is presented to draw attention to the predominant sewers’ blockages issue caused by grease, plastic and tree roots. The need for the S-BIRD dataset and various parameters such as its strength, performance, consistency and feasibility have been considered and analyzed for real-time detection tasks. The YOLOX object detection model has been trained to prove the consistency and viability of the S-BIRD dataset. It also specified how the presented dataset will be used in an embedded vision-based robotic system to detect and remove sewer blockages in real-time. The outcomes of an individual survey conducted at a typical mid-size city in a developing country, Pune, India, give ground for the necessity of the presented work