| 1. |
- Palanivinayagam, Ashokkumar, et al.
(författare)
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An optimized machine learning and big data approach to crime detection
- 2021
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Ingår i: Wireless Communications & Mobile Computing. - : Hindawi. - 1530-8669 .- 1530-8677.
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Tidskriftsartikel (refereegranskat)abstract
- Crime detection is one of the most important research applications in machine learning. Identifying and reducing crime rates is crucial to developing a healthy society. Big Data techniques are applied to collect and analyse data: determine the required features and prime attributes that cause the emergence of crime hotspots. The traditional crime detection and machine learning-based algorithms lack the ability to generate key prime attributes from the crime dataset, hence most often fail to predict crime patterns successfully. This paper is aimed at extracting the prime attributes such as time zones, crime probability, and crime hotspots and performing vulnerability analysis to increase the accuracy of the subject machine learning algorithm. We implemented our proposed methodology using two standard datasets. Results show that the proposed feature generation method increased the performance of machine learning models. The highest accuracy of 97.5% was obtained when the proposed methodology was applied to the Naïve Bayes algorithm while analysing the San Francisco dataset.
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| 2. |
- Pandya, Sharnil, Researcher, 1984-, et al.
(författare)
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Federated learning for smart cities : A comprehensive survey
- 2023
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Ingår i: Sustainable Energy Technologies and Assessments. - : Elsevier. - 2213-1388 .- 2213-1396. ; 55
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Tidskriftsartikel (refereegranskat)abstract
- With the advent of new technologies such as the Artificial Intelligence of Things (AIoT), big data, fog computing, and edge computing, smart city applications have suffered from issues, such as leakage of confidential and sensitive information. To envision smart cities, it will be necessary to integrate federated learning (FL) with smart city applications. FL integration with smart city applications can provide privacy preservation and sensitive information protection. In this paper, we present a comprehensive overview of the current and future developments of FL for smart cities. Furthermore, we highlight the societal, industrial, and technological trends driving FL for smart cities. Then, we discuss the concept of FL for smart cities, and numerous FL integrated smart city applications, including smart transportation systems, smart healthcare, smart grid, smart governance, smart disaster management, smart industries, and UAVs for smart city monitoring, as well as alternative solutions and research enhancements for the future. Finally, we outline and analyze various research challenges and prospects for the development of FL for smart cities.
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| 3. |
- Singh, Swati, et al.
(författare)
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Assessment of pathogen removal efficiency of vertical flow constructed wetland treating septage
- 2023
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Septage refers to the semi-liquid waste material that accumulates in septic tanks and other onsite sanitation systems. It is composed of a complex mixture of human excreta, wastewater, and various solid particles. Septage is a potential source of water pollution owing to presence of high organic content, significant pathogen concentrations, and a range of nutrients like nitrogen and phosphorus. The harmful impacts of septage pollution poses significant risks to public health through the contamination of drinking water sources, eutrophication of water bodies and spread of water borne diseases. Conventional septage treatment technologies often face limitations such as high operational costs, energy requirements, and the need for extensive infrastructure. Therefore, with an aim to treat septage through an alternative cost effective and energy-efficient technology, a laboratory-scale constructed wetland (CW) system (0.99 m2) consisting of a sludge drying bed and a vertical flow wetland bed was utilized for the treatment of septage. The sludge drying bed and vertical flow beds were connected in series and filled with a combination of gravel with varying sizes (ranging from 5 to 40 mm) and washed sand. Canna indica plants were cultivated on both beds to facilitate phytoremediation process. The system was operated with intermittent dosing of 30 Ltrs of septage every day for 2 months. The HRT of the system was fixed at 48 h. The average inlet loads of Biochemical Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), and Total Suspended Solids (TSS) were measured as 150 ± 65.7 g m−2 day−1, 713 ± 443.9 g m−2 day−1, and 309 ± 66.3 g m−2 day−1, respectively. After treatment, the final effluent had an average load of 6 g m−2 day−1 for BOD5, 15 g m−2 day−1 for COD, and 51 g m−2 day−1 for TSS, indicating that the CW system achieved an average removal efficiency of 88% for BOD, 87% for COD, and 65% for TSS. The average load of total coliforms and helminthes eggs in the influent was recorded as 4 × 108 Colony-Forming Units (CFU) m−2 day−1 and 3 × 107 eggs m−2 day−1, respectively. However, the CW system demonstrated significant effectiveness in reducing microbial contamination, with an average removal efficiency of 99% for both total coliforms and helminthes eggs. The vertical flow constructed wetland system, equipped with pretreatment by sludge drying bed, has proven to be efficient in treatment of septage.
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| 4. |
- Upadhyay, Sweta, et al.
(författare)
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Microplastics in freshwater: Unveiling sources, fate, and removal strategies
- 2024
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Ingår i: Groundwater for Sustainable Development. - : Elsevier BV. - 2352-801X. ; 26
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Forskningsöversikt (refereegranskat)abstract
- In aquatic environments, microplastics pose alarming threat to the environment since they actively convey hazardous contaminants and aggregate into biota. Although studies on microplastics in freshwater ecosystems have increased recently, there are still many concerns about the origins, sources, fate, and distribution of MPs. This thorough review includes 167 studies (2017–2024) with an aim to provide knowledge of the type, sources, and detection of microplastics in freshwater ecosystems, along with their consequences on aquatic species and human health. The main sources of microplastic in freshwater ecosystems include improperly disposed plastic trash, industrial raw materials, personal care items, and synthetic fabrics. Factors like retention time, flow rate, and seasonal variations influence their permanence in freshwater (FW) ecosystems, ultimately leading to their transport through river networks. The most hazardous polymers identified are PUR, PAN, PVC, Epoxy resin, and ABS. Previous studies have confirmed their ‘Trojan horse effect’ due to their ability to adorb drugs (e.g., acyclovir, atenolol, sulfamethoxazole, and ibuprofen), heavy metals (As, Cd), pesticides (difenoconazole, buprofezin, imidacloprid), antibiotic-resistant genes and microorganisms. Microplastics carrying above pollutants may possess carcinogenic properties and other health risks, considering their entry into the human body through FW-sourced water and food products. Currently, there is a lack of standardized protocols for the identification, assessment, and quantification of MPs in freshwater ecosystems. The common identification techniques are spectroscopy, Microscopy, mass spectroscopy, and novel methods like staining and AFM-IR spectroscopy. The multifaceted impact of microplastics on FW ecosystems, from contaminant transmission to human health, underscores the intricate interactions within this environmental challenge.
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| 5. |
- Victor, Nancy, et al.
(författare)
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Federated learning for iout : Concepts, applications, challenges and future directions
- 2022
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Ingår i: IEEE Internet of Things Magazine (IoT). - 2576-3180 .- 2576-3199. ; 5:4
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Tidskriftsartikel (refereegranskat)abstract
- Internet of Underwater Things (IoUT) have gained rapid momentum over the past decade with applications spanning from environmental monitoring and exploration, defence applications, etc. The traditional IoUT systems use machine learning (ML) approaches which cater the needs of reliability, efficiency and timeliness. However, an extensive review of the various studies conducted highlight the significance of data privacy and security in IoUT frameworks as a predominant factor in achieving desired outcomes in mission critical applications. Federated learning (FL) is a secured, decentralized framework which is a recent development in ML, that can help in fulfilling the challenges faced by conventional ML approaches in IoUT. This article presents an overview of the various applications of FL in IoUT, its challenges, open issues and indicates direction of future research prospects.
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