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Experimental scrutinization on production of biogas from vegetable and animal waste
Anaerobic fermentation is a highly promising technology for converting biomass waste into methane, which may directly be used as an energy source. The objective of this research was to investigate production rate of biogas from camel dung, chicken dropping and vegetable waste. Attempts have been made in this study to optimize various parameters in order to determine the most favorable conditions for maximum biogas production from three different types of wastes such as camel dung (CAD), chicken droppings (CHD) and vegetable waste (VW). The amount of biogas produced from the wastes is compared as: VW >CHD>CAD. The results showed that biogas produced from VW is 720 ml in 32 days as compared to CHD and CAD which are 600 ml in 36 days and 80 ml in 40 days respectively. The effect of the pH and temperature on the amount of biogas produced was also studied. The experiments were conducted in temperatures ranging from 36 C to 44 C. 2023 Author(s). -
Manta Ray Foraging Optimizer with Deep Learning based Malicious Activity Detection for Privacy Protection in Social Networks
Malicious activity detection is a vital component of ensuring privacy protection in social media networks. As users engage in online interactions, protecting their sensitive information becomes paramount. Social networks can proactively identify and mitigate malicious behaviors, such as cyberbullying, data breaches, and phishing attacks by applying advanced AI and machine learning (ML) technologies. This detection system analyzes user behavior patterns, content, and network traffic to flag suspicious activities, thus safeguarding user privacy and fostering a safer online environment. The incorporation of robust malicious activity detection mechanisms helps maintain trust in social networks and reinforces the commitment to preserving user privacy in an increasingly interconnected digital landscape. This article introduces a novel Manta Ray Foraging Optimizer with Deep Learning based Malicious Activity Detection (MRFODLMAD) technique for privacy protection in social networks. The drive of the MRFODL-MAD technique is to detect and classify malicious activities in the social network. To accomplish this, the MRFODL-MAD technique preprocesses the input data. For malicious activity detection, the MRFODL-MAD technique employs long short term memory (LSTM) system. The MRFO algorithm has been executed to hyperparameter tuning process to improve the performance of the LSTM network. The experimental outcomes of the MRFODL-MAD algorithm can be tested on social networking database and the results inferred the improved performance of the MRFODL-MAD algorithm under various different measures. 2023 IEEE. -
Sustainable driven Predictive Approaches to Address Climatic Crisis: Issues and Challenges
The issue of climate crisis is currently one of the critical challenges humanity faces in the present era and it holds significant implications, for the future of our planet. To gain an understanding and mitigate the impacts of climate change several methods have been developed to model and forecast future climate trends. This paper critically analyzes sustainable techniques utilized in studying the climate crisis, such as statistical models, machine learning algorithms and climate simulations. The strengths and limitations of each method is analyzed while also considering the factors that can affect their accuracy and reliability. By consolidating existing research on this subject our aim is to provide insights into the effective sustainable approaches for predicting our climates future trajectory while offering suggestions for further research, in this crucial field. 2023 IEEE. -
Three-component p-TSA catalyzed synthesis of hydrazinyl thiazole derivatives
A direct single-pot three-component procedure for synthesizing bio-active hydrazinyl thiazole derivatives has been demonstrated. The reaction involves substituted 2-Bromoacetophenones, carboxaldehydes, and thiosemicarbazide to form the hydrazinyl thiazole scaffolds via a simple condensation reaction followed by intramolecular cyclization with p-TSA as a catalyst at room temperature. The ease of product separation, lack of column chromatographic purification, and use of readily available starting materials result in an efficient approach for organic synthesis. 2023 Elsevier Ltd. All rights reserved. -
Consolidation of Cloud Computing in Smart and Sustainable Environment
Cloud computing has revolutionized IoT device data collection, administration, and analysis by offering a scalable and sustainable solution for managing vast amounts of data. The paper highlights cloud computing's benefits in data processing, device management, cost efficiency and scalability. However, challenges related to security, data ownership, and vendor lock-in require attention. A novel sustainable cloud-IoT model is presented by integrating smart computing with cloud infrastructure. It is observed that the model records promising performance. The mean response delay is 1.9 seconds and the 89.5% is the generated mean computational storage accuracy rate. In conclusion, the cloud computing empowered sustainable model can be used in organizations to gain insights from IoT data and make informed decisions, shaping future research in this rapidly evolving field. 2023 IEEE. -
Quality and Security Assurance Workload Scheduling in Heterogeneous Cloud Environment
The adoption of cloud computing has transformed how businesses manage their workloads, offering flexibility and efficiency. This study introduces a novel model that leverages trust mechanisms to ensure secure workload execution within heterogeneous cloud environments. The primary objective of this research was to enhance efficiency by reducing both time and energy consumption associated with executing workloads. The proposed model's efficacy was assessed through the examination of Montage and Inspiral workloads. The evaluation encompassed two smaller tasks from both Montage and Inspiral workloads, in addition to one larger task. To gauge performance, a comparative analysis was conducted between the proposed model and established models such as Energy Minimized Scheduling (EMS), Efficient Replanning (ERP), and Evolutionary Computing Workload Scheduling (EC-WSC). The findings reveal that the proposed model outperforms the existing models in terms of mitigating both time and energy expenditure for the considered workloads. 2023 IEEE. -
Exploration and Analysis of Seizure Spikes Through Spectral Domain Transformation
Seizure detection is the most crucial area of investigation when it comes to understanding brain disorders. This proposed research study embarked on an automated model for epileptic seizure diagnosis by means of different kinds of Spectral transformation using EEG inputs from seizure sufferers and healthy subjects. This automated model accommodates non-invasive brain electrical activity monitoring. This method aims to facilitate the analysis and identification of epileptic seizure states since, monitoring and diagnosing such brain electrical activity is a complex task due to its numerous divisions and underlying features. The primary objective of this research study is to distinguish between EEG-based seizures and healthy individuals. To achieve this goal, a combination of spectral transformation and EEG analysis techniques is utilized. These techniques include examining the frequency spectrum, magnitude spectrum, correlation, and T-Distributed Stochastic Neighboring Embedding (T-SNE) analysis. This analysis yields valuable insights from EEG data, refining the input data and making it more suitable for prediction and identification. The models performance is evaluated using two distinct datasets: real-time EEG data from individuals experiencing epileptic seizures and EEG data from healthy subjects. These datasets are sourced from the Bangalore EEG Epilepsy Dataset (BEED), India and the BONN epilepsy dataset from the UCI repository. In a comparative study of spectral transformation methods, including Complex Fast Fourier Transform (CFFT) and Real-Valued Fast Fourier Transform (RFFT), it is discovered that reducing the data dimension by using feature extraction is not the optimal approach. This simplification leads to the loss of valuable information. Therefore, preserving the full spectrum of EEG characteristics is crucial for gaining valuable insights into brain neuronal functions, ultimately enabling more accurate seizure prediction. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Modeling a Logistic Regression based Sustained Approach for Cancer Detection
This assessment and treatment of cancer may be done using logistic regression. To properly forecast whether a tumour is malignant or benign, the likelihood of binary outcomes may be simulated based on input variables and taken into account for factors like volume, topology and texture. It aids in risk assessment by estimating an individual's likelihood of developing cancer using factors like age-group, relatives past data, life choices and gene based markers. Logistic regression plays an important role in early cancer detection and creating screening tools that identify high-risk individuals through patent characteristics, biomarkers, and medical imaging data. Prediction of the probability of survival based on age, tumor characteristics, treatment options and comorbidities is useful for survival analysis. In a comparative study, logistic regression achieved a high accuracy of 97.4%, along with random forest, in cancer detection and diagnosis. 2023 IEEE. -
Sustainable Assessment of Advanced Machine Intelligence in Clinical Safety
There is growing acknowledgment that artificial intelligence (AI) is being used to evaluate complex and vast volumes of data, producing findings without human input, in a variety of healthcare contexts, including image analysis, bioinformatics and genomics. Although this technology can offer opportunities in the diagnostic and therapeutic process, various safety-related difficulties and traps can still exist. To shed light on these opportunities and challenges, this article addresses the use of AI in healthcare and its security consequences. To deliver safer technology through AI, this research explores the cost implications of all potential technological systems, while design safety, failure safety, procedural security, and safety margins are the primary methods for identifying risks & uncertainties. Additionally, the suggestion involves the identification and distribution of explicit instructions and procedures to all relevant parties, aiming to facilitate the creation and implementation of safer Al applications within healthcare settings. 2023 IEEE. -
EV Service Stations for Future Smart Cities
The market for electric vehicles (EVs) has been growing at a fast pace in recent years. It is expected to continue growing at a much faster pace in the coming decades. The emerging EV technology is increasingly gaining a high demand for continued good transport connections in smart cities. Most of the Smart Cities' charging infrastructure and future growth revolve around its public transport network, especially an EV service station. New technologies, therefore, need to be complemented with new and versatile charging options to cater to different types of charging options available for charging Li-ion Batteries with newer materials and charging capacity. Building an EV service station in the ongoing scenario anticipates smart engineering knowledge to complement innovative charging methods. An EV service station needs hardware, software, and test equipment before charging, during charge, and post-charge states. It is expected to inform the user of available options to choose and select from. This paper investigates the challenges and suggests solutions to meet the EV service station support for EV vehicles in present and future smart cities. It also highlights the demand for a skilled workforce to maintain these service stations, including updating their skills. Examples of a few smart cities in developed as well as developing countries have been quoted. These developments will contribute to the transport infrastructure needed for future smart cities. The paper paves the way for future research in this area. The Institution of Engineering & Technology 2023. -
NLP-based Health Care- Hospital Recommendation Systems with Online Text Reviews by Patients Satisfaction
Recent times, these recommendations based on reviews play a vital role in the service industry. The hospital is assessing its quality of service using these surveys or studies posted in online forums. The ongoing pandemic also played a vital role in making the online review more popular. These statistical data and visualization are informative in representing the views of patient satisfaction towards health service. As the size of data is large and it is of varied size and format it is difficult to get consolidated results. The users share their emotions and feelings through this review. So, it is a challenge to assess the emotions of the patients. Sentiment analysis using machine learning makes our work easy in evaluating the scores visually. The reviews are analyzed using natural language processing (NLP), and the sentiment of the studies is analysed as positive, negative, and neutral using polarity ranking, which in turn is converted as the recommendation system based on patient reviews. This paper aims to propose a new method of recommending the hospital based on the sentiment of the previous user review. The thought of the user is collected from the various hospitals. The proposed (Healthcare Recommendation System) HRS system has nearly 0.5 mean absolute error, which states that the proposed HRS system is significantly effective. 2023 IEEE. -
Synthesis of 1, 8-Naphthyridine-3-Carbonitriles under solvent-free conditions using ceric ammonium nitrate
1,8-naphthyridines are synthesized using a four-component, one-pot approach. This method includes the reaction of aromatic aldehyde, malononitrile, 1,6-dimethylpyridin-2(1H)-one, substituted aniline in a solvent-free condition catalyzed by Ceric Ammonium Nitrate (CAN). Contrary to the reported literature, this distinct method houses several promising factors to the same degree as solvent-free reaction conditions, shorter reaction duration, excellent yields, and a straightforward extraction process. 2023 Elsevier Ltd. All rights reserved. -
FADA: Flooding Attack Defense AODV Protocol to counter Flooding Attack in MANET
The intrinsic nature of a Mobile Ad hoc Network (MANET) makes it difficult to provide security and it is more vulnerable to network attacks. Denial of Service (DoS) attack can be executed using Flooding attack, that has the potential to bring down the entire network. This attack works by delivering an excessive number of unwanted packets that consumes too much battery life, storage space, and bandwidth, that eventually lowers the system's performance. In order to flood the network, the attacker injects fake packets into it. Both Control Packet flooding and Data flooding attacks are taken into account in this study. FADA (Flooding Attack Defense AODV) protocol is proposed to counter flooding attack that promotes greater utilization of existing resources. This research identifies the attack-causing node, isolates it and protects the network against flooding attack. Attack Detection Rate, Attack Detection Accuracy, End-to-end delay and Throughput are few metrics used for evaluation of the proposed model. NS-2.35 is used to demonstrate the efficiency of the suggested protocol and the results prove that the proposed model increases system's throughput while decreasing attack. The simulation results have shown that the proposed FADA protocol performs better than the existing models taken into consideration. 2023 IEEE. -
A Review on EMG-based Pattern Identification Methods for Effective Controlling of Hand Prostheses
The ability of amputees to do daily duties is significantly restricted by upper limb amputation. The myoelectric prosthesis uses impulses from the surviving muscles in the stump to gradually restore function to such severed limbs. Such myosignals are unfortunately tedious and challenging to gather and employ. The process of transforming it into a user control signal after it has been acquired often consumes a significant amount of processing resources. By modifying machine learning strategies for pattern recognition, the factors that influence the traditional electromyography (EMG)-pattern identification approaches may be significantly minimized. Although more recent developments in intelligent pattern recognition algorithms could discern between a variety of degrees of freedom with high levels of accuracy, their usefulness in practical (amputee) applications was less obvious. This review paper examined how well various pattern recognition algorithms for hand prostheses performed. Finally, we discussed the current difficulties and offered some suggestions for future research in our paper's conclusion. 2023 IEEE. -
Digital Transaction Cyber-Attack Detection Using Particle Swarm Optimization
The cyber digital world is an essential variant in day-to-day life in advanced technology. There is a better change in the lifestyle as intelligent technology. In larger excite to increase the advanced technology which can be developed to humans in major dependent on network and internet users. Now, in modern times, the internet has changed the primary need in human lifestyle by giving access to everything in the world while sitting in one place knowing and updating the information and usage of online subscribers or Revolution. The world is moving in Rapid and Faster communications within a fraction of a second, at a lesser cost, and it has minimal paper-based processes and relies on the digitization document instead of a paperless environment. The data is handled by finch security practices, which are used in security worldwide to establish protected data management systems like digital lending, credits, mobile Banking, and mobile payment. Cryptocurrency and blockchain, B-trading, and banking as a service are included. At the same time, leveraging the new technologies is to resist hacking cyber-attacks. This article is also involved in artificial intelligence and machine learning (AI&ML) in different cyber-attacks. This article focuses on genetic algorithms to detect the cyber-attack. The main aim of the detection is future to prevent these cyber-attacks. The comparison will take two sample genetic algorithms. The first one is taken for Ant Colony Optimization (ACO), and the proposed model is taken for Particle Swarm Optimization. The average attack detection of ACO algorithm is 45 packets at the same time PSO algorithm will detect 50 packets. 2023 IEEE. -
Model independent approach to proton polarization in photodisintegration of deuteron
In addition to other photonuclear reactions, the study of photonuclear reactions on deuterium targets is important for laser physics, nuclear physics, astrophysics, and a number of applications, including nondestructive testing of nuclear materials. In this paper, we have carried out a model independent analysis of proton polarization in photodisintegration of deuterons with initially unpolarized beam and unpolarized target. The angular dependence of the polarization is studied by expressing it in terms of multipole amplitudes. 2023 Elsevier Ltd. All rights reserved. -
Enhancing Disease Prediction in Healthcare: A Comparative Analysis of PSO and Extreme Learning Approach
The healthcare business generates a tremendous quantity of data, and the goal is to collect it and use it effectively for analysis, prediction, and treatment. The best approach to disease management is disease prevention through early intervention. There are a number of methods that can advise you on how to treat a specific sickness, but much fewer that can tell you with any degree of certainty if you will actually get sick in the first place. Preprocessing, feature selection, feature extraction, and model training are all parts of the proposed method. The suggested layout includes a preprocessing stage that takes care of things like moving average, missing values, and normalization. Feature selection describes the process of selecting the most relevant features from a dataset. After gathering features, the models are trained using PSO-ELM. The proposed strategy is superior to the widely used PSO and ELM. 2023 IEEE. -
Smart Sensory Approach for Soil Health Tracking based Precision Farming
Internet of Things (IoT) technology will have an impact on every area in the future as it will make everything intelligent, which will affect everyone's daily lives. It is a network composed of many devices that can configure themselves. The use of IoT in smart farming is transforming traditional agricultural practices by reducing crop loss, improving them, and making them more cost-effective for farmers. The study's goal is to propose a technological model for soil health monitoring that uses smart sensors and intelligent methods to communicate with farmers through a variety of channels. Farmers will benefit from the real-time farm data (temperature, humidity, soil moisture, UV index, and IR) that allows them to practice smart farming while increasing crop yields and conserving resources. 2023 IEEE. -
Circuit Breaker: A Resilience Mechanism for Cloud Native Architecture
Over the past decade, the utilization of cloud native applications has gained significant prominence, leading many organizations to swiftly transition towards developing software applications that leverage the powerful, accessible, and efficient cloud infrastructure. As these applications are deployed in distributed environments, there arises a need for reliable mechanisms to ensure their availability and dependability. Among these mechanisms, the circuit breaker pattern has emerged as a crucial element in constructing resilient and trustworthy cloud native applications in recent times. This research article presents a comprehensive review and analysis of circuit breaker patterns and their role within cloud-native applications. The study delves into various aspects of circuit breakers, encompassing their design, implementation, and recommended practices for their utilization in cloud native applications. Additionally, the article examines and compares different circuit breaker libraries available for employment in modern software development. The paper also presents a concept for improving the circuit breaker pattern, which will be pursued in our upcoming research. 2023 IEEE. -
A Low Voltage and Low Power Analog Multiplier
In this research work, a low voltage analog multiplier has been realized through the utilization of a flipped voltage follower (FVF). The multiplier is characterized by its capacity to function at low power while exhibiting high gain. The exclusive use of transistors in its implementation renders it highly appropriate for fully integrated circuit applications. The multiplier has been developed using a supply voltage of 500 mV and an operating frequency of 25 KHz. The design consumes power of 8.23 uW. Moreover, a comparative study between the proposed multiplier and the conventional gilbert multiplier is presented in the paper. All simulations and layout designs have been conducted through the virtuoso analog design environment (ADE) of Cadence at 45 nm CMOS technology. 2023 IEEE.