Rana Khalid Hamad

Babylonian Journal of Machine Learning • 2025

In bioinformatics, the classification of gene-disease associations is crucial. It directly affects whether we can untangle the genetic roots of various disease as well as if we will find some justifiable therapy for these cured diseases.Using XBNet to construct genetic algorithms for higher accuracy and speeds of gene-disease classification--this is the method developed in the book.Consisting of gene expression profiles for six diseases--Alzheimer's, Asthma, Cancer, Diabetes, Fabry and Down syndrome--our research has applied a comprehensive pre-processing technique to this data set from Kaggle. This has included such things as eliminating stop-words and punctuation marks and tokenization. Using the terms of Frequency (TF) and of Term Frequency-Inverse Document Frequency (TF-IDF method) for features extraction, our text data on genes are transformed into numerical axes fit for input to machine learning models.

Waseem Ghafori Yass, Mohammad Faris

Babylonian Journal of Machine Learning • 2023

This research focuses on the advancements in car detection techniques, particularly targeting wrong-way driving vehicles, using deep learning and machine learning methodologies. In recent years, numerous techniques have been proposed to address vehicle detection in real-time scenarios, leveraging algorithms such as YOLO (You Only Look Once) and centroid tracking to detect vehicles in various traffic situations. Additionally, methods involving UAV imagery, infrared imaging, and frame differencing approaches have enhanced the capabilities of real-time vehicle detection systems. Despite achieving significant milestones in accuracy and efficiency, existing methods still face limitations, such as high false-positive rates, imbalanced datasets, and challenges in complex environments like poor lighting and diverse road conditions. This study provides a comprehensive review of recent car detection approaches, comparing various algorithms including YOLO variants, CTAD, CNN, and DLMTD, and evaluating their strengths and limitations. A critical analysis of these methods reveals areas for improvement, particularly in terms of enhancing robustness, optimizing real-time response, and expanding detection capabilities to accommodate complex traffic patterns. The findings underscore the potential of hybrid approaches that combine object detection, tracking, and feature extraction techniques to achieve higher accuracy and adaptability in real-time applications. The study concludes by proposing a framework that addresses the observed limitations, suggesting pathways for future research in developing efficient, AI-powered car detection systems tailored for real-world applications.

Sahar Yousif Mohammed

Babylonian Journal of Machine Learning • 2024

Language learning has changed in recent years with the inclusion of ArtificialIntelligence (AI) bots. This paper discusses how AI bots have changed languageacquisition, paying more attention on how they improve language learningexperience. The Article looks at what prominent AI bots such as Gemini,ChatGPT and Cloud can do to make personalized feedback a reality as well asenhance interactivity and convenience in language learning.This article examinesthe functionality of adaptive learning algorithms which correct errors in real time,along with immersive environments that demonstrate the importance of artificialintelligence bots in achieving effective language acquisition. AI Bots are enablingbetter tutoring, increased cultural awareness and expanded learning choices.The fieldof foreign language education has been revolutionized thanks to tailored supportprovided by AI bots that promote cultural understanding and also offer flexible waysfor studying.

Unknown Author

Advances in Machine Learning & Artificial Intelligence • 2021

An important diagnostic method for diagnosing abnormalities in the human heart is the electrocardiogram (ECG). A large number of heart patients increase the assignment of physicians. To reduce their assignment, an automatic computer detection system is needed. In this study, a computer system for classifying ECG signals is presented. The MIT-BIH, ECG arrhythmia database is used for analysis. After the ECG signal is noisy in the preprocessing stage, the data feature is extracted. In the feature extraction step, the decision tree is used and the support vector machine (SVM) is constructed to classify the ECG signal into two categories. It is normal or abnormal. The results show that the system classifies the given ECG signal with 90% sensitivity.

Unknown Author

Advances in Machine Learning & Artificial Intelligence • 2024

This paper analyzes a dataset containing radio frequency (RF) measurements and Key Performance Indicators (KPIs) captured at 1876.6MHz with a bandwidth of 10MHz from an operational 4G LTE network in Nigeria. The dataset includes metrics such as RSRP (Reference Signal Received Power), which measures the power level of reference signals; RSRQ (Reference Signal Received Quality), an indicator of signal quality that provides insight into the number of users sharing the same resources; RSSI (Received Signal Strength Indicator), which gauges the total received power in a bandwidth; SINR (Signal to Interference plus Noise Ratio), a measure of signal quality considering both interference and noise; and other KPIs, all derived from three evolved node base stations (eNodeBs). After meticulous data cleaning, a subset of measurements from one serving eNB, spanning a 20-minute duration, was selected for deeper analysis. The PDCP DL Throughput, as a vital KPI metric, plays a paramount role in evaluating network quality and resource allocation strategies. Leveraging the high granularity of the data, the primary aim was to predict throughput. For this purpose, I compared the predictive capabilities of two machine learning models: Linear Regression and Random Forest. Metrics such as Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used to examine the models as they offer a comprehensive insight into the models’ accuracies. The comparative analysis highlighted the superior performance of the Random Forest model in predicting the PDCP DL Throughput. The insights derived from this research can potentially guide network engineers and data scientists in optimizing network performance, ensuring a seamless user experience. Furthermore, as the telecommunication industry advances towards the integration of 5G and beyond, the methodologies explored in this paper will be invaluable in addressing the increasingly complex challenges of future wireless networks.

Abdulazeez Alsajri, Amani Steiti

Babylonian Journal of Machine Learning • 2023

The widespread utilization of the internet and computer systems has resulted in notable security concerns, characterized by a surge in intrusions and vulnerabilities. Malicious users manipulate internal systems, resulting in the exploitation of software flaws and default setups. With the integration of the internet into society, there is an emergence of new risks such as viruses and worms, which highlights the importance of implementing robust security measures. Intrusion detection systems (IDS) are security technologies utilized to monitor and analyze network traffic or system activity with the purpose of identifying hostile behavior. This article presents a proposed method for detecting intrusion in network traffic using a hybrid approach, which combines a genetic algorithm and an SVM algorithm. The model underwent training and testing on the KDDCup99 dataset, with a reduction in features from 42 to 29 using the hybrid approach. The results demonstrated that throughout the system testing, it exhibited a remarkable accuracy of 0.999. Additionally, it achieved a true positive value of 0.9987 and a false negative rate of 0.012.

Hadeel M Saleh, Abdulrahman Kareem Oleiwi, Ahmed Abed Hwaidi Abed

Babylonian Journal of Machine Learning • 2023

Human Immunodeficiency Virus (HIV) is a global health issue that can progress to Acquired Immunodeficiency Syndrome (HIV) if not diagnosed and treated early. The advent of Artificial Intelligence (AI), particularly in machine learning and deep learning, presents new opportunities for improving the accuracy and efficiency of HIV diagnosis. This research explores the application of AI techniques in diagnosing HIV by reviewing previous studies and proposing a novel AI-based approach. The proposed methodology leverages deep learning algorithms, such as convolutional neural networks (CNNs), along with advanced data preprocessing techniques to enhance diagnostic accuracy, sensitivity, and specificity. The results of the proposed CNN-based model show an accuracy of 96.2%, sensitivity of 95.8%, specificity of 96.8%, and an AUC-ROC score of 0.965. Compared to Random Forest (accuracy: 92.1%), SVM (accuracy: 91.5%), and traditional methods (accuracy: 89.0%), the CNN model outperforms existing techniques significantly in terms of accuracy, sensitivity, and specificity. This demonstrates the effectiveness of the proposed AI approach for enhancing early and accurate HIV detection.

Alok Singh Chauhan, H Mary Henrietta

Babylonian Journal of Machine Learning • 2023

The domain of machine learning has experienced an unparalleled increase in attention and implementation, becoming an essential component of diverse businesses. This review paper provides a thorough analysis of the comprehensive handbook named "Machine Learning Basics: A Comprehensive Guide." Written by [Dr. Jane Doe], this guide has become a vital reference for those at all levels of expertise seeking to comprehend and traverse the intricate realm of machine learning.

Unknown Author

Advances in Machine Learning & Artificial Intelligence • 2021

Software defect prediction is a significant activity in every software firm. It helps in producing quality software by reliable defect prediction, defect elimination, and prediction of modules that are susceptible to defect. Several researchers have proposed different software prediction approaches in the past. However, these conventional software defect predictions are prone to low classification accuracy, time-consuming, and tasking. This paper aims to develop a novel multi-model ensemble machine-learning for software defect prediction. The ensemble technique can reduce inconsistency among training and test datasets and eliminate bias in the training and testing phase of the model, thereby overcoming the downsides that have characterized the existing techniques used for the prediction of a software defect. To address these shortcomings, this paper proposes a new ensemble machine-learning model for software defect prediction using k Nearest Neighbour (kNN), Generalized Linear Model with Elastic Net Regularization (GLMNet), and Linear Discriminant Analysis (LDA) with Random Forest as base learner. Experiments were conducted using the proposed model on CM1, JM1, KC3, and PC3 datasets from the NASA PROMISE repository using the RStudio simulation tool. The ensemble technique achieved 87.69% for CM1 dataset, 81.11% for JM1 dataset, 90.70% for PC3 dataset, and 94.74% for KC3 dataset. The performance of the proposed system was compared with that of other existing techniques in literature in terms of AUC. The ensemble technique achieved 87%, which is better than the other seven state-of-the-art techniques under consideration. On average, the proposed model achieved an overall prediction accuracy of 88.56% for all datasets used for experiments. The results demonstrated that the ensemble model succeeded in effectively predicting the defects in PROMISE datasets that are notorious for their noisy features and high dimensions. This shows that ensemble machine learning is promising and the future of software defect prediction.

Abdulazeez Alsajri

Babylonian Journal of Machine Learning • 2023

As we enter the Industry 5.0 era, enormous volumes of data are being created across digital systems. Machine learning techniques have recently achieved immense success in areas such as intelligent control, decision-making, speech recognition, natural language processing, computer graphics, and computer vision. This despite the significant challenge of analyzing and interpreting massive datasets. Owing to their strong performance, deep learning and machine learning algorithms have become widely deployed across various real-time engineering applications. Developing working knowledge of machine learning is now critical for building automated, smart systems that can process data in domains like healthcare, cybersecurity, and intelligent transportation. There exist multiple strategies in machine learning, including reinforcement learning, semi-supervised learning, unsupervised learning, and supervised learning algorithms. This research provides a comprehensive examination of leveraging machine learning for managing real-time engineering systems, with the goal of augmenting their capabilities and intelligence. It contributes to the understanding of how different machine learning approaches can be applied in real-world use cases like cybersecurity, healthcare, and intelligent transportation. Additionally, it highlights ongoing research objectives and difficulties that machine learning techniques encounter while tackling real-world systems. This research serves both industry professionals and academics as a reference, while technically benchmarking decision-making across different application areas and real-world scenarios.

Ashima Tyagi, Vibhav Prakash Singh, Manoj Madhava Gore

Neuroinformatics • 2024

Muhammad Ahmad, Maryam Yousaf, Aisha Batool et al.

Fuel • 2024

Ke Feng, Yi Lu, Yao Shen et al.

Journal of Power Sources • 2023

Qi Yang, Xin Bao, Ziying Li et al.

Journal of Water Process Engineering • 2022

Jocelyn Liao, Zhen He

Journal of Emerging Investigators • 2022

Climate change brings frequent and intense storms, which challenge aging stormwater infrastructures. As a sustainable stormwater solution, green roofs are being more frequently used in urban areas. However, high installation and maintenance costs have limited applications of green roofs on a large scale. A plant microbial fuel cell (P-MFC) is a novel technology that uses bacteria living around the root of plants to generate electricity. This study explores the integration of P-MFCs with an extensive green roof module for the dual benefit of stormwater runoff reduction and renewable energy generation. While most P-MFC systems in the literature are based on flooded plants, the green roof MFC developed in this study uses sedum plants which are among the most common vegetation layers for green roof solutions. We hypothesized that MFC efficiency could be improved by introducing a water storage layer and capillary sub-irrigation. Moreover, the capillary irrigation wick can function as a salt bridge to further enhance power generation. Three prototypes were fabricated to test our hypotheses, including a control unit without sub-irrigation and two units with capillary irrigation and one with additional salt bridge configuration. Experiments demonstrated that capillary irrigation and the salt bridge configuration significantly increased P-MFC’s power density and decreased the internal resistance. The research demonstrated that green roof modules with integrated P-MFCs can be a renewable energy generator, promoting the adoption of green roofs as sustainable solutions for climate resilience by simultaneously reducing storm floods, capturing carbon dioxide (CO2) in the atmosphere, and producing green electricity.

D. Vidhyeswari, A. Surendhar, S. Bhuvaneshwari

Chemosphere • 2022

Jimil Mehta, Soumesh Chatterjee, Manisha Shah

Journal of Environmental Management • 2024

Fabian Fischer, Nancy Merino, Marc Sugnaux et al.

Chemical Engineering Journal • 2022

Demin Jiang, Huina Chen, Hao Xie et al.

ChemistrySelect • 2022

Abstract Hydrophobicity of carbon‐based anodes hinders bacterial adhesion and biofilm formation. Herein, MnO 2 @MXene coated carbon cloth (CC) was designed as an anode for microbial fuel cells (MFCs). The anode integrated the hydrophilicity and conductivity of MXene with the biocompatibility of MnO 2 . This unique structure promoted bacterial colonisation and biofilm formation on the anode surface. MnO 2 @MXene enhanced electricity generation performance and wastewater degradation efficiency owing to the synergistic effect of MXene and MnO 2 . The MFC achieved a short startup time and an effective extracellular electron transfer process. The MnO 2 @MXene/CC anode ensured a higher power density and efficient decolourisation in MFCs. The MFC device achieved a high maximum power density of 746.3 mW/m 2 and a Congo red decolourisation efficiency of 87.6 % at 48 h. This work offers a potential strategy for the effective degradation of wastewater and recovery of electrical energy.

Soichiro Hirose, Trang Nakamoto, Kozo Taguchi

Resourceedings • 2023

Fossil fuels, the primary source of energy supply in modern society, are both unsustainable and damaging to the environment. The most cost-effective way to reduce the use of fossil fuels is to switch to renewable energy sources. Soil microbial fuel cells (SMFC) are a green energy production method because they use electron-generating bacteria in the soil to obtain electrical energy from organic matter. One way to improve the output of SMFCs is to increase the specific surface area of the anodes. The larger specific surface area allows more electrons to be received from the bacteria. In this study, bucky paper (BP) was utilized as the anode of SMFC. BP is a freestanding film fabricated from multi-walled carbon nanotubes (CNT) by vacuum filtration method. CNT has a high specific surface area and electrical conductivity. BP is also considered to be mechanically stable in soil due to its CNT network structure. However, the surface of the BP is hydrophobic. In SMFCs, the hydrophobic surface of the anode is a fatal disadvantage in terms of the affinity of microorganisms. Thus, heat treatment and UV ozone treatment were employed to make the surface of BP hydrophilic, and their output in SMFCs was investigated. As a result, SMFCs using UV-ozone-treated BPs as anodes produced the highest power density of 28.8 μW/cm². Also, unlike thermal treatment, UV ozone treatment did not damage the CNT structure. Hence, in this experiment, the output power of the SMFC was stable for at least 140 hours.

Harsha Nagar, Srimukhi Mandava, Mohammed K. Al Mesfer et al.

Fuel • 2023

Manisha Verma, Vishal Mishra

Biomass and Bioenergy • 2022

Marzieh Cheraghipoor, Davod Mohebbi-Kalhori, Meissam Noroozifar et al.

Fuel • 2020

Dhruva Mukhopadhyay, Rakesh Kumar Sharma, Pratima Gupta

Fuel Cells • 2022

Abstract Lignin is one of the most versatile and complex macromolecules, which can be converted to value‐added products such as p‐coumaric acid and vanillin upon depolymerization. The current work explored oxidative lignin depolymerization in a microbial peroxide‐producing cell containing manganese peroxidase enzymes. A double‐chambered microbial peroxide‐producing cell was constructed containing the immobilized manganese peroxidase on alginate beads in the cathode chamber, while the anodic chamber contained wastewater. This setup was run for 8 days after the addition of lignin in the catholyte. The voltage measured in the circuit was 0.491 V while the current and power densities were 223 µA/cm 2 and 110 µW/cm 2 , respectively on the 8th day of the experiment. The maximum H 2 O 2 concentration observed was 1.5 mM on the 6th day. Depolymerization of lignin was confirmed by the change in the significant peaks at 280 nm of the ultraviolet‐visible spectrum. A change in the signature regions of β‐β linkages and β‐O‐4 linkages in the Fourier‐transform infrared spectrum was also observed. Liquid chromatography–quadrupole time of flight–mass spectrometry analysis revealed the presence of compounds including isoeugenol, acetovanillone, methacrylic acid, phenamacril, diofenolan, and jasmolin identified as the product of lignin depolymerization.

Unknown Author

Indian Journal of Chemical Technology • 2024

Unknown Author

Archives of Environmental Protection • 2023

The contamination of the environment by antibiotics has become a serious problem, supported by abundant scientific evidence of its negative impact on both aquatic ecosystems and human health. Therefore, it is crucial to intensify research efforts towards developing effective and efficient processes for removing antibiotics from the aquatic environment. In this study, a bacterial consortium capable of breaking down penicillin was employed in a ceramic separator microbial fuel cell (MFC) to generate electricity. The consortium’s properties such as laccase activity, penicillin removal and microbial structure were studied. The SF11 bacterial consortium, with a laccase activity of 6.16±0.04 U/mL, was found to be effective in breaking down penicillin. The highest rate of penicillin removal (92.15±0.27%) was achieved when the SF11 consortium was incubated at 30 °C for 48 hours. Furthermore, when used as a whole-cell biocatalyst in a low-cost upflow MFC, the Morganella morganii-rich SF11 consortium demonstrated the highest voltage and power density of 964.93±1.86 mV and 0.56±0.00 W/m3, respectively. These results suggest that the SF11 bacterial consortium has the potential for use in ceramic separator MFCs for the removal of penicillin and electricity generation.

Bingying Cao

Applied and Computational Engineering • 2024

As global energy demand grows and environmental problems intensify, the search for clean, renewable energy solutions becomes especially urgent. This article reviews microbial fuel cell (MFCs) technology, a new type of clean energy technology that uses microbial metabolic activity to convert organic matter into electricity. Compared with traditional fossil fuel power generation methods, MFCs have significant advantages such as low pollution, low noise, and renewable. The working principle of MFCs, including the reaction process of anode and cathode, as well as the key factors affecting the performance of MFCs, such as the choice of anode and cathode materials, are discussed. In addition, the coupling applications of MFCs with other technologies, such as photocatalysis, electrofenton technology, microbial electrolysis (MEC) and constructed wetland technology, are discussed, which provide new possibilities for improving wastewater treatment efficiency and expanding the application field of MFC. Finally, the challenges and future development direction of MFCs technology are prospected, and key research directions such as improving energy conversion efficiency, reducing cost and enhancing system stability are pointed out.

Peng Cheng, Yingchuan Zhang, Xinlan Ying et al.

Fuel • 2024

Jiawei Yang, Shaoan Cheng, Shenglong Zhang et al.

Chemosphere • 2022

Pankaj Kumar, Suraj Prakash Singh Rana, Sakshi et al.

Fuel • 2025

Shuyi Zhou

Innovation in Science and Technology • 2022

Microbial fuel cell (MFC) is a device that uses microorganisms to convert chemical energy from organic matter directly into electrical energy. It is considered to have the potential for a wide range of applications to meet future human energy needs fuel diversity. This paper introduces the basic working principle of MFC and illustrates the electrode material, membrane and cell configuration selection on the performance influence of MFC. In addition, the application progress of MFC in recent years is reviewed, including sewage treatment, microbial electrolysis cell (MEC) and microbial desalination cell (MDC). Finally, the development direction of MFC is prospected: membrane and electrode materials need to be further studied, and MFC coupling technology needs to be continuously promoted.

B. Sivasankari, Vishwathi S.V, S. Ganesh

PARIPEX INDIAN JOURNAL OF RESEARCH • 2021

Waste generated by both agro based industries and domestic units have high nutrient contents to support microbial growth, these wastes are indiscriminately dumped and constitutes environmental and health hazards. Some of these wastes can be used to grow some bacterial species in microbial fuel cell to generate bioelectricity. The Microbial fuel cell is a device where the bacteria can grow on one electrode, they breakdown organic matter and release electrons from it. The bacteria can do this by keeping them separate from the oxygen, and when they release those electrons it creates a potential between the electrodes of about half a volt and voltage times current is power, and that is how power is generated from it. The waste materials used in this work are banana peels. Bacterial isolates that are used from the sewage and hay soil all within Gandhigram institute, Gandhigram. Microscopic characterization of the isolates by Gram reaction revealed the Gram negative and Gram positive and Biochemical test showed that the three isolated organisms were Escherichia sp., Pseudomonas sp, Bacillus sp. Microbial fuel cell were fabricated with a two plastic bottles as anode and cathode chamber. The electrode used were Aluminium mesh. A 3.75% sodium chloride, 2.2% agar salt bridge connected the chambers. The organism and banana peel as biocatalyst. Result that was monitored showed the maximum of 86mV at 24 hours reading respectively

Mingyan Zhang

Highlights in Science, Engineering and Technology • 2024

Microbial fuel cells (MFC) are bioelectrochemical systems that convert the chemical energy of organic compounds or renewable energy into electrical or bioelectrical energy through microbial-catalyzed reactions on the anode. The core principle of the technology is that microorganisms at the anode release electrons during the oxidation of the substrate, and electrons pass through the external circuit to the cathode, where they undergo a reduction reaction with oxygen, and finally combine with hydrogen ions to form water. Microorganisms play the role of electron transport medium, which realizes the efficient conversion of substrate energy to electric energy.As a green energy technology, it can generate electricity while relying on microorganisms to degrade organic pollutants in sewage. However, the high cost and low conversion efficiency limit the industrial applications of MFC. This paper briefly describes the reaction mechanism of microbial fuel cells and reviews the research progress on two important factors that influence the performance of MFC. Finally, the future development of MFC is anticipated to provide some references for related research.

John Parker Evans, Dominic F. Gervasio, Barry M. Pryor

Catalysts • 2021

The construction of optimized biological fuel cells requires a cathode which combines the longevity of a microbial catalyst with the current density of an enzymatic catalyst. Laccase-secreting fungi were grown directly on the cathode of a biological fuel cell to facilitate the exchange of inactive enzymes with active enzymes, with the goal of extending the lifetime of laccase cathodes. Directly incorporating the laccase-producing fungus at the cathode extends the operational lifetime of laccase cathodes while eliminating the need for frequent replenishment of the electrolyte. The hybrid microbial–enzymatic cathode addresses the issue of enzyme inactivation by using the natural ability of fungi to exchange inactive laccases at the cathode with active laccases. Finally, enzyme adsorption was increased through the use of a functionally graded coating containing an optimized ratio of titanium dioxide nanoparticles and single-walled carbon nanotubes. The hybrid microbial–enzymatic fuel cell combines the higher current density of enzymatic fuel cells with the longevity of microbial fuel cells, and demonstrates the feasibility of a self-regenerating fuel cell in which inactive laccases are continuously exchanged with active laccases.

Fareeha Batool, Waheed Miran, Marghoob Ahmed et al.

Journal of Applied Electrochemistry • 2025

Chengcheng Xing, Demin Jiang, Le Tong et al.

ChemElectroChem • 2021

Abstract The anode characteristics are important to the performance of microbial fuel cells (MFCs) due to the influence on biofilm formation and extracellular electron transfer (EET). Herein, MXene was modified by the superhydrophilic cationic polymer poly(diallyldimethylammonium chloride) (PDDA) to prepare MXene@PDDA/carbon cloth (CC) as an anode for MFC. The unique anode combines the advantages of high conductivity, superhydrophilicity and biocompatibility. The anode facilitated the enrichment of electrochemical active bacterium and promoted the efficiency of EET process. MFCs based on MXene@PDDA/CC anodes can produce the remarkable power output and the satisfactory effluent removal efficiency through an external series resistance of 1 kΩ. The device can gain the maximum output voltage of 585 mV and the maximum power density of 811 mW m −2 . The efficiencies of methyl orange decolorization and chemical oxygen demand were up to 79 % and 84 % after 12 h, respectively.

Tafadzwa Portia Mahurede, Chido Hermes Chihobo, Beaven Utete et al.

Fuel Communications • 2023

Ali Rezaei, Soheil Aber, Elnaz Asghari et al.

Fuel • 2024

Zahra Khaksar, Maryam Farahmand Habibi, Majid Arvand et al.

Fuel • 2024

Aisha Salisu Buhari, Hindatu Yusuf

International Journal of Science and Research (IJSR) • 2024