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Biomedical Mammography Image Classification Using Patches-Based Feature Engineering with Deep Learning and Ensemble Classifier
In order to reduce the expense of radiologists, deep learning algorithms have recently been used in the mammograms screening field. Deep learning-based methods, like a Convolutional Neural Network (CNN), are now being used to categorize breast lumps. When it involves classifying mammogram imagery, CNN-based systems clearly outperform machine learning-based systems, but they do have certain disadvantages as well. Additional challenges include a dearth of knowledge on feature engineering and the impossibility of feature analysis for the existing patches of pictures, which are challenging to distinguish in low-contrast mammograms. Inaccurate patch assessments, higher calculation costs, inaccurate patch examinations, and non-recovered patched intensity variation are all results of mammogram image patches. This led to evidence that a CNN-based technique for identifying breast masses had poor classification accuracy. Deep Learning-Based Featured Reconstruction is a novel breast mass classification technique that boosts precision on low-contrast pictures (DFN). This system uses random forest boosting techniques together with CNN architectures like VGG 16 and Resnet 50 to characterize breast masses. Using two publicly accessible datasets of mammographic images, the suggested DFN approach is also contrasted with modern classification methods. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Biomass-derived N-doped carbon to anchor bimetallic-phospho boride for hydrogen evolution from alkaline seawater
Seawater electrolysis offers a sustainable pathway for hydrogen production, but is hindered by the limited activity and stability of electrocatalysts, with Pt-based materials being highly active yet costly and scarce. To address these issues, we synthesize nitrogen-doped carbon (NC) via a solvent-free method from golden shower biomass. NC is integrated with CoMoPB catalysts using a facile chemical reduction process. The resulting CoMoPB/NC catalyst exhibited superior HER activity, achieving a low overpotential of 34 mV at 10 mA/cm2 in alkaline natural seawater, outperforming the commercial Pt/C catalyst under similar conditions. The CoMoPB/NC catalyst demonstrated considerable stability at ?500 mA/cm2 for 100 h and showed strong HER performance in seawater electrolyzers, reaching ?1.98 V at 500 mA/cm2. This study explores the potential of biomass-derived catalysts to rival and surpass commercial noble metal-based systems, offering a cost-effective and sustainable solution for industrial-scale seawater electrolysis and renewable energy applications. 2025 Elsevier Ltd -
Biomass-derived carbonaceous materials: Synthesis and photocatalytic applications /
Novel Applications of Carbon Based Nano-materials, 1st ed., pp.412-429, eBook ISBN : 9781003183549. -
Biomass-derived carbonaceous materials: Synthesis and photocatalytic applications
[No abstract available] -
Biomass-derived carbon supported cobalt-phospho-boride as a bifunctional electrocatalyst for enhanced alkaline water splitting
Developing efficient and low-cost bifunctional electrocatalysts for overall water splitting in order to reduce the future energy crisis is crucial and challenging. Herein, a facile two-step fabrication via pyrolysis and chemical reduction was used for the synthesis of biomass-derived carbon-based electrocatalyst (MT) from mulberry bark and its subsequent modification with cobalt phospho-boride (MT/CoPB) for efficient bifunctional electrocatalysis in alkaline media. The effect of B/P ratios and carbon-to-metal ratios on electrocatalytic performance of HER was investigated. Notably, the optimized MT/CoPB catalyst (B/P = 5, C : M = 2 : 1) exhibited a lower overpotential of ?86 mV for HER and 310 mV for OER to reach the current density of 10 mA cm?2. The robust electrocatalytic performance of MT/CoPB towards the HER and OER was attributed to the combined effect of carbon and CoPB. Notably, it achieved a low cell voltage of 1.59 V to reach a current density of 10 mA cm?2, also maintaining reliable long-term stability. Characterization studies revealed that the enhanced performance was due to the amorphous structure of the catalyst, high electrochemical surface area, and efficient charge transfer. This work demonstrates the potential of biomass-derived carbon-based materials in the development of cost-effective and durable electrocatalysts for water splitting and green hydrogen production. 2025 RSC. -
Biomass-Derived Carbon Materials in Heterogeneous Catalysis: A Step towards Sustainable Future
Biomass-derived carbons are emerging materials with a wide range of catalytic properties, such as large surface area and porosity, which make them ideal candidates to be used as heterogeneous catalysts and catalytic supports. Their unique physical and chemical properties, such as their tunable surface, chemical inertness, and hydrophobicity, along with being environmentally friendly and cost effective, give them an edge over other catalysts. The biomass-derived carbon materials are compatible with a wide range of reactions including organic transformations, electrocatalytic reactions, and photocatalytic reactions. This review discusses the uses of materials produced from biomass in the realm of heterogeneous catalysis, highlighting the different types of carbon materials derived from biomass that are potential catalysts, and the importance and unique properties of heterogeneous catalysts with different preparation methods are summarized. Furthermore, this review article presents the relevant work carried out in recent years where unique biomass-derived materials are used as heterogeneous catalysts and their contribution to the field of catalysis. The challenges and potential prospects of heterogeneous catalysis are also discussed. 2022 by the authors. -
Biomass-Based Functional Carbon Nanostructures for Supercapacitors
For the creation of next-generation biocompatible energy technologies, it is urgently necessary to examine environmentally acceptable, low-cost electrode materials with high adsorption, rapid ion/electron transit, and programmable surface chemistry. Because of their wide availability, environmentally friendly nature, and affordability, carbon electrode materials made from biomass have received a lot of interest lately. The biological structures they naturally possess are regular and accurate, and they can be used as templates to create electrode materials with precise geometries. The current study is primarily concerned with recent developments in research pertaining to biomass-derived carbon electrode materials for supercapacitor applications, including plant, fruit, vegetable, and microorganism-based carbon electrode materials. Also provided is a summary of alternative synthesis methods for the conversion and activation of biomass waste. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Biomass- or Biowaste-Derived Carbon Nanoparticles as Promising Materials for Electrochemical Sensing Applications
Modern human lifestyle incorporates the use of sensors to a great extent. Electrochemical sensors are the oldest and most commonly studied type of sensor with a wide commercial usage and numerous possibilities. Porous carbons are an important class of electrode materials and have a number of benefits compared to other materials in terms of sensor fabrications. Biomass pyrolysis and hydrothermal carbonization are important techniques to synthesize cost-effective, cheap, and more environmentally friendly porous carbon nanomaterials with higher electrocatalytic efficiency, selectivity, and sensitivity and better detection limits. The surface area of hierarchical porous architecture along with the graphitic nature of bio-derived carbon materials greatly affects the performance of electrochemical sensors. Numerous techniques are performed to improve the surface properties such as activation, doping, etc., in order to enhance the electrocatalytic behavior of working electrodes. The carbon materials discussed here are promising candidates as an effective alternative to many commercial electrochemical sensors. 2022 WILEY-VCH GmbH, Boschstra 12, 69469 Weinheim, Germany. All rights reserved -
Biomass Derived Fluorescent Nanocarbon Sensor for Effective Sensing of Toxic Cadmium Metal Ions
Cadmium ion (Cd2+) is common in our surroundings and may readily bioaccumulate into the organism following passage through the respiratory and digestive systems. Chronic exposure to Cd2+ can lead to considerable bioaccumulation in an organism because of its longer biological high life (1030 years), which permanently harms the health of humans and animals. Considering this hazardous effect of toxic Cd2+ metal ions, there is a need to develop a toxic-free and simple sensor synthesized from easily available and biocompatible biomass or natural precursor. Herein we report the effective synthesis and development of a fluorescence sensor from Indigofera tinctoria (L.), a well-known medicinal plant via one step green, hydrothermal synthesis method. The remarkable fluorescence and larger stokes shift make it ideal for fluorescence sensing strategy. This sensor detects potentially toxic Cd2+ assisting fluorescence sensing strategy in the metal ion concentration range from 1 nM to 1 M. The SternVolmer plot exhibits a remarkable linear detection range exhibiting limit of detection (LOD) as 14.74 nM. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Biomass derived carbon quantum dots embedded PEDOT/CFP electrode for the electrochemical detection of phloroglucinol
Carbon nanocomposites have garnered a lot of attention among various nanomaterials due to their distinct characteristics, such as large surface area, biocompatibility, and concise synthetic routes. They are also a viable contender for electrochemical applications, notably sensing, due to their intriguing electrochemical features, which include large electroactive surface area, outstanding electrical conductivity, electrocatalytic activity, and high porosity and adsorption capability. Herein, an electrochemical sensor for phloroglucinol (PL) was designed using a CFP electrode modified with biomass-derived carbon quantum dots (S-CQD) doped on conducting organic polymer poly(3,4-ethylene dioxythiophene) (PEDOT) via electrodeposition method. The obtained nanocomposite (S-CQD+PEDOT) on the CFP electrode possesses a high surface area. The higher electrocatalytic activity of S-CQD and significant conductivity of PEDOT- modified electrode enhance the electrocatalytic activity for the phloroglucinol oxidation. The oxidation peak current of PL shows a higher response on the finally modified electrode than the other electrodes. The developed electrochemical sensor for the selective and sensitive detection of PL showed a good linear range of 36 -360 nM and a detection limit of 11 nM. The modified electrodes were characterized using Transmission electron spectroscopy (TEM), Fourier Transform infrared spectroscopy (FT-IR), and X-ray photon spectroscopy (XPS). Finally, the developed method was successfully used to detect Phloroglucinol from industrial effluents with RSD (0.841.02%) and (98.5101.2%) of recovery. 2023 -
Biomass Carbon Dots: Illuminating New Era in Antimicrobial Defense and Cancer Combat
The twenty-first century has witnessed remarkable advancements across diverse facets of human life, including significant progress in the medical field, economic growth, scientific breakthroughs, and technological advancements. Despite these strides that improved living standards, the persistent threat posed by pathogenic infections caused by bacteria, fungi, viruses, etc., remains a critical concern. The enduring emergence of new variations of these infections continues to impact lives profoundly. Cancer is another looming spectre that continues to challenge human health security. Consequently, extensive research endeavours aim to develop swift, efficient, and innocuous methods for curing and preventing these infections. This paper explores a burgeoning field in physics, focusing on recent advancements in nanomaterials, particularly in developing carbon dots (CDs). Characterized by their size, which is less than 10nm, CDs have proven exceptionally beneficial in diagnosing and treating life-threatening health issues while preserving the viability of healthy cells. Their versatility is evident in various biomedical applications, serving as bioimaging probes, intracellular drug delivery agents, and agents for bactericidal and fungicidal, as well as in cancer treatment and diagnosis. The key attributes contributing to their efficacy include ease of functionalization, biocompatibility, fluorescence, low cytotoxicity, and catalytic properties. As an innovative nanomaterial, CDs showcase tremendous potential in advancing medical diagnostics and therapeutics, offering a glimpse into a future where these tiny entities play a pivotal role in ensuring human well-being. This review focuses on the antibacterial, antifungal, antiviral, and anticancerous activities of the CDs derived from various precursors derived by biomass. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Biomarkers of Autistic Study : Biochemical, Genomics, Epigenetics and Cytogenetic Signatures
Autism is a complex disorder characterized by social issues, impaired communication, newlineand repetitive behavior. The prevalence of autism has increased significantly over the past two decades, with an estimated incidence of 1 in 150 children in 2000. Cytogenetic investigations are essential for confirming clinical diagnoses, as the disorder has high phenotypic variability and genetic heterogeneity. A study aims to confirm behavioral phenotypes of autistic subjects newlinein South India using DSM IV and ATEC open questionnaires. The study found that metabolic factors, including hormones, neurotransmitters, and oxidative ions, play crucial roles in the progression of symptoms. The study also revealed the roles of two major causative genes (NRXN1 and CNTNAP2) in a spectrum of genotypes imparting severity and heterogeneity. -
Biomarker study of the biological parameter and neurotransmitter levels in autistics
Autism is a prevalent developmental disorder that combines repetitive behaviours, social deficits and language abnormalities. The present study aims to assess the autistic subjects using DSM IV-TR criteria followed with the analysis of neurotransmitters, biochemical parameters, oxidative stress and its ions in two groups of autistic subjects (group I < 12years; group II ? 12years). Antioxidants show a variation of 10% increase in controls compared to autistic age < 12years. The concentration of pyruvate kinase and hexokinase is elevated in controls approximately 60% and 45%, respectively, with the significance of 95 and 99%. Autistic subjects showed marked variation in levels of neurotransmitters, oxidative stress and its related ions. Cumulative assessment of parameters related to biochemical markers and neurotransmitters paves the way for autism-based research, although these observations draw interest in an integrated approach for autism. 2020, Springer Science+Business Media, LLC, part of Springer Nature. -
Biological treatment solutions using bioreactors for environmental contaminants from industrial waste water
Human needs have led to the development of various products which are produced in the industries. These industries in turn have become a source of various environmental concerns. As industries release regulated and unregulated contaminants into the water bodies, it has become a serious concern for all living organisms. Various emerging contaminates from industries like pesticides, pharmaceuticals drugs like hormones, antibiotics, dyes, etc., along with byproducts and new complexes contaminate the water bodies. Numerous traditional approaches have been utilized for the treatment of these pollutants; however, these technologies are not efficient in most cases as the contaminants are mixed with complex structures or as new substances. Advanced technologies such as bioreactor techniques, advanced oxidation processes, and so on have been used for the treatment of industrial wastewater and have served as an alternative way for wastewater treatment. Overall, biological treatment techniques based on bioreactors provide a long-term and ecologically useful solution to industrial wastewater contamination. They play an important role in saving water resources and encouraging a greener sustainable future for mankind. The current review outlines the industrial effluents that are released into water bodies, contaminating them, as well as the numerous traditional and novel treatment procedures used for industrial wastewater treatment. Graphical abstract: [Figure not available: see fulltext.] 2023, The Author(s). -
Biological Feature Selection and Classification Techniques for Intrusion Detection on BAT
Privacy is a significant problem in communications networks. As a factor, trustworthy knowledge sharing in computer networks is essential. Intrusion Detection Systems consist of security tools frequently used in communication networks to monitor, detect, and effectively respond to abnormal network activity. We integrate current technologies in this paper to develop an anomaly-based Intrusion Detection System. Machine Learning methods have progressively featured to enhance intelligent Anomaly Detection Systems capable of identifying new attacks. Thus, this evidence demonstrates a novel approach for intrusion detection introduced by training an artificial neural network with an optimized Bat algorithm. An essential task of an Intrusion Detection System is to maintain the highest quality and eliminate irrelevant characteristics from the attack. The recommended BAT algorithm is used to select the 41 best features to address this problem. Machine Learning based SVM classifier is used for identifying the False Detection Rate. The design is being verified using the KDD99 dataset benchmark. Our solution optimizes the standard SVM classifier. We attain optimal measures for abnormal behavior, including 97.2 %, the attack detection rate is 97.40 %, and a false-positive rate of 0.029 %. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Biological extraction of chitin from fish scale waste using proteolytic bacteria Stenotrophomonas koreensis and its possible application as an active packaging material
Chitin being the second most abundant polymer found in nature has extensive application and versatile material properties including biocompatibility. Extraction of chitin from diverse sources are majorly done using chemical extraction methods using high concentration of alkali that makes the method non eco-friendly and economically non-viable. This calls for eco-friendly methods of chitin extraction from cost-effective substrates through green methods. This research work presents a simplified one-step biological extraction of chitin from fish scales by successive fermentation using Stenotrophomonas koreensis isolated from soil. The fermentative approach for chitin extraction from fish scales using S. koreensis enzyme activity is not reported elsewhere in the available literature to the best of our knowledge. Chitin yield of 28% (w/w) was obtained after the successive fermentation. The extracted polymer was characterized using differential scanning calorimetry (DSC), Fourier transform infrared (FTIR), X-ray diffraction (XRD), and thermo gravimetric analysis (TGA). Furthermore, the possibility of converting extracted chitin into an active packaging material was explored by chemically, converting it to chitosan followed by analysis of its DPPH scavenging activity. The DPPH radical scavenging activity varied from 67.025 to 80.2%, which corresponds to 0.25 to 2mg/mL of chitosan. The chitosan films fabricated were subjected to biodegradation studies using soil burial method. Biodegradation rate of chitosan films was observed to be 21.49 0.62% (w/w) after 50days of incubation. Thus, the present research work highlights an integrated waste valorization strategy through microbial fermentation for commercially important biopolymer production. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. -
Biological elimination of minerals from high ash coal by Aspergillus-like fungi
Efficiency of filamentous fungi such as Aspergillus niger on the bio-liquefaction of low rank Indian coals, its chemical composition, surface characteristics of the products and the microbial mechanisms of coal conversion were studied. Virgin and bio-liquefied/solubilized coal samples were characterized using FT-IR, Scanning electron microscopy and CHNS and proximate analysis. The micrographs were bright field and reveal several features correspond to the mineral grains comprising of aluminium, silicates and calcites. The absence of some morphological features corresponds to inorganic elements in residual samples which confirm demineralisation with the possible formation of respective Aluminum and Silicate complexes. The change in absorption of mineral matter functional group of these coal samples were studied using Fourier transform infra red spectroscopy (FT-IR). From the proximate analysis it was found that the ash content decreased by 76% when treated with fungal culture. Global Science Publications. -
Biological and Environmental Applications of Myco-synthesized Titanium Dioxide Nanoparticles
In the present investigation, TiO2NPs were myco-synthesized through the extracellular enzymes of endophytic fungi, Aspergillus versicolor FCPRS11 isolated from stem of Azadirachta indica. The synthesized TiO2NPs was characterized using UV- Vis, FT-IR, SEM, XRD, EDX, DLS and Zeta Potential. The synthesized TiO2NPs were analyzed for their antimicrobicidal properties against five clinical pathogens with two fungal pathogens and were exhibiting significant inhibition towards the bacteria at minimum concentration of 50g/ml of TiO2NPs. The free radical scavenging mechanism of the synthesized TiO2NPs was monitored through various assessment to understand about NPs antioxidant properties and the IC50 values were compared with the IC50 value of standard ascorbic acid (91g/ml). Further the NPs were analyzed for in vitro anti- inflammatory property exhibiting 73.87% inhibition and anti- diabetic properties (62.18% inhibition) proving that TiO2NPs evinced a promising biomedical activity. The larger surface area of the synthesized TiO2NPs as per the SEM analysis, allowed evaluation of their adsorption capacity on soil collected from metallurgical site containing combination of heavy metals and contaminants. The results of adsorption studies demonstrated that the adsorption increases with increase in time of exposure of TiO2NPs and adsorption capacity was determined by employing Langmuir model. The % dye degradation was evaluated by photocatalytic dye degradation studies where at 100mg/L of TiO2NPs, over 91% dye was found to be degraded within 240mins.These findings highlight the potential of myco-synthesized TiO?NPs as effective agents for biomedical applications and environmental nanoremediation. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Bioinformatics Tools and Deep Learning for Plant High-Throughput Phenotyping and Phenomics
High-throughput phenotyping and phenomics are essential for advancing plant research and improving crop performance. The integration of bioinformatics tools and deep learning methodologies has transformed the way data is processed and analyzed in these fields. Bioinformatics tools facilitate the management and interpretation of large-scale genomic and phenotypic data, enabling researchers to extract valuable insights. Deep learning algorithms, particularly convolutional neural networks, have shown significant promise in automating the analysis of complex plant images and enhancing trait identification and prediction. This synergy between bioinformatics and deep learning accelerates the identification of key traits, improves the precision of phenotypic assessments, and supports the development of more resilient and productive crops. This chapter highlights how these advanced technologies contribute to more effective and scalable plant phenotyping and phenomics efforts. 2025 selection and editorial matter, Jen-Tsung Chen; individual chapters, the contributors. -
Bioinformatics Research Challenges and Opportunities in Machine Learning
This research work has studied about the utilization of machine learning algorithms in bioinformatics. The primary purpose of studying this is to understand bioinformatics and different machine algorithms which are used to analyze the biological data present with us. This research study discusses about different machine learning approaches like supervised, unsupervised, and reinforcement which play an essential role in understanding and analyzing biological data. Machine learning is helping us to solve a wide range of bioinformatics problems by describing a wide range of genomics sequences and analyzing vast amounts of genomic data. One of the biggest real-world problems is that machine learning is helping us to identify cancer with a given gene expression, which is done using a support vector machine. In addition, this study discusses about the classification of molecular data, which will help find out minor diseases. With the advancement of machine learning in healthcare and other related applications, data collection becomes a tedious process. This article also focuses on some of the research problems in machine learning domain. The uses of machine learning algorithms in bioinformatics have been extensively studied. These objectives will help to understand bioinformatics and different machine algorithms that are used to analyze the biological data. This research study presents different machine learning approaches like supervised, unsupervised, and reinforcement, which play an important role in understanding and analyzing biological data. Machine learning helps to solve a wide range of bioinformatics related challenges by describing a wide range of genomics sequences and analyzing huge amounts of genomic data. One of the biggest real-time challenges is that the machine learning is helping to identify cancer with a given gene expression, and this is done by using a support vector machine. Finally, this research study has discussed about the classification of molecular data, which will be helpful in finding out minor diseases. 2022 IEEE.