African Journal of Advances in Science and Technology Research

Evaluation of Bacteriological Quality and Proximate Composition of African Giant Snail (Archachatina Marginata) Sold in Port Harcourt, Nigeria

Victoria Daminabo — 2023-06-30

Snails are highly perishable and prone to vast variations in quality due to differences in species, as well as environmental and feeding habits. This study, therefore, seeks to evaluate the bacteriological and proximate composition of Snails (Archachatina marginata) sold in markets around Port Harcourt. In this study, a total number of sixty (60) samples were collected from three markets: Creek Road, Mile One and Rumuokoro. The samples were collected using sterile bags properly labelled according to standard microbiological procedures. Standard analytical procedures were employed to determine the bacteriological characteristics and proximate composition of the various parts such as flesh, intestine, and fluid of the samples. Statistical analyses were carried out using ANOVA and All Pairs Turkey-Kramer. Results obtained from the study showed that Total heterotrophic bacteria counts of the snail parts from the three sampled markets ranged from 3.7 ±0.52x10⁶ cfu/ml (fluid) to 8.6 ±1.24x10⁶ cfu/g (Intestine)., total coliform count ranged from 0.6 ±0.31x10⁴ cfu/ml (fluid) to 4.3 ±1.1x10⁴cfu/g (Intestine)., Listeria count ranged from 0.6 ±0.34x10⁴cfu/ml to 1.9 ±0.55x10⁴cfu/g while Salmonella counts ranged from 0.4 ±0 x10⁴cfu/ml to 3.6 ±1.58x10⁴ cfu/g. The mean values for all the microbial counts were significantly different (P<0.05) in the three samples across the sampled markets. Using genomic identification technique Listeria species isolated and identified in this study include; L.monocytogenes, L.grayi, L. seeligeri, L.ivanovii, and L.welshmeri while species of Salmonella include S. enterica and bongori. Other bacterial isolates were identified as Vibrio spp, Bacillus spp Staphylococcus spp Shigella spp Pseudomonas spp. Enterobacter spp. E. coli, Micrococcus spp. Acinetobacter spp. Klebsiella spp. Results of proximate analyses revealed that the protein content of the snail was relatively good which makes it a good alternative source of protein. This study revealed that the meat of snails traditionally sold in the city of Port Harcourt were of poor microbiological quality, The presence of pathogenic organisms isolated in this study is of public health concern as these organisms are known causes of food-borne diseases. Therefore, awareness of public health implications of undercooked food samples such as snails should be emphasized via television, radio stations and schools.

Probabilistic Risk Assessment of Canned Meat Consumptions in Bayelsa State, Nigeria

Abinotami Williams Ebuete — 2023-06-30

Food of animal origin provides important nutrients (protein, zinc, iron, selenium, vitamins and phosphorus) that constituted a well-balanced diet. The meat food industry employs numerous technologies such as canning to prolong shelf life, ease distribution and preserved quality. Unfortunately, the meat canning process has raised public health concerns over the safety and quality of the products due to the exposure of man to toxic elements (heavy metals). This survey determines the content of Cadmium, Chromium, Copper, Iron, Tin, Lead, Mercury, Manganese, Nickel and Zinc in canned meat by means of ICP-MS apparatus and mercury analyzer; perform Probabilistic Risk Assessment (non-carcinogenic) using Tolerable Daily Intake (TDI); Target Hazard Quotient (THQ) and Health Risk Index (HRI)mg/kg-bw/day. The analysis shows that Sn, Hg and Fe were below recommended limits; Cd and Cu were also within limits except in Costa and Exeter products while Cr, Pb, Mn, Ni and Zn were above recommended limits in all products. The TDI recorded nine elements (Cd,Cr,Cu,Fe,Pb,Mn,Ni,Sn and Zn)that were beyond recommended Oral Reference Dose (RfD) in different canned meat. The THQ revealed Cu and Ni above recommended RfD values (4.0E^-2 and 2.0E^-1) which posed carcinogenic risk effects on the use; however, the overall HRI values of all the heavy metals were below the limits (<1), indicating non-carcinogenic health effects on the exposed populations. Therefore, the consumption of this product should be limited to avert future health risks.

Mutagenic Effects of Borassus Aethiopum (Mart) Placenta Ash Extracts on the Root Tips of Allium Cepa (L)

M. Ibrahim — 2023-07-31

This study investigates the Mutagenic Effects of Borassus aethiopum (Mart) Placenta Ash Extracts on the Root Tips of Allium cepa (L). The aim of the study was to determine the mutagenic effects of Borassus aethiopium placenta ash water extracts on onion (Allim cepa) root tips. The seeds of A. cepa were obtained locally from Jimeta main market. The Seeds were dried in the laboratory. Ash extraction was prepared using the traditional method practiced by the Kilba, Marghi and Bura people, with some modification. The seeds selected were soaked for 48 hours. Root tips were harvested when the roots are about 2.5 cm long. The harvested root tips were immediately dropped into a solution of 0.05% colchicine for 5 hours, at the end of which the root tips were washed thoroughly in running tape water and placed in glacial acetic alcohol 1:3 v/v for 24 hours for fixation. The root tips were then hydrolysed in a water bath at 60 degrees. Allium test carried out on different concentrations of raw Borassus aethiopum Placenta Ash water extract revealed induction of chromosomal aberrations in the genome of Allium cepa. Chromosomal aberrations such as sickness, fragmentation, break, laggards, dissolution, Chromosome gap, Vagrant metaphase and Agglutination were observed in onion roots tips treated with 0.5, 0.75, 1.0 and 1.25 g/ml concentrations of the Borassus aethiopum Placenta Ash water extract. Highest percentages (16.6%) of chromosomal aberrations were recorded for 1.25 g/ml extract, while 0.75g/ml of it gave the lowest percentage (7.3%). No chromosomal aberration was observed in the control and 0.5g/mils concentration. Observations made in this study call for caution in the consumption of raw Borassus aethiopum Placenta Ash water for treatment of ailment. Low concentration (0.5g/ml) and wide spacing of dosage are therefore suggested for the use of Borassus aethiopum Placenta Ash water extract in traditional medicine and soup preparation in order to prevent the risk of genetic accidents (mutation).

A Triple Phase Hybrid Security Model for Cloud Storage Using Advanced Encryption Standard

Solomon Tashara — 2023-07-31

Cloud storage is an integral part of modern computing; however, the associated data security concerns have hindered its widespread adoption. This paper proposes a triple-phase hybrid security model for cloud storage using Advanced Encryption Standard (AES) that can address these security concerns. In the first phase, the AES algorithm is used to encrypt the data using a shared key between the user and the cloud server. In the second phase, a data integrity verification algorithm is applied to guarantee that the data being stored in the cloud are not tampered with. Lastly, a transmission security layer is employed to protect data transmissions between the user and the cloud server. The data is encrypted using AES during the encryption process using a 16-bit key. The encrypted data is inserted into a cover image during the steganography stage. The data is once more encrypted using AES and stored in the cloud during the hybrid phase. The effectiveness of the suggested model was assessed using a variety of security measures. The proposed triple-phase hybrid security model is evaluated in an extensive security experiment. The results demonstrate that the proposed security model is able to effectively protect data stored in the cloud from threats such as unauthorized access, data manipulation and data leakage. Furthermore, the proposed model is also able to effectively minimize the transmission overhead and reduce the total computational cost required for data encryption and decryption.

Optimized Rivest, Shamir and Adleman (RSA) for Network Inter-Layer Communication

Peter Ezekiel — 2023-07-31

This paper examines the use of Optimized Rivest, Shamir and Adleman (RSA) for Network Inter-Layer Communication. RSA is an algorithm used for secure data transmission which is based on the difficulty of factoring large prime numbers. It is an efficient public-key cryptography method used for authenticating messages and digital signatures. We examine the performance of RSA in network inter-layer communication and compare it to other methods. To achieve this appropriate key sizes of AES- 256 bits to balance security and performance were chosen for a good balance between security and performance. Key Generation, load balancing and performance monitoring were also considered to fine-tune the implementation using this optimization technique. Our results show that RSA performs well in terms of both security and performance, making it a viable choice for secure network inter-layer communication. The computational complexity of RSA encryption is greatly reduced, according to simulation results, using the suggested approach. Overall, the suggested method enhances the RSA cryptographic protocol's functionality when used for inter-layer communication in networked systems. The findings demonstrated that RSA had a faster encryption time than AES, leading to a high throughput for the RSA approach.

Comparison and Analysis of Data Mining Techniques for Intrusion Detection

Ibrahim Adamu — 2023-07-31

This research investigates the use of data mining techniques for intrusion detection. Decision Trees, Artificial Neural Network, Naïve Bayes and Support Vector Machine are the strategies examined. The dataset used was collected from different online repositories where they are made available as open-source data. The sample size used were 910, 10077, 10679 instances of KDDCUP’99, NSL-KDD-train and CICIDS2017 respectively, selected using random sampling technique, The Weka workbench tool was used for preprocessing and creation of the intrusion detection system. The accuracy, speed, and scalability of the techniques, among other criteria, are taken into account and contrasted. The study also examines the best method for detecting intrusions in dynamic networks and various applications. The performance of each technique in terms of accuracy, precision, recall, and F1-score is also examined in this research. The results revealed that the Decision Tree performs better with accuracy, precision, recall and F-measure of 99% than the other classifiers Support Vector Machine, ANN, NB and KNN in most of the tests on the three different datasets. Both Decision Tree and ANN classifier showed superior performance in detecting attacks. In conclusion, this paper reveals that artificial neural networks are the most accurate data mining technique for intrusion detection. However, in terms of implementation, Decision Tree classifier take a very short time to implement compared to ANN which takes a very long time to be implemented. Finally, this research recommended that employing these optimizing techniques to develop an intrusion detection model has a better accuracy rate.

Prevalence of Entamoeba Histolytica among Patients Attending Federal Polytechnic Clinic in Mubi, Adamawa State

Yakubu Mohammed Sani — 2023-07-31

This study was undertaken to determine the prevalence Of Entamoeba histolytica among patients attending the Federal polytechnic clinic, Mubi, Adamawa State. The sample was collected randomly with the biodata of the patients, such as age, sex and occupation noted. The samples were examined microscopically using X10 objective lens and then X40 for confirmation of the parasite. From the result obtained, it shows that amoebiasis is more prevalent among children than adults. This might be as a result of poor personal hygiene among children than adults. Based on residence, the total number of people that were affected with the parasite in the rural areas was 66(64.7%) while in the urban areas, there were 21 (43.8%) positive samples. This indicates that the prevalence of amoebiasis (amoebic dysentery) was found to be more prevalent among rural dwellers than in the urban areas.

Hybrid Diagnostic Model for Kidney Disease Prediction Using Data Mining Techniques

Usa John Guli — 2023-07-31

This paper proposes and evaluates a hybrid diagnostic model for kidney disease prediction using data mining techniques, as a potential solution to the current problems facing existing models. The model is based on a combination of Artificial Neural networks, and Support Vector Machines algorithms, trained on the symptoms and patient-reported data. The dataset contains 400 instances which are based on 25 attributes, retrieved from the University of California, Irvine machine learning repository chronic_Kidney_Disease Dataset. WEKA toolkit was used to preprocess the dataset, apply the data mining algorithms, analysis and evaluation. The evaluation of the model using a 10-fold cross-validation technique shows that the hybrid model outperforms both individual ANN and SVM algorithms in terms of accuracy and stability. Also, it shows that the hybrid of ANN-SVM has the lowest MAE, RMSE, and RRSE in classifying the CKD dataset, followed by ANN having lower MAE compared to ANN, but ANN had lower RMSE, RAE, and RRSE than the SVM. The model described in this paper is an efficient tool for predicting kidney disease, as it can be used to diagnose a variety of kidney diseases and can potentially reduce the number of unnecessary tests that are currently conducted to diagnose kidney disease. In addition, the model can be used to ensure that patients receive the most appropriate medical screenings and treatments for kidney diseases. In conclusion, the proposed hybrid diagnostic model for kidney disease prediction using data mining techniques has shown that it is a viable and promising alternative to existing methods.

Wireless Body Sensor Network Applied too Blood Pressure Monitoring System

Aminu Aminu Yakubu — 2023-08-04

Continuous and accurate blood pressure monitoring is crucial for the early detection and management of cardiovascular diseases. Wireless Body Sensor Networks (WBSNs) have emerged as a promising technology for non-invasive and continuous blood pressure monitoring, offering numerous advantages over traditional cuff-based methods. This abstract presents a comprehensive overview of the application of WBSNs in blood pressure monitoring systems, focusing on sensor technology, data transmission, signal processing, power management, security, and clinical applications. Sensor technology in WBSNs encompasses various techniques, such as photoplethysmography (PPG) and oscillometric methods, enabling the measurement of blood pressure with acceptable accuracy. The collected physiological data are wirelessly transmitted to a central processing unit, which employs advanced signal processing algorithms to filter noise and motion artifacts, ensuring reliable blood pressure readings. Efficient power management techniques play a vital role in extending the battery life of wearable devices, enabling continuous monitoring without frequent recharging. Low-power communication protocols like Bluetooth Low Energy (BLE) and Zigbee facilitate seamless data transmission while minimizing energy consumption. Data security and privacy are paramount in healthcare applications. Encryption, secure communication, and user authentication measures are employed to protect sensitive patient data from unauthorized access. Validation studies and clinical trials have demonstrated the reliability and accuracy of WBSNs for blood pressure monitoring. The continuous and non-invasive nature of WBSNs allows for remote monitoring and telemedicine applications, empowering healthcare providers to remotely monitor patients and intervene promptly when necessary. Moreover, the integration of WBSNs with electronic health records (EHRs) and telehealth platforms streamlines data sharing and enables personalized healthcare interventions. In conclusion, the application of Wireless Body Sensor Networks to blood pressure monitoring holds significant promise in revolutionizing healthcare practices. With ongoing advancements in sensor technology, data analysis, power efficiency, and security measures, WBSNs offer the potential to improve patient outcomes and enhance cardiovascular health management.