FACULTY OF PHYSICAL SCIENCE

Phishing attacks are one of the most common and dangerous cybersecurity threat today, with attackers using techniques that are getting sophisticated daily to deceive users and get access to their sensitive information. This study shows the development and implementation of a machine learning based phishing URL detection tool designed to identify malicious URLs and do so with high accuracy. This research addresses the growing challenge of detecting phishing websites by analyzing URL characteristics and patterns that distinguish legitimate sites from fraudulent ones. Utilizing a comprehensive dataset of over 10,000 URLs (comprising both phishing and legitimate websites), this study implements multiple machine learning algorithms including Random Forest, Support Vector Machines (SVM), and Gradient Boosting to classify URLs. The system extracts 30 distinct features from URLs, including lexical properties, domain-based characteristics, and third-party service indicators. Feature engineering techniques were applied to optimize model performance, with priority given to handling imbalanced datasets through Synthetic Minority Over-sampling Technique(SMOTE ). The results shows that the Random Forest classifier achieved the highest accuracy of 96.8%, with precision and recall scores of 95.2% and 97.1% respectively. The Gradient Boosting model closely followed with 95.9% accuracy, while the SVM model achieved 92.4% accuracy. Cross-validation techniques were used to make sure the model is robust and prevent overfitting. Feature importance analysis revealed that URL length, presence of suspicious keywords, domain age, and SSL certificate status were among the most significant predictors of phishing attempts. To validate practical applicability, a web-based detection tool was developed using Flask framework, enabling real-time URL scanning and classification. The system incorporates a user-friendly interface that provides instant feedback on URL legitimacy, along with detailed risk analysis and security recommendations. Performance testing also verified an average response time below 200 milliseconds per analysis for URL, making the tool practical for real-world deployment. This research contributes to the study of cybersecurity with the presentation of an efficient, automated phishing detection system that can be employed with web browsers, email clients, or independently

This study focuses on the development and characterization of biodegradable packaging films produced from acetylated cassava starch reinforced with kaolinite and plasticized with ethylene glycol. Cassava starch was extracted, chemically modified through acetylation, and confirmed using Fourier Transform Infrared (FTIR) spectroscopy. The acetylated starch spectrum displayed a distinct absorption peak around 1740 cm⁻¹, attributed to the carbonyl (C=O) stretching vibration of ester groups, confirming acetylation. An additional peak at 1230–1260 cm⁻¹ corresponds to C–O stretching of the acetyl ester linkage. The observed decrease and narrowing of the O–H stretching band intensity around 3400 cm⁻¹ reflects the replacement of hydroxyl groups by acetyl moieties, reducing intermolecular hydrogen bonding. Slight intensity variations in the 1000–1150 cm⁻¹ region also indicate modifications to the starch backbone. Acetylation lowered the gelatinization temperature from 65 °C to approximately 51 °C, indicating reduced structural order and enhanced thermal processability. Bioplastic films were formulated using varying concentrations of ethylene glycol (2–5 g) and kaolinite filler (0–1 g) through a solution casting technique. The physicochemical analysis shows that the extracted starch had a high yield (62.3%), a moisture content of 12.3%, and a gelatinization temperature consistent with high-quality cassava starch. Film characterization revealed notable variations in colour, thickness, solubility, and absorption behaviour across formulations. Water, moisture, and acid absorption increased with higher plasticizer content (2g – 5g), while kaolinite reduced uptake due to its barrier-enhancing layered structure. All samples were soluble in 1 M NaOH, confirming the susceptibility of acetate ester linkages to alkaline hydrolysis, but remained insoluble in ethanol

This study investigates the medicinal properties of the leaf extract of Morinda Citrifolia, commonly known as Noni, with a particular focus on its anti-diabetic potential, antioxidant activity, and biochemical composition. The research encompasses an in-depth analysis of the Morinda Citrifolia extract phytochemical composition, antidiabetic activity, elemental composition, heat-induced hemoglobin inhibition, and alkaloid and amino acid profiles. After
identifying bioactive components by phytochemical screening, the extract was tested in vitro for its potential as an antidiabetic and its capacity to prevent hemoglobin denaturation. In addition to
analyzing the alkaloid and amino acid profiles, elemental analysis was used to ascertain the concentration of both beneficial and detrimental components. An inhibitory assay was used to
measure the antidiabetic action, and absorbance measurements were used to identify heat-induced hemoglobin inhibition.
The extract contained alkaloids, flavonoids, tannins, saponins, glycosides, terpenoids and phenolic chemicals, according to phytochemical screening. With a maximal inhibitory activity of 49.10%
at a dosage of 0.5 mg/mL, the antidiabetic activity demonstrated a dose-dependent rise in inhibition. Significant action was shown by the heat-induced hemoglobin inhibition, which peaked
at 84.07% at 300 mg/mL. Essential elements including calcium, magnesium, zinc, and potassium were found by elemental analysis; dangerous elements like lead and chromium were not found in
any discernible amounts. The total protein or amino acid content in the extract was 111.58840, and there was 0.2 g of total alkaloids.
Morinda Citrifolia extract has significant biological properties, such as antioxidant and antidiabetic actions, as well as the capacity to prevent hemoglobin denaturation. The extract's possible therapeutic applications are further supported by the presence of vital components and bioactive phytochemicals. The presence of protein and amino acids, however, raises the plant’s nutritional and medicinal value. These results lay the groundwork for additional research into
Morinda Citrifolia’s active ingredients and potential medical uses

This study investigates the relationships between economic growth, government expenditure, and interest rates in Nigeria, employing various statistical methods. The research aims to provide actionable insights into the interactions between these crucial macroeconomic variables and their implications for policymaking. The theoretical foundation draws from Wagner's Law and the Keynesian Framework, which offer contrasting perspectives on whether government expenditure is a cause or effect of economic growth. Using data from the Central Bank of Nigeria and the World Bank, the study employs the Augmented Dickey- Fuller (ADF) unit root test to assess stationarity, the Granger causality test to examine causal relationships, and Ordinary Least Squares (OLS) regression to analyze the effects of interest rates and government expenditure on Gross Domestic Product (GDP). The findings confirm the applicability of Wagner's Law in the Nigerian context, indicating that economic growth granger-causes government spending. Furthermore, the analysis reveals a positive relationship between interest rates, government expenditure, and GDP, although the results are not statistically significant. The study highlights the importance of interest rates as a policy instrument for influencing economic performance and attracting foreign investment. To enhance the statistical robustness of the analysis, the study incorporates the Analysis of Variance (ANOVA) table, demonstrating its effectiveness in x evaluating and improving the performance of regression models. The research culminates in actionable recommendations for policymakers, emphasizing the need for strategic fiscal policies, careful interest rate management, and targeted investments in sectors that foster economic growth. Overall, this study contributes to the understanding of the intricate dynamics between economic growth, government expenditure, and interest rates in Nigeria, providing valuable insights for policymakers and researchers alike.

The primary objective of Random Walk Theory is that it suggests the changes in a
particular element e.g. stock price, having the same distribution and are independent of each other. Therefore, it assumes that the past movement or trend of maybe a stock price cannot be used to predict its future movement. Random Walk Theory have crucial point of conversation in ballot theorem, Markov process and gambler’s ruin. Though, various kind of Random Walks are of interest, which can vary in more than one way. The term itself most often refer to an extraordinary class of markov chain. Random Walk theory can actually take place in variety of spaces: usually concentrated ones include graphs, others on the integers or the real line in the plane or higher dimensional vector spaces on curved surfaces. Next we discussed the application of Random Walk Theory in relation to gambler`s ruin, we furthermore look into this and then concluded.

Emotions and views of people have become important factors in communication and decision-making in the age of computer science and information technology. Because web documents are machine-readable and easily accessible, there is a growing interest in leveraging these feelings in the context of consumer evaluations and weblogs. This increase is in line with developments in information retrieval (IR), machine learning (ML), and natural language processing (NLP), which have made accessible techniques for gathering and evaluating opinions. With an emphasis on web papers, this extensive article explores Opinion Mining and Sentiment Analysis (OMSA). It sheds light on the changing area of sentiment analysis and makes recommendations for future research directions by examining important ideas, technical developments, research obstacles, and current solutions. This study's scope is broad and includes anything from explaining basic terms to assessing the effects of technology, resolving issues, and suggesting new research directions. Its importance is extensive, as it advances knowledge in academia, real-world applications, technology, information retrieval, business and marketing tactics, and other areas. Technically speaking, this effort highlights the critical role that OMSA plays in algorithmic decision- making and computational science by providing a thorough computational exploration of the topic

This study is to assess heavy metal content in three leafy vegetables. Telfairia occidentalis (Pumpkin leaf), Amaranth green (Green leaf) and Celosia argentea (Shoko leaf). Samples were collected from Uselu market, Egor local government area of Benin city in Edo State, Nigeria in the month of February. The samples were prepared using Atomic Absorption Spectroscopy (AAS) method. The level of five different heavy metals namely Lead (Pb), Cadmium (Cd), Zinc (Zn), Chromium (Cr) and Iron (Fe) were
analyzed using Atomic Absorption Spectroscopy (AAS). Result obtained from the analysis shows that Pumpkin leaf contained a mean value of 2.00mg/kg Fe, 0.01mg/kg Cr, 2.11mg/kg Zn, while Cd and Pb were below detection limit (BDL). Shoko leaf had a mean value of 3.85mg/kg Fe, 0.04mg/kg Cr, 1.52mg/kg Zn, 0.01mg/kg Cd and Pb was below detection limit (BDL). Green leaf had a mean data of 7.70mg/kg Fe, 0.03mg/kg Cr, 1.64mg/kg Zn while Cd and Pb were below detection limit (BDL). Among
the three samples, the mean value of all the heavy metals are below standard regulatory limit of 425.50mg/kg Fe, 0.03mg/kg Cr, 99.40mg/kg Zn, 0.20mg/kg Cd and 0.30mg/kg Pb except for Cr in Shoko leaf which mean value is slightly above that of standard regulatory limit. The samples are safe for human and animal consumption, but attention should be paid to Shoko leaf to avoid the level of Cr to be higher than it is by being mindful of the site for cultivation of the crop.

The growing demand for energy conservation and sustainable technologies has highlighted the need for intelligent systems to optimize energy consumption in various domains, including lighting. This paper presents the design and implementation of a real-time energy-efficient smart lighting system that integrates advanced sensors, wireless communication, and adaptive control strategies. The system employs motion sensors, ambient light sensors, and time-based algorithms to dynamically adjust lighting levels based on environmental conditions, occupancy, and user preferences. The smart lighting system is capable of reducing energy wastage by automatically dimming or turning off lights in unoccupied spaces, while ensuring adequate illumination when needed. Additionally, the system incorporates real-time monitoring and data analytics to track energy consumption patterns, providing actionable insights for further optimization. Experimental results demonstrate the effectiveness of the proposed system in reducing energy consumption, enhancing user comfort, and contributing to sustainable building management practices. The system offers a scalable solution for residential, commercial, and industrial applications, addressing both environmental and economic goals of energy efficiency

Three soil tests were gathered from the Ogheghe people group, Benin City, Edo state. The examples were exposed to a few research facility tests which included; Dampness content assurance, Atterberg limit (fluid breaking point and plastic cutoff), Molecule size examination, Explicit gravity, Compaction test, and California bearing proportion tests. As far as possible pliancy records are 53.87%, 32.59%, 35.13%, 14.81%, and 36.86%, 15.92% separately. The most extreme dry thickness and ideal dampness content qualities are 1.60g/cm3, 1.65g/cm3, 1.74g/cm3 and 14.2%, 11.4%, and 13.4% separately. Results acquired were
contrasted and the writing and the Government Service of Works and Lodging (FMWH) necessities, demonstrating these dirts are really great for filling and subgrade in street development. In any case, I recommend that more examination ought to be directed to decide their appropriateness for use in other design developments.

This paper focuses on the application of forensic science in latent fingerprint detection by the usage of distinct nanomaterials and their benefit with respect to the quality of fingerprint images. Nanotechnology is involved with the study of nanoscale materials, it is having a substantial impact in every industry. It is a widely used technique because of its ability to change and characterize matter down to individual and tiny atoms. Nanoparticles have recently showed considerable potential in the production of nano-fingerprints i.e. the nextgeneration of fingerprint development procedures. Silica nanoparticles were produced to detect invisible finger impressions on non-absorbent surfaces. Silicon usually has been obtained from mineral in rock and sand, however, silicon also can be obtained in organic material such as risk husk, bamboo leaf, wheat husk and another agricultural waste. Bamboo is a source of organic silica (bio-silica). The bamboo absorbs silicic acid from the water and then convert it into SiO2 hydrate in the root branches and leaves